First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform

Journal of Instrumentation First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform To cite this article: B. Abi et al 2020 JINST 15 P12004 View the article online for updates and enhancements. Recent citations Calibration of Calorimetric Measurement in a Liquid Argon Time Projection Chamber Tingjun Yang - This content was downloaded from IP address 150.214.205.97 on 12/01/2021 at 13:44 2020 JINST 15 P12004 Published by IOP Publishing for Sissa Medialab Received:July 30, 2020 Accepted:October 18, 2020 Published:December 3, 2020 First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform The DUNE collaboration E-mail: [email protected],[email protected],[email protected] Abstract: The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber with an active volume of 7.2×6.1×7.0m3. It is installed at the CERN Neutrino Platform in a specially-constructed beam that delivers charged pions, kaons, protons, muons and electrons with momenta in the range 0.3 GeV/𝑐to 7 GeV/𝑐. Beam line instrumentation provides accurate momentum measurements and particle identification. The ProtoDUNE-SP detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment, and it incorporates full-size components as designed for that module. This paper describes the beam line, the time projection chamber, the photon detectors, the cosmic-ray tagger, the signal processing and particle reconstruction. It presents the first results on ProtoDUNE-SP’s performance, including noise and gain measurements, 𝑑𝐸/𝑑𝑥 calibration for muons, protons, pions and electrons, drift electron lifetime measurements, and photon detector noise, signal sensitivity and time resolution measurements. The measured values meet or exceed the specifications for the DUNE far detector, in several cases by large margins. ProtoDUNE-SP’s successful operation starting in 2018 and its production of large samples of high-quality data demonstrate the effectiveness of the single-phase far detector design. Keywords: Large detector systems for particle and astroparticle physics; Noble liquid detectors (scintillation, ionization, double-phase); Time projection Chambers (TPC) ArXiv ePrint:2007.06722 c 2020 CERN. Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. https://doi.org/10.1088/1748-0221/15/12/P12004 2020 JINST 15 P12004 Contents 1 Introduction 1 2 The ProtoDUNE-SP detector 3 2.1 Cryostat 3 2.2 Time projection chamber 4 2.3 Beam plug 8 2.4 Cold electronics 9 2.5 Photon detectors 10 2.6 Cosmic-ray tagger 10 2.7 Data acquisition, timing and trigger system 12 3 CERN beam line instrumentation 14 3.1 Beam line instrumentation components 15 3.2 Beam line simulation and optimization 16 3.3 Beam line event reconstruction and particle identification 16 3.3.1 Momentum spectrometer technique/calculation 16 3.3.2 Particle identification logic 17 4 TPC characterization 18 4.1 TPC data preparation and noise suppression 18 4.1.1 Pedestal evaluation 18 4.1.2 Initial charge waveforms 19 4.1.3 Sticky code identification 20 4.1.4 ADC code mitigation 21 4.1.5 Timing mitigation 22 4.1.6 Tail removal 22 4.1.7 Correlated noise removal 23 4.2 Charge calibration 24 4.3 TPC noise level 25 4.4 Signal processing 28 4.5 Event reconstruction 32 4.5.1 Hit finding 32 4.5.2 Pattern recognition with Pandora 33 4.6 Signal to noise performance 36 5 Photon detector characterization 37 5.1 The photon detector system 37 5.1.1 Light collectors 38 5.1.2 Photosensors 38 5.1.3 Readout DAQ and triggering 40 –i– 2020 JINST 15 P12004 5.1.4 Photon detector calibration and monitoring system 41 5.2 Photosensor performance 42 5.2.1 Single photoelectron sensitivity 42 5.2.2 Signal to noise in photosensors in passive ganging configurations 45 5.2.3 Light calibration 45 5.2.4 Afterpulses and crosstalk 46 5.2.5 Response stability over time 47 5.3 Photon detector performance 47 5.3.1 Efficiency 51 5.3.2 Comparisons of cosmic-ray muons to simulation 55 5.3.3 Time resolution 56 6 TPC response 57 6.1 Space charge effects in ProtoDUNE-SP 58 6.2 Drift electron lifetime 63 6.3 Calibration based on cosmic-ray muons 68 6.3.1 Charge calibration 68 6.3.2 Energy scale calibration 70 6.4 Calorimetric energy reconstruction and particle identification 73 6.4.1 Identificationandcalorimetric energyreconstructionof1GeV/𝑐beam pions and muons 73 6.4.2 Identification and calorimetric energy reconstruction of 1 GeV/𝑐beam protons 77 6.4.3 𝑑𝐸/𝑑𝑥 for 1 GeV/𝑐electrons 78 6.4.4 Particle identification: protons and muons 80 7 Photon detector response 81 7.1 Calorimetric energy reconstruction from scintillation light and energy resolution 81 7.1.1 Beam electrons and EM showers 82 8 Conclusions 86 The DUNE collaboration 92 1 Introduction The Deep Underground Neutrino Experiment (DUNE) [1] is a next-generation, long-baseline neutrino oscillation experiment, with a near detector at Fermilab and a far detector located at the 4850 ft level of Sanford Underground Research Facility (SURF), in Lead, South Dakota, U.S.A., 1285 km from the neutrino production target. The neutrino detectors at the DUNE far site will be housed inside four cryostats, each of which will contain 17.5 kt of liquid argon (LAr). The first detector to be constructed will be a single-phase time projection chamber (TPC), similar to, but a factor of 25 more massive than the pioneering T600 detector built by the ICARUS –1– 2020 JINST 15 P12004 Collaboration [2]. ProtoDUNE-SP, the ProtoDUNE single-phase apparatus (the NP04 experiment at CERN) [3], assembled and tested at the CERN Neutrino Platform [4], is designed as a test bed and full-scale prototype for the elements of the first far detector module of DUNE [5]. NP04 is a CERN-approved experiment to explore large volume LArTPCs and forms an integral part of the DUNE Collaboration. In addition to its role as a demonstration prototype and engineering test bed, the ProtoDUNESP TPC was exposed to a tagged and momentum-analyzed particle beam with momentum settings ranging from 0.3 GeV/𝑐to 7 GeV/𝑐. This beam enabled the acquisition of large samples of data on the behavior of charged pions, kaons, protons, muons and positive electrons (positrons) in LAr. The beam was set to deliver only positively-charged particles for the data samples used in this paper, although future runs will also include negatively-charged particle beams. These data serve as templates for understanding how these particles will appear when produced in neutrino interactions in DUNE, and they will be an important reference in the analysis of interactions in DUNE. These data also provide a real-world test bed for the development of algorithms for pattern recognition, event reconstruction and analysis, and they will be used to measure the cross sections of interactions of charged particles in LAr. The ProtoDUNE-SP apparatus is designed to satisfy the stringent new requirements and achieve the improved levels of performance required by DUNE [6]. The membrane cryostat and its associated cryogenic system are the largest LAr systems ever constructed. The argon purification system is the largest constructed to date. As compared to previous devices, such as ICARUS [2], ArgoNeuT [7], LongBo [8], MicroBooNE [9], and the 35-ton prototype [10] which had shorter maximum drift distances, the 3.6 m drift distance in ProtoDUNE-SP makes higher demands on argon purity. Under the nominal electric field of 500 V/cm, the maximum electron drift time is 2.25 ms. The long drift distance also requires higher voltages in the HV system used to provide the drift field, and the stored energy which may be released in a discharge is also higher than in previous devices. To allow for higher voltages and to reduce the chance of discharges, ProtoDUNESP incorporates specially-chosen materials for the cathode and the field cage structure, and new shapes for the field rings. The sense-wire assemblies, known as Anode Plane Assemblies (APAs), contain three planes of readout wires on both faces and are of a novel design and construction. To improve the signal-to-noise ratio, the sense wire readout amplifiers and analog-to-digital converters (ADCs) are placed inside the LAr close to the wires. Furthermore, the data acquisition system accommodates a higher data rate and larger event sizes than previous LArTPC systems. ProtoDUNE-SP includes a novel photon-detector design which embeds the photon detectors within the APAs in order to collect scintillation light from ionized LAr. Due to the small available area, the photon detectors are required to be highly efficient for detecting single photons. The performance of the photon detectors in ProtoDUNE-SP is a primary topic of this paper. Cosmic-ray interactions with the detector cause a buildup of positive ions that drift very slowly towards the cathode. The accumulated space charge is proportional to the rate of incident cosmic rays and it depends strongly on the drift distance. The space charge alters the electric field in the detector, changing both its strength and its direction, causing distortions in both the measured positions of particles traversing the detector and their apparent ionization densities. However, the effects of space charge buildup are expected to be largely absent in the DUNE Far Detector due to its low cosmic-ray rate, a consequence of its deep underground location. In the analyses presented –2– 2020 JINST 15 P12004 in this paper, corrections for the effects of space charge are applied where appropriate in order for the results of these studies to be generally applicable. The ProtoDUNE-SP Technical Design Report [3] contains a detailed description of the design. A description of the apparatus as built, plus a description of the installation, testing and commissioning is given in [11]. This paper is organized as follows. Section 2describes different components of the ProtoDUNE-SP detector. Section 3describes details of the CERN beam line instrumentation. Sections 4–7summarize results on TPC characterization, photon detector characterization, TPC response, and photon detector response. Section 8concludes the paper. This paper summarizes the initial results from analyzing the ProtoDUNE-SP data. More in-depth studies will be reported in future publications. 2 The ProtoDUNE-SP detector The ProtoDUNE-SP apparatus, shown in figure 2, is described by a right-handed coordinate system in which the 𝑦axis is vertical (positive pointing up) and the 𝑧axis is horizontal and points approximately along the beam direction. The 𝑥axis is also horizontal and points along the nominal electric field direction and is perpendicular to the wire planes.1 2.1 Cryostat The TPC is installed in a membrane cryostat [12] with internal dimensions of 8.5 m in both the 𝑥 and 𝑧directions, and 7.9 m in 𝑦. The cryostat is filled to a height of about 7.3 m and its pressure is maintained to 1050 mbar (absolute). The TPC is suspended by the detector support system, which is a network of steel beams held in place by nine penetrations in the roof of the cryostat. A detailed description of the design, construction, leak-checking, testing and validation of the cryostat is given in [11]. The cryogenics control system is also described there. We give a summary here of the argon purification system that has played a crucial role in the detector performance achieved. The argon received from the supplier has contaminants of water, oxygen and nitrogen at the parts per million level each. Water and oxygen will capture drifting electrons and the concentration of these contaminants needs to be reduced by a factor of at least 104and maintained at this level to allow operation of the TPC. Purification of argon in the liquid phase, as required for the mass of argon involved here, is reported in [13]. The present system builds on purification systems developed for ICARUS and most recently at Fermilab [14] and [9], including the use of the same filter materials. The main features of the system are indicated in figure 1. There are three circulation loops. In one, liquid leaves the cryostat via a penetration in the side. It is pumped as liquid through a set of filters, and it is reintroduced to the cryostat at the bottom. The pump can drive about 7 t/hr giving a volume turnover time of about 4.5 days. In the second loop, argon gas from the purge pipes with which each signal penetration is equipped is purified directly while warm and it is recondensed to join the liquid flow out of the cryostat. In the third loop, the main boil-off from the argon is recondensed directly and it then joins the liquid flow out of the cryostat. When the argon is first 1Throughout this paper, the charge and energy deposited per unit track length are conventionally referred to as 𝑑𝑄/𝑑𝑥 and 𝑑𝐸/𝑑𝑥 respectively. The 𝑑𝑥 in these expressions is not oriented along the detector coordinate 𝑥but rather it is a differential step along the track path. –3– 2020 JINST 15 P12004 circulated, the contamination level falls following a perfect mixing model with a time constant of the turnover time until a steady state is reached in which the rate of contamination from leaks and outgassing from impurities balances the clean-up rate. In the NP04 cryostat, thanks to the rate of recirculation and the avoidance of leaks, this state is equivalent to an oxygen contamination [15] of a few parts per trillion resulting in essentially full-strength signals from the furthest parts of the TPC. Figure 1. A schematic of the argon purification system at NP04. Instrumentation for monitoring the state of the argon is distributed outside the TPC near the innerwallsofthecryostat. Threepuritymonitors, formerly usedwiththeICARUST600detector[2], were refurbished with new gold photocathodes and quartz fibers. They are deployed in ProtoDUNESP, each at a different height. They monitor and give fast feedback on the drift electron lifetime in the liquid argon. Two vertical columns of resistance temperature detectors (RTDs) measure the temperature gradient of the liquid argon. Computational fluid dynamics (CFD) calculations have been performed that predict the temperature distribution and the internal flow pattern of the argon [16]. The temperature is predicted to vary by 15 mK total over the height of the liquid. The RTDs have been cross calibrated in situ to better than 2 mK and their measurements agree with the predictions within ±3.7mK. A set of cameras and LED lights in the liquid and in the ullage provides monitoring of the mechanical state of the apparatus during filling and operation. 2.2 Time projection chamber The time projection chamber is divided into two separate half-volumes with a solid, planar cathode in the center, at 𝑥=0in the 𝑦𝑧 plane, with three APAs forming an anode plane opposite to the –4– 2020 JINST 15 P12004 cathode on either side. The two active regions of the TPC, permeated by an electric field, enclose a volume 6.1 m high along the 𝑦direction, 7.0 m along 𝑧, and 3.6 m in the positive and negative 𝑥direction. The entire active volume, except very thin regions at the boundaries, is instrumented for ionization charge readout at the APA end of the drift. Table 1summarizes the nominal TPC parameters and features. Figure 2shows a view of the TPC with its major components labeled and a photo of one of the two drift volumes. Table 1. Nominal LArTPC parameters and features. Here, X refers to all collection planes, and Z and C refer to the collection planes on the sides of the TPC and cryostat, respectively. TPC configuration Anode-Cathode-Anode (2 active volumes) TPC dimensions (active volumes) 6.086 (h) ×3.597 (w) ×7.045 (l)m3 (instrumented volumes) 5.984 (h) ×3.597 (w) ×6.944 (l)m3 Total active volume (nominal, at room T) 2 ×154 m3 Total instrumented LAr mass (87.65 K) 419 t Number of TPC wire planes 4 (G, U, V, X) Number of wires (total) 15360 (instrumented) G: Grid plane 2 ×2880 (non-instrumented) U: 1st induction plane 2 ×2400 (instrumented, wrapped) V: 2nd induction plane 2 ×2400 (instrumented, wrapped) Z: TPC-side collection plane 2 ×1440 (instrumented) C: Cryostat-side collection plane 2 ×1440 (instrumented) Wire orientation (w.r.t. vertical) G: 0◦, U: +35.7◦, V: −35.7◦, X: 0◦ Wire pitch (normal to wire direction) 4.79 mm (G, X); 4.67 mm (U, V) Wire type Cu-Be Alloy #25, diam. 150 μm Gap width between planes 4.75 mm E-Field (nominal) in drift volume 500 V/cm Cathode plane voltage −180 kV Anode plane bias voltages G: -665 V, U: -370 V, V: 0 V, X: +820 V Ground mesh 0 V Max. drift length 3572 mm (Cathode-to-G-plane distance at 87.65 K) Drift velocity (nominal field, 87.65 K) 1.59 mm/μs Max. drift time (nominal field, 87.65 K) 2.25 ms The cathode plane of the TPC is formed from six cathode plane assemblies (CPAs). Each CPA is 1.15 m wide and 6.1 m high, and consists of three vertically stacked cathode panels. The stored electrical energy in the TPC when fully charged presents a challenge. If the cathode were electrically conducting, an electrical breakdown can discharge it rapidly, endangering the front-end electronics. Instead, the cathode is constructed out of resistive materials which give it a very long discharge time constant, reducing the risk. The CPA panels are constructed from FR4, a fire-retardant fiberglassepoxy composite material. These panels are laminated on both sides with a commercial Kapton –5– 2020 JINST 15 P12004 Figure 2. Top: a view of the TPC with its major components labeled; bottom: a photo of one of the two drift volumes, where three APAs are on the left side and the cathode is on the right side. film with a resistivity of ∼3.5 MΩ/sq. The cathode plane is biased at -180 kV to provide a 500 V/cm drift field. A field cage with 60 voltage steps on each side of the cathode ensures the uniformity of the nominal drift field between the cathode plane and the sense planes. The electric field differs from the nominal prediction due to space-charge effects, which are described in section 6.1. –6– 2020 JINST 15 P12004 The ProtoDUNE-SP timing system provides a 50 MHz clock multiplexed on an 8b10b encoded data stream that is broadcast to all endpoints. The timing system interfaces to the CERN SPS beam presence signals and can be used to switch modes for data taking with and without beam. The timing system data stream also provides the trigger distribution. The timing system is partitionable, a feature that allows parts of the experiment to run independently. A clock synchronized to the Global Positioning System provides 64-bit timestamps that are used to mark the trigger and data times irrespective of file name, run, or trigger record numbers. A hardware triggering system was designed in order to perform event selection in ProtoDUNESP. The core element of this system is the Central Trigger Board (CTB) which is a custom printed circuit board (PCB) in charge of processing the status of the auxiliary detectors to aid in making prompt readout decisions. The readout decisions are ultimately made by the timing system which communicates with the CTB through various commands. The CTB hosts a MicroZed, which is a commercial PCB with an onboard System-On-a-Chip (SoC). The SoC contains both Programmable Logic (PL) and a Processing System (PS) and serves as an interface between the auxiliary detectors (photon detectors, beam instrumentation, and CRT) and the DAQ through the timing system. The CTB has 32 individual CRT pixel inputs (a pixel being a unit of two overlapping panels), 24 optical inputs for the photon detection system, and seven inputs for beam instrumentation signals, all of which are translated into digital pulses and forwarded to the PL for further processing. The CTB triggering firmware operating in the PL is organized into a two-level hierarchy of low-level and high-level triggers (LLTs and HLTs) which are configurable at run-time by the DAQ system. LLTs are defined for inputs from a single subsystem while HLTs can be defined using the various LLTs and can therefore span any or a combination of the subsystems. Several trigger conditions can be set up; each one is uniquely identified by a bitmask and is embedded into a trigger word issued to the DAQ. An overview of the CTB trigger scheme and its interface with the DAQ is depicted in figure 8. Figure 8. CTB trigger hierarchy. – 13 – 2020 JINST 15 P12004 Additionally, multiple trigger conditions can be satisfied during a single triggered detector readout. To distinguish between these, the CTB timestamps all LLTs and HLTs generated with the 50 MHz system clock. This (64-bit) timestamp is also included in the trigger word along with the bitmask. In the HLT, trigger conditions can be configured to require coincidences or anti-coincidences between the various LLTs. Only when all the required conditions for an HLT are satisfied is a trigger command passed to the timing system. The timing system is then responsible for validating or vetoing3the issued trigger. If accepted, the timing system forwards the readout decision to the DAQ software and to the individual readout systems (i.e. TPC, photon detectors, and CRT). However, for accountability,4the CTB sends a data word directly to the DAQ to be stored regardless of whether or not the trigger is validated by the timing system. All HLTs can be classified as beam-on or beam-off triggers. The former relies mostly on the beam instrumentation inputs and requires the conditions to be satisfied during a beam spill while the latter requires that the conditions are satisfied outside the beam spill. The most common examples of beam-on triggers include those aimed at tagging electron, proton and kaon events. By requiring different signal combinations from the beam instrumentation inputs, one can identify specific particles for a relevant energy range, which will be discussed in section 3. The most common examples of beam-off triggers are those arising from cosmic-ray activity. Several of these triggers are in place to select events with specific topologies, requiring CRT pixels from specific regions to register hits in coincidence with pixels from another region. For example, by requiring that at least one upstream CRT pixel is hit in coincidence with a downstream CRT pixel, one can select throughgoing muon candidates. Another trigger is set up for cathode-crossing muon candidates, which is achieved by requiring coincidence hits on CRT pixels on opposing drift volumes and sides of the cryostat. In addition to the logic-specific triggers, an aperiodic random trigger is provided to read out the detector without regard to trigger conditions. For each triggered readout of the detector, the TPC data consists of 6000 consecutive samples of each ADC, which are digitized at a rate of 2 MHz, for a total of 3 ms of time. Each time period of 500 ns between ADC samples is called a “tick.” The data readout starts 250 μs (500 ticks) before the trigger time in order to collect charge deposited by particles that arrive earlier than the trigger but cause charge to arrive at the anodes during time periods that overlap those of triggered events. Corresponding data from the photon detectors and the CRT are saved in the output data stream for analysis. Compressed raw data trigger records have a typical size of 60 MB, and trigger rates of 40 Hz were reliably sustained by the data acquisition system. A typical physics run lasts several hours. 3 CERN beam line instrumentation The ProtoDUNE-SP TPC is located in the CERN North Area in a tertiary extension branch of the H4 beam line. The 400 GeV/𝑐primary proton beam is extracted from the CERN Super Proton Synchrotron (SPS) and is directed towards a beryllium target, producing a mixed hadron beam with a 3In case a trigger has already been issued by the timing system. 4If beam pile-up occurs, the timing system vetoes any additional beam triggers if it has issued one in the last 10 ms. However, the CTB still reports multiple beam triggers in this case. – 14 – 2020 JINST 15 P12004 momentum of 80 GeV/𝑐. This secondary beam is then transported to impinge on a secondary target, producing a tertiary, very low energy (VLE) beam in the 0.3–7 GeV/𝑐momentum range. The H4VLE beam line then accepts, momentum-selects and transports these particles to the ProtoDUNE-SP detector. The secondary target material can be changed between copper and tungsten. The latter is chosen for momenta below 4 GeV/𝑐in order to increase the hadron content of the beam. However, the copper target was unintentionally used for the 2 GeV/𝑐run instead of the tungsten target. 3.1 Beam line instrumentation components The H4-VLE beam line is instrumented with three types of detectors that provide particle identification and a trigger for the TPC. There are eight profile monitors (“XBPF”), three trigger counters (“XBTF”) and two threshold Cherenkov counters (“XCET”). There are also three bending magnets that direct the beam toward the ProtoDUNE-SP detector. The second of these magnets is also used as part of a momentum spectrometer. The relative positions of each of these features can be seen in figure 9. A description of the beam line design has been reported elsewhere [26], while an in-depth discussion of the instrumentation can be found in [27]. ProtoDUNE -SP Time of Flight Momentum Spectrometer Beam line Trigger 28.575 m XBTF XBPF XCET XBPF XBTF XBTF XBPF Figure 9. A schematic diagram showing the relative positions of the trigger counters (XBTFs), bending magnets (triangles), profile montiors (XBPFs) and Cherenkov detectors (XCETs) in the H4-VLE beam line. Combining data from different pieces of instrumentation can be used for triggering, reconstructing momentum and measuring time of flight. The XBPFs, described in detail in [28], are scintillating fiber detectors, each containing 192 square fibers, approximately 1 mm thick. The fibers are arranged in a planar configuration and cover an area of approximately 20 ×20 cm2. Each device contains a single plane of fibers and therefore measures one spatial coordinate. Pairs of these detectors, rotated by 90◦with respect to each other, are placed at several points along the beam line. This arrangement allows the beam position to be tracked on a particle-by-particle basis. The XBPF data is also used in the reconstruction of a particle’s momentum, discussed in section 3.3.1. Hits in the last two sets of XBPF devices are used to measure the trajectories of the beam particles that are then extrapolated to the face of the ProtoDUNE-SP TPC. The XBTFs are designed in a similar way. However, instead of each fiber being read out separately, they are gathered into two bundles and therefore offer no position resolution. Instead, the signals from upstream and downstream planes, which are separated by 28.575 m, are connected to a time-to-digital converter (FMC-TDC [29]). The TDC signals from these two planes provide a particle’s time of flight (TOF). The resolution of this measurement has been measured to be approximately 900 ps [27]. – 15 – 2020 JINST 15 P12004 Coincident signals from the middle and downstream XBTFs act as a “general trigger.” These general triggers are sent to the CTB serving as conditions for HLTs as described in section 2.7. During data taking across the momentum regime of interest, the measured efficiencies of the XBPFs with respect to these triggers are greater than 95% for all chambers [27]. The two Cherenkov counters used in the H4-VLE are of similar design [30,31], although one is able to sustain a higher radiator gas pressure. The internal pressures of the two devices were tuned to tag different particle species at various momenta. A combination of the TOF and the two Cherenkov signals (high and low pressure), offers particle identification for analysis across the whole momentum spectrum of interest. During the beam run, signals from these devices were sent to the CTB to form HLTs tagged as various beam particle species. 3.2 Beam line simulation and optimization The beam transported in the H4-VLE beam line is produced by the collision of the secondary mixed hadron beam of 80 GeV/𝑐with the secondary fixed target. To limit the contributions from the decays of unstable low-energy hadrons such as pions and kaons, a beam line length of less than 50 m is required. Low-energy beam particles need to be sufficiently separated from the highenergy background in this distance, and enough space for the beam line instrumentation is required. Detailed simulation studies were carried out in order to meet these specifications. The performance of the initial layout was calculated with the beam optics code Transport [32] andrefinedbyacomprehensiveMAD-X [33](andMAD-X-PTC [34])simulation[35]. TheMonte Carlo simulations use two frameworks, G4beamline [36] and FLUKA [37,38]. Different target lengths and materials were investigated to satisfy the experimental needs of rate and beam composition. The target choice (either copper or tungsten) and the different field strengths of the beam line’s dipoles and quadrupoles are incorporated into the G4beamline and FLUKA models. Based on these studies, estimates of the beam rates, compositions and background rates at the experiment location are obtained. The background suppression was improved by optimizing the shielding using the FLUKA simulation [27]. 3.3 Beam line event reconstruction and particle identification Information from the three types of beam line instruments (discussed in section 3.1) is combined in order to perform particle identification on an event-by-event basis. A search window in time of 500 ns is defined around each general trigger; timestamps associated with data packets from each device are then matched within this interval. 3.3.1 Momentum spectrometer technique/calculation The three XBPF detectors surrounding the middle bending magnet provide a measurement of each particle’s momentum. This is illustrated in figure 10 [30]. The lateral position of the particle at each XBPF detector (𝜒1,𝜒2,𝜒3) is provided by the index of the activated fibers in the profile monitors. These measurements, along with the known distances between the monitors (𝐿1,𝐿2,𝐿3) and the measured magnetic field are used with equations. (3.1) and (3.2) to determine a particle’s bending – 16 – 2020 JINST 15 P12004 - 18 - Figure 18: Layout of the H2-VLE (and similarly H4-VLE) momentum spectrometer around the last dipole. To validate the performance of the spectrometer, we used the high statistics simulation, which includes all the material in the line, the gas in the Cherenkov detectors at the right pressures per momentum, as well as the expected special resolution of the profile monitors. For each particle, we compute its momentum from the above equation, and therefore the measured Δp/p of the line. Assuming no material in the beam line for a central momentum of 12 GeV/c and position resolutions of 0.2 mm, 0.5 mm and 0.8 mm we obtain a Δp/p of 1.1%, 2.5% and 3.9% accordingly, as shown on Figure 19. When the material along the beam is included, the reconstructed momentum resolution Δp/p deteriorates, because of the multiple scattering, with the effect becoming more significant in lower energies, as shown on Figures 20, 21 and 22. For the 2 GeV beam, the reconstructed momentum resolution with all material included and with Figure 10. A schematic diagram showing the method by which momentum is reconstructed for a given beam particle (red), as discussed in the text. Taken from [30]. The direction of the 𝑥axis is opposite to the convention used in this paper. angle 𝜃and momentum 𝑝. cos 𝜃=𝑀[Δ𝐿tan 𝜃0+Δ𝜒cos 𝜃0] + 𝐿1Δ𝐿 √︃[𝑀2+𝐿2 1][(Δ𝐿tan 𝜃0+Δ𝜒cos 𝜃0)2+Δ𝐿2] (3.1) 𝑝=299.7924 𝜃×∫𝐿mag 0(𝐵𝑑𝑙)(3.2) Here, 𝑀≡𝛼+𝜒1,𝛼=𝜒3𝐿2−𝜒2𝐿3 𝐿3−𝐿2cos 𝜃0,Δ𝐿≡𝐿3−𝐿2, and Δ𝜒≡𝜒2−𝜒3.𝜃0is the nominal bending angle of the beam and is equal to 120.003 mrad [27]. 3.3.2 Particle identification logic The beam line is designed to provide particle identification (PID) for the various particle types (𝑝, 𝜇,𝜋,𝑒,𝐾) comprising the beam. Depending on the beam momentum settings, different conditions are applied to the data from the beam line instrumentation to extract the particle types. These conditions are listed in table 2. This technique is demonstrated for selected runs at various beam momenta in figures 11(a)–11(d). Figure 12 shows the measured momentum and TOF distribution throughout the selected runs. The red curves show expected TOF for several particle types (𝑒,𝜇,𝜋, 𝐾,𝑝and 𝑑) given the particle’s momentum, its mass, and assuming a distance of 28.575 m between the TOF monitors. – 17 – 2020 JINST 15 P12004 Table 2. A summary of beam line instrumentation logic used in the identification of particle types. Each cell reflects how a particular type of instrumentation is used at a given reference momentum. When time of flight is used, the values of the lower and upper cuts are given in nanoseconds. In the case of the high-pressure Cherenkov (XCET-H) and the low-pressure Cherenkov (XCET-L), zero and one represent the absence and presence of a signal respectively. When a given piece of instrumentation is not involved in a logic decision at a given momenta, a dash is used. Momentum (GeV/𝒄) 1 2 3 6–7 𝑒 TOF (ns) 0, 105 0, 105 — — XCET-L 1 1 1 1 XCET-H — — 1 1 μ/𝜋 TOF (ns) 0, 110 0, 103 — — XCET-L 0 0 0 1 XCET-H — — 1 1 𝐾 TOF (ns) — — — — XCET-L — — 0 0 XCET-H — — 0 1 𝑝 TOF (ns) 110, 160 103, 160 — — XCET-L 0 0 0 0 XCET-H — — 0 0 4 TPC characterization The large quantities of high-quality data collected by ProtoDUNE-SP enable many studies of the performance of the TPC. This section describes the offline data preparation and noise suppression, charge calibration, noise measurement, signal processing, event reconstruction, signal-to-noise performance, and a measurement of the electron lifetime. 4.1 TPC data preparation and noise suppression The ProtoDUNE-SP detector is typically triggered at a rate of 1-40 Hz where each trigger record includes synchronized contiguous samples from all TPC channels, typically with a length of 3 ms corresponding to 6000 ticks (ADC samples). Trigger records are processed independently of one another, beginning with data preparation which converts the ADC waveform (ADC count for each tick) for each channel to a charge waveform. The data preparation comprises evaluation of pedestals, charge calibration, mitigation of readout issues, tail removal and noise suppression. These operations are necessary in order to optimize the performance of subsequent stages of event reconstruction. The data preparation steps are described in detail in the following subsections. 4.1.1 Pedestal evaluation Voltage offsets are introduced at the inputs to the amplifier and ADC for each channel to keep the signals in the appropriate range for each of these devices. These offsets and the gains of both devices vary from channel to channel and so there are channel-to-channel variations in the ADC pedestal, i.e. the mean ADC count that would be observed in the absence of signal. In addition, the – 18 – 2020 JINST 15 P12004 Time of Flight [ns] 80 100 120 140 160 Relative Frequency 0.0 0.2 0.4 0.6 0.8 1.0 eπ / µ p DUNE:ProtoDUNE-SP Beam Line Data (1 GeV/c) (a) Nominal beam momentum = 1 GeV/𝑐. Vertical lines represent the time of flight cuts used for electrons (blue), and muons/pions (red). Time of Flight [ns] 85 90 95 100 105 110 115 Relative Frequency 0.0 0.2 0.4 0.6 0.8 1.0 eπ / µ p DUNE:ProtoDUNE-SP Beam Line Data (2 GeV/c) (b) Nominal beam momentum = 2 GeV/𝑐. Vertical lines represent the time of flight cuts used for electrons (blue), and muons/pions (red). Time of Flight [ns] 85 90 95 100 105 110 Relative Frequency 0.0 0.2 0.4 0.6 0.8 1.0 eπ / µ p/K DUNE:ProtoDUNE-SP Beam Line Data (3 GeV/c) (c) Nominal beam momentum = 3 GeV/𝑐. Time of Flight [ns] 85 90 95 100 105 Relative Frequency 0.0 0.2 0.4 0.6 0.8 1.0 π / µe / K p DUNE:ProtoDUNE-SP Beam Line Data (6 GeV/c) (d) Nominal beam momentum = 6 GeV/𝑐. Figure 11. Time of flight distributions for different reference momenta, separated by particle using the PID techniques listed in table 2. The distributions are normalized such that the maximum height is equal to 1. pedestal is observed to have significant variation from one trigger record to another, presumably due to low-frequency (compared to the 3 ms readout window) noise pickup before the amplifier. To cope with this, the pedestal is evaluated independently for each channel and each trigger record. The pedestal is evaluated by histogramming the ADC count for all (typically 6000) ticks and fitting the observed peak with a Gaussian whose mean is used as the pedestal. The RMS of the fit Gaussian provides an initial estimate of the noise in the channel and is typically around four to six ADC counts. Due to the sticky-code issues described in section 4.1.3, these ADC count distributions are sometimes observed to have spikes at the offending sticky codes which can bias the pedestal estimate. To reduce this bias, the peak bin is excluded from the fit if it holds more than 20% of the samples. 4.1.2 Initial charge waveforms Initial charge waveforms are obtained for each channel by subtracting the pedestal from each of the ADC counts and multiplying this difference by the gain assigned to the channel. These gains may be set to 1.0 to obtain a charge waveform in units of ADC counts or they may be taken from a charge calibration. The standard ProtoDUNE-SP reconstruction makes use of the calibration discussed in section 4.2. – 19 – 2020 JINST 15 P12004 10 2 10 3 10 4 10 Momentum [GeV/c] 0 2 4 6 8 Time of Flight [ns] 80 100 120 140 DUNE:ProtoDUNE-SP Beam Line Data (1-7 GeV/c) e µ π Kp d Figure 12. The distribution of particles’ time of flight against reconstructed momentum from several runs at various beam reference momenta. The red curves are predictions for 𝑒,𝜇,𝜋,𝐾,𝑝and deuterons (𝑑) in order of increasing time of flight. Figure 13(a) shows an example event display consisting of waveforms on wires in the collection plane of APA 3 shown side by side in a two-dimension color plot. Pedestal subtraction and charge calibration have been applied. APA 3 instruments the upstream drift volume on the side of the cathode on which the beam enters. 4.1.3 Sticky code identification A few percent of ADC ASIC channels suffer from an issue known as “sticky code,” in which certain ADC values would be preferentially produced by the ADC independent of the input voltage causing the readout channel to appear to “stick” at a particular value. The flaw in this ADC design is a failure of transistor matching at the transition from digitizing the six most significant bits to the six least significant bits. The sticky codes therefore tend to prefer values of zero or 63 plus a multiple of 64, though other sticky codes have been observed in the data as well. Sticky codes were observed in test-bench measurements in advance of installation, where the dynamic range of the ADC was tested with a calibrated source of charge. The pedestal histograms and a few waveforms for all channels were scanned by eye to obtain an initial list of sticky codes and this list is extended when other problematic channels are uncovered. A total of 498 codes in 312 channels (of 15360) have been identified as sticky and are mitigated as described in the following section. Approximately 70 channels are flagged as bad due to very high fractions of sticky codes or population of multiple widely-separated sticky values. Another 35 are flagged as noisy due to serious – 20 – 2020 JINST 15 P12004 (a) After pedestal subtraction and calibration. (b) After ADC sticky code and timing mitigation. (c) After tail removal. (d) After correlated noise removal. Figure 13. Example event displays for a collection plane showing background reduction in successive stages of data processing. The horizontal axis is the tick and vertical axis is the channel number. The color scale represents the charge for each channel averaged over five ticks with the range chosen to make the noise visible. Signals from charged tracks appear mostly in black and are off scale, well above the noise level. Horizontal dashed lines indicate the boundaries between the ten FEMBs used to read out the channels for this plane. The second from the bottom is FEMB 302 referenced in the text. but less-severe sticky-code issues. The solidly unresponsive channels mentioned in section 2.4 are also flagged as bad. A total of 133 channels are flagged as bad or noisy. The data for these channels are prepared like any others, but downstream processing such as deconvolution or track finding may choose to ignore these channels or treat them in a special manner. 4.1.4 ADC code mitigation For channels that have known sticky codes, if the ADC value on a particular sample is at one of the sticky values, it is replaced with a value interpolated from the nearest-neighboring non-sticky codes. If the two neighbors on either side exhibit a significant jump (20 ADC counts), the interpolation uses a quadratic fit. Otherwise, linear interpolation is used. Figure 14 shows an example charge waveform before and after mitigation. This is from FEMB 302 where sticky codes are particularly prevalent. A sticky code has been identified and the waveform is significantly improved after mitigation. Sticky codes very close to the pedestal are not flagged. – 21 – 2020 JINST 15 P12004 0 50 100 150 200 250 300 350 400 450 500 Tick 960 980 1000 1020 1040 ADC count DUNE:ProtoDUNE-SP 0 50 100 150 200 250 300 350 400 450 500 Tick 40− 20− 0 20 40 Signal [ADC count] DUNE:ProtoDUNE-SP Figure 14. Example of a raw ADC waveform with sticky codes (top) and the corresponding waveform after pedestal subtraction and ADC sticky code mitigation (bottom). The dashed lines in the top plot show the six-bit boundaries. 4.1.5 Timing mitigation One of the 120 FEMBs (FEMB 302) does not receive the master timing signal used to clock the ADCs. The ADCs on that FEMB make use of a backup clock that resides on the FEMB. Although the master and FEMB clocks both nominally run at 2 MHz, reconstructed signals show that the FEMB clock runs 0.07% slower than the master clock. The charge waveforms for FEMB 302 are corrected to match the sampling rate and offset for the other channels. The charge for each sample is replaced with a linear interpolation of the original charges of two samples nearest in time. Figure 13(b) shows an event display made from mitigated waveforms for the same data with the same scale and binning as figure 13(a). Both sticky-code and timing mitigations are added. The shift in the FEMB 302 timing and reduction in noise are discernible. Channels flagged as bad or noisy are zeroed and appear white in the display. 4.1.6 Tail removal In each TPC channel, the amplifier and ADC are AC-coupled using a high-pass RC filter with a time constant of approximately 𝜏RC =1.1 ms (2200 ticks) for collection-plane channels and 3.3 ms (6600 ticks) for induction-plane channels. The typical signal from a charged track will be much faster, 10-20 μs (20-40 ticks) and this AC coupling implies the observed signal will be followed by a long tail of opposite sign whose area cancels that of the initial signal. The tails are much smaller and are neglected in induction-plane channels, where the signals are bipolar and thus integrate to zero. The decay time is comparable to the mean time between cosmic-ray signals, about 1500 ticks, and it is a significant fraction of the data readout time used to evaluate the pedestal, typically 6000 ticks. Variations in cosmic arrival time and charge deposit per channel (in particular due to varying angle of incidence) imply that all signals are superimposed on a fluctuating background of accumulated tails from preceding signals, many of which arrive before the readout window starts. Tails from these fluctuations are clearly visible in collection-plane waveforms and event displays. – 22 – 2020 JINST 15 P12004 0 200 400 600 800 1000 Frequency [kHz] 0 500 1000 1500 2000 /(20 kHz)] 2 Power/tick/channel [e = 93 eΣW/o CNR: = 82 eΣWith CNR: DUNE:ProtoDUNE-SP C wires Run 5240 0 200 400 600 800 1000 Frequency [kHz] 0 500 1000 1500 2000 /(20 kHz)] 2 Power/tick/channel [e = 111 eΣW/o CNR: = 92 eΣWith CNR: DUNE:ProtoDUNE-SP U wires Run 5240 0 200 400 600 800 1000 Frequency [kHz] 0 500 1000 1500 2000 /(20 kHz)] 2 Power/tick/channel [e = 97 eΣW/o CNR: = 83 eΣWith CNR: DUNE:ProtoDUNE-SP Z wires Run 5240 0 200 400 600 800 1000 Frequency [kHz] 0 500 1000 1500 2000 /(20 kHz)] 2 Power/tick/channel [e = 113 eΣW/o CNR: = 94 eΣWith CNR: DUNE:ProtoDUNE-SP V wires Run 5240 Figure 20. Noise frequency power spectra before and after CNR. Each plot is evaluated from 1000-sample blocks in non-signal regions. Upper and lower left respectively show the cryostatand TPC-side collection planes. The induction planes are on the right: top is U and bottom is V. The first bin in each plot includes only the zero frequency component. The others are sums over 20 kHz bins. is held at constant voltage, is given by 𝑖=𝑒∇𝜙· ®𝑣𝑒,(4.3) where 𝑒is the charge in motion, and ®𝑣𝑒is the charge velocity at a given location. The so-called weighting potential 𝜙of a selected electrode at a given location is determined by virtually removing the charge and setting the potential of the selected electrode to unity while grounding all other conductors. The field response is defined to be the induced current on different wires due to a moving point charge. The field response is an essential input to the signal processing procedure as will be discussed below. For ProtoDUNE-SP, the field response is calculated with Garfield [41], a TPC drift simulation code, in a 2D scheme as illustrated in figure 21(a). During the field response simulation, a point charge is positioned at different positions in a horizontal plane 10 cm away from the grid plane and the drift path is recorded from the simulation as shown in figure 21(b). The electron drift velocity can be determined from the electric field [42,43], while the precomputed weighting potential for a U-plane wire is also shown in figure 21(b). With the drift path and the weighting potential, the field response of the point charge on the sense wire can be calculated according to eq. (4.3). This procedure is repeated for a series of point charges that spans a region of 21 wires with the wire of interest at the center. In order to sample the rapidly-changing field response functions – 29 – 2020 JINST 15 P12004 adequately, point charges are simulated drifting in from positions on a grid with a spacing of one tenth of the wire pitch. After convolving the electronics response, the total response as a function of time and wire pitch is presented in figure 22, where a “Log10” color scale is defined for the sake of visibility: 𝑖in “Log10” =          log10(𝑖·105),if 𝑖 > 1×10−5, 0,if −1×10−5≤𝑖≤1×10−5, −log10(−1·𝑖·105),if 𝑖 < −1×10−5. (4.4) As shown in figure 21(b), the weighting potential of the first induction (U) plane is significantly different from zero over a region of a few wires even with the presence of the grid plane. As a result, the current induced on a sense wire contains contributions not only from charges passing between the wire and its immediate neighbors, but also from moving charges that are farther away. A region that is 10 cm in front of the grid plane and ±10 wires around the wire of interest in the simulation is sufficient to envelope the field response. M0 V (a) x [cm] z [cm] (b) Figure 21. (a) Illustration of the 2D ProtoDUNE-SP TPC scheme for the Garfield simulation, where ±10 wires (large black dots) are considered for each wire plane and the electrons drift simulation starts 10 cm away from the grid plane. The inset shows the spacing of the starting points of the simulated electrons; (b) Garfield simulation of electron drift paths (yellow lines) in a 2D ProtoDUNE-SP TPC scheme and the equal weighting potential lines (green) for a given wire in the first induction plane, where the latter is shown in percentage from 1% to 45%. A long-range induction effect is noticed as the weighting potential has significant strength several wire spacings away from any particular wire. In order to deconvolve the ionization electron distribution from the measured signal, it is natural to mathematically describe this long-range effect as follows: 𝑀(𝑤𝑖1, 𝑡 𝑗1)=∫𝑤𝑖2∫𝑡𝑗2∫𝑡𝑗3 𝑄(𝑤𝑖2, 𝑡 𝑗2) · 𝐹(𝑤𝑖2−𝑤𝑖1, 𝑡 𝑗3−𝑡𝑗2) · 𝐸(𝑡𝑗1−𝑡𝑗3) · 𝑑𝑡 𝑗3·𝑑𝑡 𝑗2·𝑑𝑡𝑤𝑖2 =∫𝑤𝑖2∫𝑡𝑗2 𝑄(𝑤𝑖2, 𝑡 𝑗2) · 𝑅(𝑤𝑖2−𝑤𝑖1, 𝑡 𝑗1−𝑡𝑗2) · 𝑑𝑡 𝑗2·𝑑𝑡𝑤𝑖2(4.5) – 30 – 2020 JINST 15 P12004 X Figure 22. The overall response function convolved with electronics response. The color density is shown in a modified, sign-dependent base 10 log scale, as described in the text. where measured signal 𝑀(𝑤𝑖1, 𝑡 𝑗1)on the 𝑤𝑖1th wire and time 𝑡𝑗1is a convolution of i) the ionization charge distribution as a function of the position in wire number and the drift time: 𝑄(𝑤𝑖2, 𝑡 𝑗2), ii) the field response that describes the induced current on the wires when the ionization charge moves: 𝐹(𝑤𝑖2−𝑤𝑖1, 𝑡 𝑗3−𝑡𝑗2), and iii) the electronics response that amplifies and shapes the induced current on the wire: 𝐸(𝑡𝑗1−𝑡𝑗3). For simplicity, one can firstly convolve the field response and the electronics response into the overall response function: 𝑅(𝑤𝑖2−𝑤𝑖1, 𝑡 𝑗1−𝑡𝑗2), in which the fine-grained position-dependent field response function is averaged within one wire pitch. – 31 – 2020 JINST 15 P12004 Because of the long-range induction effect, instead of a 1D deconvolution involving only the time dimension, a two-dimensional (2D) deconvolution involving both the time and wire dimensions is performed to extract the ionization electron distribution. In practice, the FFT algorithm is used to convert the data from the discrete 2D time and wire domain to a discrete 2D frequency domain [44,45]. To avoid amplifying high-frequency noise in the deconvolution, two Wienerinspired filters are applied separately in both dimensions. In addition, to further reduce the noise contamination and improve the charge resolution, a technique for identifying the signal regions of interest (SROI) is adopted and adjusted accordingly. The application of SROIs are particularly important to process the induction plane signals. As an example, a raw waveform that has been de-noised as described in section 4.1 and the corresponding extracted charge distribution after the deconvolution are shown in figure 23(a) and figure 23(b), respectively. 0 25 50 75 100 125 150 175 200 U Wire No. 0 100 200 300 400 500 600 Time [ s] 15 10 5 0 5 10 15 DUNE:ProtoDUNE-SP (a) After Noise Filtering 0 25 50 75 100 125 150 175 200 U Wire No. 0 100 200 300 400 500 600 Time [ s] 300 200 100 0 100 200 300 DUNE:ProtoDUNE-SP (b) After Deconvolution Figure 23. An interaction vertex from the 7-GeV charged particle beam data (run 5152, event 89) measured on the induction U plane: (a) Raw waveform in ADC counts after noise filtering; (b) Ionization charge in number of electrons, scaled by 200, extracted with the 2D deconvolution technique. 4.5 Event reconstruction There are two distinct steps in the ProtoDUNE-SP event reconstruction chain to go from the deconvolved waveforms to fully reconstructed interactions: hit finding and pattern recognition. These steps are described in sections 4.5.1 and 4.5.2, respectively. 4.5.1 Hit finding The hit finding algorithm fits peaks in the wire waveforms, where a hit represents a charge deposition on a single wire at a given time. Each hit corresponds to a fitted peak. Ideally, after the deconvolution process described in section 4.4, the signals on all wires, regardless of whether they are inductionplane wires or collection-plane wires, will be waveforms containing possibly overlapping Gaussianshaped peaks. The algorithm searches for candidate hits in the waveform and fits them to a Gaussian shape to produce the hits. Situations can occur in which charge deposits do not form a simple Gaussian shape, for example when a particle trajectory is close to being in the plane containing the wire under study and the electric field. If, after the candidate peak-finding, a very large number of candidate peaks are found in a given SROI then the algorithm bypasses the hitfitting step and the pulse is instead divided into a number of evenly-spaced hits. An example of a fitted waveform is shown in figure 24, where three hits have been reconstructed. – 32 – 2020 JINST 15 P12004 4600 4650 4700 s]µTime Tick [0.5 0 10 20 30 40 50 Charge/tick [200 e] Deconvolved waveform Fitted Gaussian hits DUNE:ProtoDUNE-SP Figure 24. An example of a reconstructed waveform on a single wire from ProtoDUNE-SP data. The two induction planes consist of wires that are wrapped around the APA. As a result, it must be determined on which segment of the wrapped wire that a given energy deposit was actually measured. Firstly, triplets of wires (one on each plane) are formed using signals within a narrow time window. Often, for a given collection wire, only a single pair of induction wires are matched, thus the hits are disambiguated at this stage. Otherwise, there can be multiple induction wires consistent in time with the collection wire. In this case, the algorithm aims to minimize the difference in charge between the collection wire and the candidate induction wires in a deterministic manner. A full description of the method is given in ref. [6]. Simulation studies show that this technique assigns more than 99% of hits to their correct wire segments. 4.5.2 Pattern recognition with Pandora Pattern recognition in ProtoDUNE-SP is performed using the Pandora software package [46], which executes multiple algorithms to build up the overall picture of interactions in the detector. Pandora has been used successfully in other liquid argon time projection chambers (LArTPCs) such as MicroBooNE [47]. New features have been developed for ProtoDUNE-SP since it differs from MicroBooNE with its multiple TPCs and drift volumes in addition to the need for a testbeam particle specific reconstruction chain. Pandora contains chains of reconstruction algorithms that focus on specific topologies, but they all follow a common pattern. The first step involves two-dimensional clustering of the reconstructed hits in each of the three detector readout planes separately. Dedicated algorithms then match sets of 2D clusters between the three views. If matching ambiguities are discovered, information from all three views is used in order to motivate changes to the original 2D clustering. Once consistent matches between 2D clusters have been made, three-dimensional hits are constructed and particle interaction hierarchies are created. In the Pandora ProtoDUNE-SP reconstruction, all of the clusters are reconstructed first under the cosmic-ray hypothesis using a set of algorithms designed to reconstruct track-like particles. Cosmic-ray candidates are subsequently identified and removed so that beam-particle analysis can proceed. One important feature of the cosmic-ray reconstruction step is the “stitching” of tracks across the boundaries between neighboring drift volumes bounded by a CPA or an APA. The – 33 – 2020 JINST 15 P12004 stitching procedure is applied when two 3D clusters have been reconstructed in neighboring drift volumes that have consistent direction vectors and an equal but opposite shift in the drift direction from the CPA or APA. When the clusters are shifted by this amount, a single collinear cluster with a known absolute position along the drift direction and time 𝑡0relative to the trigger time is produced. Figure 25 shows the reconstructed 𝑡0distribution for data and simulation for those cosmic-ray muon tracks that have been stitched at the cathode or anode. Cosmic-ray muons that cross the cathode have 𝑡0values between −2500 μs and 500 μs, and those that cross the anode have −250 μs< 𝑡0<2750 μs. Tracks satisfying one or more of the following criteria are identified as “clear” cosmic-ray candidates: •The particle enters through the top of the detector and exits through the bottom. •The measured 𝑡0for stitched tracks is inconsistent with a particle coming from the beam. •Any of the reconstructed hits appear to be located outside of the detector when assuming 𝑡0=0, which indicates that the object is inconsistent with the timing of the beam. The hits from these clear cosmic-ray candidates are removed from the trigger record before the Pandora reconstruction chain continues to further process the data. These tracks, and in particular those with a measured 𝑡0, form a critical component of the various detector calibrations detailed in section 6. 3000−2000−1000−0 1000 2000 3000 s]µ [ 0 Reconstructed t 0.00 0.01 0.02 0.03 0.04 0.05 Fraction of events Simulation Data DUNE:ProtoDUNE-SP Figure 25. The Pandora reconstructed 𝑡0distribution for cosmic-ray muons that cross either the cathode or anode in data (black points) and simulation (red). Once the energy deposits from the clear cosmic rays have been removed, the reconstruction continues with a 3D slicing algorithm that divides the detector into spatial regions containing all of the hits from a single parent particle interaction. These 3D slices could contain beam particles or cosmic rays that were not clear enough to be removed in the first-pass cosmic-ray removal process. Two parallel reconstruction chains are applied to these slices — one is the aforementioned cosmic-ray reconstruction, and the other is a test-beam specific reconstruction. – 34 – 2020 JINST 15 P12004 Beam ⇡+ <latexit sha1_base64="lrM6zOIadMZqc/YrGNuhJjWYmMo=">AAAB/HicbVDLSsNAFJ3UV62vaJduBosgCCWpgi5L3bisYB/QxDKZTtqhkwczN2II7a+4caGIWz/EnX/jtM1CWw8MHM65h3vneLHgCizr2yisrW9sbhW3Szu7e/sH5uFRW0WJpKxFIxHJrkcUEzxkLeAgWDeWjASeYB1vfDPzO49MKh6F95DGzA3IMOQ+pwS01DfLDrAnkEHW0KHJ1In5w3nfrFhVaw68SuycVFCOZt/8cgYRTQIWAhVEqZ5txeBmRAKngk1KTqJYTOiYDFlP05AETLnZ/PgJPtXKAPuR1C8EPFd/JzISKJUGnp4MCIzUsjcT//N6CfjXbsbDOAEW0sUiPxEYIjxrAg+4ZBREqgmhkutbMR0RSSjovkq6BHv5y6ukXavaF9Xa3WWl3sjrKKJjdILOkI2uUB3doiZqIYpS9Ixe0ZsxNV6Md+NjMVow8kwZ/YHx+QMnmpUW</latexit> Figure 26. A reconstructed, simulated test-beam interaction showing the incoming beam 𝜋+track in magenta and a number of secondary particles created at the interaction vertex. The different colors represent different reconstructed particles. The test-beam specific reconstruction consists of a more complex chain of algorithms capable of reconstructing the intricate hierarchies of particles seen in hadronic interactions that can produce numerous track-like and shower-like topologies. Included in this reconstruction chain is a dedicated search for the primary interaction vertex of the test-beam particle. As well as being used to inform the clustering, the vertex is essential for constructing the correct particle hierarchy. Once the slices have been reconstructed under both hypotheses, cosmic-ray and test-beam, a boosted decision tree (BDT) algorithm is used to determine which, if any, of the slices are consistent with being of test-beam origin. The input variables to the BDT primarily use topological information to measure the consistency of the interaction with the test-beam particle hypothesis. The output from the reconstruction is in the form of a particle hierarchy, where links are made between parent and child particles to give the flow of an interaction from the initial beam particle. Figure 26 shows an example of a fully reconstructed particle hierarchy for a simulated beam interaction, where the incoming beam 𝜋+is shown as the magenta track. A suite of tools has been produced for ProtoDUNE-SP analysers to easily access this hierarchical information in order to perform the analyses. The charge deposition per unit length, 𝑑𝑄/𝑑𝑥 is reconstructed for track-like objects such as muons, charged pions, kaons, protons and the beginnings of electromagnetic showers. The charge 𝑄is taken as the area of the Gaussian fit to the individual hit. The segment length 𝑑𝑥 is calculated as the wire spacing divided by the cosine of the angle between the track direction and the direction normal to the wire direction in the wire plane. The raw 𝑑𝑄/𝑑𝑥 is further calibrated to remove nonuniform detector effects and converted to energy loss 𝑑𝐸/𝑑𝑥 for energy measurement and particle identification. This procedure is described in section 6.3. – 35 – 2020 JINST 15 P12004 4.6 Signal to noise performance The measurement of the signal-to-noise ratio (S/N) for the ProtoDUNE-SP detector is carried out using a selected cosmic-ray muon sample in Run 5432 taken on Oct. 20, 2018. Muon tracks crossing the LArTPC at shallow angles with respect to the anode plane and large angles with respect to the direction of the wires in the planes are considered for the S/N characterization. To ensure good track quality, track length is required to be at least 1 m. The electron drift lifetime of the sample was approximately 24 ms as independently measured by the purity monitor. The signal on each wire in a plane is defined to be the maximum pulse height of the raw waveform after subtracting the pedestal. The noise value is defined to be the standard deviation of a Gaussian function fit to the distribution of ADC values in signal-free regions of a channel’s waveform. The signal size depends on the angle of the track with respect to the wire and also with respect to the electric field. We standardize the signal on a wire to be that from tracks that are perpendicular to the wire and also perpendicular to the electric field. We define two angles 𝜃𝑥𝑧 (the angle made by the projection of a track on the 𝑥𝑧 plane with the 𝑧direction) and 𝜃𝑦𝑧 (the angle made by the projection of a track on the 𝑦𝑧 plane with the 𝑧direction). The two angles are illustrated in figure 27. To minimize the influence of angular dependence of S/N, we select muon tracks that have minimum component in both drift and wire direction. Angle cuts of 𝜃𝑥𝑧 and 𝜃𝑦𝑧 within 20◦are applied. A correction is applied to the raw waveform signal to standardize the strength, adjusting for angular effects. y z x θxz θyz Track Direction Figure 27. The definitions of the track direction angles 𝜃𝑥𝑧 and 𝜃𝑦𝑧. The angle-corrected S/N distributions are shown in figure 28. No electron drift lifetime corrections are applied to the angle-corrected S/N calculations. The most probable values (MPVs) of the S/N distributions after the noise mitigation are 40.3, 15.1, and 18.6 for the collection plane, the U plane, and the V plane, respectively. The actual performance of the S/N for the three planes is much better than the expectation in the ProtoDUNE-SP technical design report [3] — 9.0 for the three planes. The angle-corrected S/N results with and without the noise filters, together with the estimation using the averaged values of the S/N distributions, are summarized in table 3. – 36 – 2020 JINST 15 P12004 The differences in the average S/N values for the three planes is explained using the ShockleyRamo theorem, discussed in section 4.4. The three planes have similar weighting fields but different local drift velocities. Among the three planes, the collection plane has the largest local drift velocity and hence the best S/N performance. The S/N performance is slightly better for the V plane with respect to the U plane. This is because the local drift velocity at the V plane is higher than that of the U plane due to larger bias voltage, while the weighting fields are the same for both. 0 50 100 150 Angle-Corrected Signal-to-Noise Ratio 0.0 0.2 0.4 0.6 0.8 1.0 Relative Frequency U Plane, Raw V Plane, Raw Collection Plane, Raw U Plane, Noise-filtered V Plane, Noise-filtered Collection Plane, Noise-filtered DUNE:ProtoDUNE-SP Cosmics Data Figure 28. Angle-corrected S/N distributions before and after the noise filtering of the three planes using the cosmic-ray muons for the characterization. The histograms are normalized such that the maximum frequency is one. Table 3. Summary table of the angle-corrected S/N performance before and after the noise mitigation for the ProtoDUNE-SP detector. No electron drift lifetime corrections are applied. The most-probable value (MPV) and the average value for each plane are listed. Plane Peak signal-to-noise ratio Raw Data After noise filtering MPV Average MPV Average Collection 30.9 38.3 40.3 48.7 U 12.1 15.6 15.1 18.2 V 14.9 18.7 18.6 21.2 5 Photon detector characterization 5.1 The photon detector system The ProtoDUNE-SP photon detector system (PDS) comprises 60 optical modules embedded within the six APA frames of the TPC. These modules view the LAr volume from each anode side opposite – 37 – 2020 JINST 15 P12004 the central cathode. Three different photon collection technologies proposed for DUNE’s far detector modules [3] are implemented in ProtoDUNE-SP’s PD system. In each technology, incident LAr scintillation photons, which have wavelengths around 128 nm, are converted into longerwavelength photons using photofluorescent compounds as wavelength shifters (WLS). Visible light is trapped within the modules, a portion of which is eventually incident on an array of silicon photomultiplier photosensors (SiPMs) [19]. 5.1.1 Light collectors Each APA contains ten support structures located behind the wire planes for the PDS modules. Each module is a long, thin bar oriented along the 𝑧axis. The spacing between modules in 𝑦 is approximately 60 cm, as illustrated in figure 29. Of the 60 modules, two are based on the ARAPUCA photon detector technology [48], 29 are dip-coated light guides [49,50], and 29 are double-shift light guides [51]. All light-guide modules have the same dimensions. The optical area of a module is 207.4×8.2cm2in size, and the light is read out on one end of the bar. The ARAPUCA modules are segmented longitudinally (along the 𝑧direction) into 12 cells, each with its own readout. The first eight cells each have an optical area of 9.8×7.9cm2and the remaining four cells are double-area cells, each with an optical area of 19.6×7.9cm2. One ARAPUCA module is located in the top half of the upstream APA in the beam-side drift volume. A second is located in the middle of the APA in the center of the opposite drift volume. The two light-guide designs fill the remaining modules in alternating positions in the APAs. One example of each module type is highlighted in figure 29, together with the photo-sensor arrays equipping the module. The installation of the modules within an APA behind the wire planes and a grounding mesh is also visible in figure 29. The two light-guide designs convert incident VUV photons into the visible range using tetraphenyl butadiene (TPB) (emission peak ∼430 nm), while the ARAPUCA design uses p-terphenyl (PTP) (emission peak ∼340 nm). In the dip-coated acrylic light guide, wavelength-shifted photons are confined inside by total internal reflection and they are guided toward the end of the bar that is in optical contact with a photosensor array. The double-shift light guide contains an internal light guide doped with the second WLS (490 nm emission) to facilitate trapping of double-converted photons within the module and guide them toward the photosensors at the end of the bar. The ARAPUCA uses a dichroic filter window (400 nm cutoff) to reflect photons from a second WLS (TPB) inside the cell underneath the window and prevent them from exiting before they are absorbed or detected by the photosensors distributed inside the cell. 5.1.2 Photosensors Three silicon photosensor models, each with an active area of 6×6mm2, are employed throughout the photon detection system. They were selected among those that were available commercially or that were newly developed during the PDS material procurement phase: the SensL SiPM MicroFC60035-SMT (35 μm pixel size) and two types of Hamamatsu MPPC S13360-6050 (50 μm pixel size) — the CQ-type (Quartz windowed for Cryogenic application) and the VE-type (VErtical through-silicon via). Arrays of photosensors of the same model are passively ganged together in parallel forming large-area single channels for voltage supply and signal readout. The arrays formed by three SensL SiPMs are indicated in the following as 3-S-SiPM, those formed by 3 Hamamatsu – 38 – 2020 JINST 15 P12004 5.2.2 Signal to noise in photosensors in passive ganging configurations The signal-to-noise ratio (SNR) is a good performance metric for the characterization of the photosensor component of the PDS during normal operating conditions. The SNR of the individual channel (three or twelve photosensors in parallel) at the reference bias voltage setting is here defined as: SNR =𝜇1 𝜎0(5.1) where, referring for example to figure 33 (left panels), the signal 𝜇1is the mean value from the Gaussian fit of the one-PE peak (the minimal detectable signal), and the noise is evaluated from the Gaussian spread, 𝜎0of the zero-PE peak. The SNR for all channels of the three types are shown in figure 35. For the 12-H-MPPC channels of the ARAPUCA modules, the SNR values are around 6, while for the 3-S-SiPM channels of the double-shift and dip-coated bar modules the SNR is in the range 10 to 12. The signal-to-noise ratio, as defined in equation (5.1), is directly proportional to the gain and the higher SNR shown by the 3-S-SiPM channels is primarily due to their higher 𝑉oV setting adopted for operation. 0 50 100 150 Channel 0 5 10 15 20 SNR DUNE:ProtoDUNE-SP 3-S-SiPM 0 10 20 30 40 50 Channel 0 5 10 15 20 SNR DUNE:ProtoDUNE-SP 3-H-MPPC 0 5 10 15 20 25 Channel 0 5 10 15 20 SNR DUNE:ProtoDUNE-SP 12-H-MPPC Figure 35. Signal-to-Noise Ratio (SNR) for the 3-S-SiPM channels (left), for the 3-H-MPPC channels (center), and for the 12-H-MPPC channels (right). 5.2.3 Light calibration Calibration of the light response is necessary to convert the charge signal from the photosensors into the corresponding number of photons detected. The detector calibration LED pulser is flashed synchronously with the data acquisition. Calibration runs at varying intensities of the flasher are needed in order to produce a suitable illumination in each PDS element. For each channel, the digitized waveform recorded in coincidence with the short LED pulse is baseline subtracted and the photosensor charge output is measured by waveform integration over a predefined time window (see section 5.1). Typical charge distributions with multi-peak structure from a LED calibration run are shown in figure 33 (left panels, top for a 12-H-MPPC channel and bottom for a 3-S-SiPM channel). Among the possible different calibration methods, the one adopted here relies on the statistical features of photon counting measurements under stable pulsed, low illumination conditions. The number of detected photons (𝑛) per light flash follows the Poisson distribution with 𝜆, the expected mean number of photons detected per flash, whose value is directly related to the probability of detecting 0-photons in that flash: 𝑃(𝑛)=𝜆𝑛𝑒−𝜆 𝑛!with 𝑃(0)=𝑒−𝜆→𝜆=−ln 𝑃(0)(5.2) – 45 – 2020 JINST 15 P12004 The probability 𝑃(0)can be estimated by the relative frequency of detecting zero photoelectrons in many LED trials, and from this the mean number of photons detected per flash is inferred: 𝜆=−ln 𝑁0 𝑁Tot (5.3) where 𝑁0is the observed number of counts under the zero-PE peak (1st peak in the charge distribution of figure 33 — left panels) and 𝑁Tot is the number of LED flashes in the calibration run. The mean number of photons per flash, 𝜆, depends on the illumination level (LED flash amplitude). The illumination is maintained constant during the run and low enough to have sufficient probability of 0-photon detected. In addition to this, the measured rate is corrected for the accidental background rate of environmental photons, which are not correlated to the LED flash, that may be detected in the trigger window. The background rate is measured in the portion of the recorded waveforms before the LED trigger. 0 1 2 3 4 5 6 [V] oV V 0 1 2 3 4 5 ] 6 10× Charge [e / ndf 2 χ 0.9038 / 5 Prob 0.9699 p0 0.05813±0.01003 − p1 0.01547± 0.5308 / ndf 2 χ 0.9038 / 5 Prob 0.9699 p0 0.05813±0.01003 − p1 0.01547± 0.5308 / ndf 2 χ 0.2771 / 4 Prob 0.9912 p0 0.03156± 0.01073 p1 0.5093± 0.8627 p2 0.07006± 0.5335 / ndf 2 χ 0.2771 / 4 Prob 0.9912 p0 0.03156± 0.01073 p1 0.5093± 0.8627 p2 0.07006± 0.5335 Gain Calibration factor 12-H-MPPCDUNE:ProtoDUNE-SP 0 2 4 6 [V] oV V 0 2 4 6 8 ] 6 10× Charge [e / ndf 2 χ 0.01447 / 5 Prob 1 p0 0.1859±0.00537 − p1 0.0388± 0.8227 / ndf 2 χ 0.01447 / 5 Prob 1 p0 0.1859±0.00537 − p1 0.0388± 0.8227 / ndf 2 χ 1.465 / 4 Prob 0.8328 p0 0.2529± 0.07685 p1 0.3996± 0.5232 p2 0.2684± 0.778 / ndf 2 χ 1.465 / 4 Prob 0.8328 p0 0.2529± 0.07685 p1 0.3996± 0.5232 p2 0.2684± 0.778 Gain Calibration factor 3-S-SiPMDUNE:ProtoDUNE-SP Figure 36. Charge signal per detected photon (blue points) and charge signal per avalanche (black points) as a function of applied over-voltage 𝑉oV for a typical 12-H-MPPC channel (left), and 3-S-SiPM channel (right). The difference is due to the correlated noise contribution, mainly from afterpulse and crosstalk in neighboring microcells of the photosensor. The vertical dotted line at the operation over-voltage set point indicates the gain 𝑔𝑖and the calibration factor 𝑐𝑖used in data analysis for the 𝑖-th channel shown in the figure. The photosensor response to 𝜆detected photons, estimated by equation (5.3), is the mean charge output per flash h𝑄iin the calibration run (average of the distribution shown in figure 33 — left panels). The calibration factor for the i-𝑡ℎ PDS channel is thus determined by the ratio 𝑐𝑖=h𝑄i𝑖/𝜆𝑖 and represents the output charge per photon detected by the individual photosensor channel. The charge issued when an incident photon is detected is expected to be, on average, larger than the single-avalanche induced charge. The comparison of the charge per photon detected (calibration factor 𝑐𝑖— blue line) and charge per avalanche (gain 𝑔𝑖— red line) as a function of the applied over-voltage 𝑉oV is shown in figure 36 for a typical 12-H-MPPC channel (left), and 3-S-SiPM channel (right). The difference is due to the correlated noise contribution to the signal formation in the photosensor. This is found to grow exponentially with increasing voltage. 5.2.4 Afterpulses and crosstalk A common feature of Si-photosensors is the generation of avalanche pulses subsequent to a primary event. The avalanche of a single microcell in a device has a finite probability of inducing an – 46 – 2020 JINST 15 P12004 0 50 100 150 Channel 1.0 1.2 1.4 1.6 1.8 2.0 <Avalanche>/Ph DUNE:ProtoDUNE-SP 3-S-SiPM 0 10 20 30 40 50 Channel 1.0 1.2 1.4 1.6 1.8 2.0 / Ph〉 Avalanche 〈 3-H-MPPCDUNE:ProtoDUNE-SP 0 5 10 15 20 25 Channel 1.0 1.2 1.4 1.6 1.8 2.0 / Ph〉 Avalanche 〈 DUNE:ProtoDUNE-SP 12-H-MPPC Figure 37. Afterpulse and crosstalk contribution to the photosensor signal expressed by the average number of avalanches generated per detected photon for the 3-S-SiPM channels (left), for the 3-H-MPPC channels (center), and for the 12-H-MPPC channels (right). avalanche in neighboring microcells (optical crosstalk), or/and of re-triggering itself before the microcell is fully recovered (afterpulse). The rate of these secondary pulses increases at higher gain settings. The correlated noise due to these effects is a well-known limiting factor for a precise photon counting with silicon photosensors. The measurement of the charge per photon detected (the calibration factor 𝑐𝑖, defined in section 5.2.3) and the charge per avalanche (the gain 𝑔𝑖, defined in section 5.2.1) allows the calculation of the crosstalk and afterpulse probability for each photosensor by the ratio 𝑐𝑖/𝑔𝑖 in units of [Ava/Ph], average number of avalanches per photon detected. This ratio is sensitive to the over-voltage on the photon detector and can be used to monitor for changes in the operating characteristics of the photosensor as a measure of the stability of the PD system. Figure 37 shows the measured avalanche/photon value for each channel in the PDS. An average of ∼1.3 Ava/Ph is found for the 3-S-SiPM channels and the 12-H-MPPC, while a larger factor ∼1.6 Ava/Ph characterizes the 3-H-MPPC channels. 5.2.5 Response stability over time The sensor gain, the calibration factor and the size of the afterpulse and crosstalk component of the signal can be used as a system monitor. Any drift in these parameters is an indication of instability in the system. The calibration data taken at various times during operation provide measurements that indicate the system stability as a function of time. Figure 38 shows the value of the gain for typical 12-H-MPPC channels and 3-S-SiPM channels over the course of several months. Within the uncertainties of the measurements, neither the gain nor the other parameters were found to be drifting over time for any of the sensors used in the ProtoDUNE-SP photon detector system. 5.3 Photon detector performance The PD modules (ARAPUCA and light-guide bars) are exposed to scintillation light from ionization events in the drift volume. A fraction of the emitted photons impinge upon the optical surface of any given PDS module, and a charge signal is issued, proportional to the number of photons detected by the photosensors of the module. The detection efficiency 𝜖𝐷of a PDS module is defined here as the ratio of detected photons to impinging photons. Test-beam data from particles of known type, energy and incident direction in the LAr volume are used to determine 𝜖𝐷and thus evaluate the performance of the different detection technologies implemented in the PDS. For each beam event, the number of detected photons 𝑁Det 𝑗is evaluated from offline data reconstruction (baseline subtraction, waveform integration and charge-to-photon conversion) for – 47 – 2020 JINST 15 P12004 0 20 40 60 80 100 Time [days] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 ] 6 10× Gain [e DUNE:ProtoDUNE-SP 12-H-MPPC 0 20 40 60 80 100 Time [days] 0 2 4 6 ] 6 10× Gain [e DUNE:ProtoDUNE-SP 3-S-SiPM Figure 38. Stability of the photo-sensor response over time: gain stability (charge signal per avalanche) for typical 12-H-MPPC channel (left) and 3-S-SiPM channel (right), from calibration runs performed over ∼ 100 days of operation. The shaded band corresponds to a ±5% gain interval. Gain variations over time are contained well within the band. Statistical error bars are small, not visible inside the symbol. each of the 29 light-guide bars and for each of the 12 cells of the ARAPUCA module in the beam side of the PDS. A Monte-Carlo simulation of test beam events is used for extracting the corresponding number of photons incident 𝑁Inc 𝑗on each PDS element (light-guide module or cell in the ARAPUCA module). This simulation is performed with the LArSoft toolkit [53], which has a detailed description of the geometry of the ProtoDUNE-SP detector, including a proper description of the materials and the positions of the TPC components surrounding the LAr volume (APA, CPA, FC, as shown in figure 39). Simulation of beam events is performed with standard Geant4/LArG4 generator within LArSoft. This accounts for the well known features of scintillation in liquid argon ensuing ionization processes. Photon emission: the emission spectrum is a narrow band in the Vacuum-UV (VUV) wavelength range peaking around 𝜆=128 nm (FWHM≃6nm), exponentially distributed in time with two very different time components (fast ∼5ns and slow ∼1.3−1.4μs, with intensity ratio 0.3 in case of minimum ionizing particles). Electric fields applied to the LAr medium affect the intensity of scintillation emission. At 500 V/cm (ProtoDUNE-SP operation), a photon yield of 2.4×104photons/MeV for minimum ionizing particles is assumed in simulations, 60% of the maximum yield measured at zero field. The relative uncertainty on the photon yield value is 8.5% [55]. The photon yield dependence on increasing linear energy transfer, the rate of energy deposited by ionizing particles, is not included in the current simulation. Photon propagation: LAr is transparent to its own scintillation light. However, during propagation through LAr, VUV photons may undergo Rayleigh scattering, absorption from residual photo-sensitive impurities diluted in LAr and reflections at the boundary surfaces that delimit the LAr volume. In the MC simulation, the Rayleigh scattering length, the reflectivities of materials for VUV photons, and the absorption length as a function of the impurity concentration are parameters that are fixed at their best estimates from existing data. Tracking each of the large number of VUV photons emitted in an event using Geant4 is computationally expensive, so a pre-computed optical library is used to look up the probability that a photon produced at a particular location in the liquid – 48 – 2020 JINST 15 P12004 ARAPUCA dip-coated LG double-shift LG APA3 APA2 APA1 Central Cathode (Muon) Beam Entry Point Figure 39. 3D event display made with the Wire-Cell BEE display [54] showing data from a 7 GeV/𝑐beam muon crossing the whole TPC volume, fully reconstructed by the LArTPC. Only tracks inside a predefined sub-volume (red box) are shown. The beam muon track enters near the cathode and propagates along the beam direction about 10◦downward and 11◦toward the anode plane. Ten bars are located in each APA frame. The dip-coated bars are indicated in blue, the double-shift bars in green and the segmented ARAPUCA bar in orange. argon volume is detected by a specific PDS channel. In order to create the optical library, the liquid argon volume is segmented into small sub-volumes (voxels) of size ∼6×6×6 cm3. For each voxel, a large number (of order 5×105) of VUV photons is sampled with an isotropic angular distribution. All photons are tracked using Geant4, recording how many reach the sensitive area of each optical detector. In the simulation used to create the optical library, the Rayleigh scattering length for VUV photons in liquid argon is assumed to be 90 cm, according to the most recent experimental determination [56,57]. Due to the high level of purity during the beam run (Oxygen equivalent impurity concentration < 100 ppt), absorption by impurities is assumed to be negligible. Light reflection at VUV wavelength is low for perfectly polished metal surfaces (20% or less) and effectively null for any other material. The actual reflectance of the (extruded, non polished) Al profiles of the field cage surrounding the LAr drift volume is unknown and therefore it is set to zero in the current simulations. Once the library is created, ProtoDUNE-SP detector simulation jobs retrieve information from the library when the trajectory of a ionizing particle in the LAr volume is simulated by Geant4, converting the number of emitted photons from energy deposited in each voxel directly into the number of photons impinging upon the area of each PD module coming from this given voxel. The uncertainty on the rate at which photons arrive at the detector after photon – 49 – 2020 JINST 15 P12004 transport is dominated by the uncertainty on the Rayleigh scattering length. Neglecting reflections at the LAr volume boundaries is expected to be a subdominant effect. A relative uncertainty of 5% is assigned to the number of photons incident on the detector surface by varying the Rayleigh length by 20% around the nominal value in the simulation. The uncertainty on the possible bias due to the photon library parameterization is not included in the total uncertainty. Figure 40. Schematic diagram illustrating photons impinging on a TPC wire plane (left) where the wire pitch 𝑝, the wire gauge 𝑑, and the incident photon angle (𝛾) are defined. On the right, the map of transmission — color scale from 0 to 1 — through the set of parallel planes (TPC wire planes and the mesh) as a function of the polar angles 𝜃, 𝜙 of the incident photon direction (the planes lie in the (𝑦, 𝑧)plane, 𝜃=𝛾when 𝜙=±𝜋/2). Photon transmission at the anode plane: the optical surfaces of the PD modules lie immediately behind the four wire planes of the TPC and a fifth (grounding) plane made by the woven metallic meshstretched across the APA frame (see section 2.2). A correctionis applied to account for the light transmission through this series of parallel planes, which is not included in the detector simulation in LArSoft. The geometrical transparency of the mesh (percentage ratio of opening to total area, function of wire gauge and pitch) is 85% The transparency is reduced to 75% when the TPC wire planes above the mesh plane are also considered. This corresponds to the transmission upper value for orthogonal incident light. Transmission at any angle is then obtained based on a geometrical model,5as illustrated in figure 40. A stand-alone simplified MC simulation is then performed to evaluate the transmission of light from beam events. Optical photon emission is sampled over straight trajectories crossing the LAr volume along the beam direction, nearly representing beam muon tracks, or sampled according to a spatial parametrization of electromagnetic showers, representing the longitudinal and transverse energy deposition from incident beam electrons. After photon propagation to the APAs, the angular distribution of incident photons on each PD module is folded with the transmission map to obtain the transmission coefficients for beam muons and beam electrons. These were found in the 65-71% range, mildly depending on the position of the PD module. The relative uncertainty on the transmission coefficients is evaluated to be 7% (one-sided) 5A simplified geometrical model is used in which VUV photons intercepting a wire of the mesh are absorbed (no reflection). The transmission coefficient shows a dependence on the polar angle of incidence (𝜃) almost flat with T=0.75–0.7 for photons incoming with 𝜃 < 45◦and then decreasing above that angle. Only a small modulation in the azimuthal angle 𝜙is expected across the whole range due to the geometrical orientation of the wires and mesh planes (𝜙=±35.7◦,0◦,90◦) and the gauge per pitch ratios (𝑑/𝑝). – 50 – 2020 JINST 15 P12004 to account for the simplified assumptions in the model (no reflection). The transmission coefficients for each module so determined are then used to scale down the number of photons arriving at the APA from the Geant4 MC simulation of the beam events into the actual number of photons 𝑁Inc 𝑗 incident on the surface of the PD module behind the APA. 5.3.1 Efficiency Runs with beam momentum settings from 2 to 7 GeV/𝑐are considered for the efficiency study. The muon and electron samples for the runs at different momenta are selected using the PID information from the beam instrumentation and the recorded light signals passing quality cuts are fully reconstructed (O(10k events/sample) for each run). Correspondingly, Monte Carlo runs were generated with muons or electrons entering the TPC volume from the beam-plug with the same momentum (nominal value and spread) and direction to reproduce the features of the H4-VLE beam line (see section 3). The MC samples were generated with the same number of triggers as were collected in the corresponding data samples. For each run the MC distribution of the number of photons in the event impinging upon the 𝑗-th PDS element (light-guide module or ARAPUCA cell) and the distribution from real data sample of photons detected by the same module/cell are extracted. Muon data: for each of the 12 cells of the ARAPUCA module located in APA 3 of the PDS beam side, the mean value h𝑁Det 𝑗iof the detected photon distribution from the muon data samples with beam momenta of 2, 3, 6 and 7 GeV/𝑐(open circles of assigned color) are displayed in figure 41 (top-left), the mean values h𝑁Inc 𝑗iof the photons incident on the cell surface from the MC muon event samples is shown in the (center-left) panel. Statistical errors are small (few per-mille relative to the mean values, not visible inside the symbols), systematic uncertainties not shown in the figure are discussed later in this section. The detection efficiency 𝜖𝑗=h𝑁Det 𝑗i/h𝑁Inc 𝑗igiven by the ratio of the two mean values in the (bottom-left) panel, for each cell at all momenta. Cells in the ARAPUCA module corresponding to channels 𝑗=1, . . . , 12 are ordered along the 𝑧axis with the upstream cell#1 at the beam entry point (𝑧=0) into the LArTPC volume. Muons at all incident momenta are energetic enough to cross the entire LAr volume and exit from the downstream side (see figure 39). The number of detected photons increases from cell to cell along 𝑧due to the increasing visibility of the muon track from the cells deeper into the LAr volume. In every cell the number of detected photons is observed to increase with incident muon beam momentum (open circles of different color in figure 41) due to the increase in the energy loss along the track for more energetic muons. Cells in the ARAPUCA module are of two types: the last four cells (channels 𝑗=9, . . . , 12) have double size but equal number of photosensors than the first eight. The high step in the number of collected photons 𝑁Inc 𝑗at 𝑗=9— figure 41 (center) — reflects the double geometrical acceptance of these cells. A smaller step is observed in the detected photons 𝑁Det 𝑗(top). This is due to the halved photocathode coverage partly mitigated by the light trapping in the ARAPUCA cell. For each light-guide module in the PDS beam side the number of detected photons and incident photons were evaluated in the same manner from the same beam muon samples (data and MC runs with beam momenta of 2, 3, 6 and 7 GeV/𝑐). The efficiency from the ratio of the detected to incident photons is shown in figure 42 (left) for the 15 double-shift light-guide modules in APA3, 2 and 1 and in figure 43 (left) for the 14 dip-coated light-guide modules. Statistical error bars are small, not visible inside the symbols. The locations of APAs 1, 2, and 3 in the ProtoDUNE-SP detector are shown in figure 39. The efficiency of light-guide modules is expected to be proportional to the – 51 – 2020 JINST 15 P12004 2 4 6 8 10 12 Cell # 6 8 10 12 14 〉 Det N〈 Muons 2.0 GeV/c 3.0 GeV/c 6.0 GeV/c 7.0 GeV/c DUNE:ProtoDUNE-SP ARAPUCA 2 4 6 8 10 12 Cell # 0 20 40 60 80 100 〉 Det N〈 Electrons 2.0 GeV/c 3.0 GeV/c 6.0 GeV/c 7.0 GeV/c DUNE:ProtoDUNE-SP ARAPUCA 2 4 6 8 10 12 Cell # 0 500 1000 1500 2000 〉 Inc N〈 Muons 2.0 GeV/c 3.0 GeV/c 6.0 GeV/c 7.0 GeV/c DUNE:ProtoDUNE-SP ARAPUCA 2 4 6 8 10 12 Cell # 0 2000 4000 6000 〉 Inc N〈 Electrons 2.0 GeV/c 3.0 GeV/c 6.0 GeV/c 7.0 GeV/c DUNE:ProtoDUNE-SP ARAPUCA 2 4 6 8 10 12 Cell # 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Efficiency [%] Muons 2.0 GeV/c 3.0 GeV/c 6.0 GeV/c 7.0 GeV/c DUNE:ProtoDUNE-SP ARAPUCA 2 4 6 8 10 12 Cell # 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Efficiency [%] Electrons 2.0 GeV/c 3.0 GeV/c 6.0 GeV/c 7.0 GeV/c DUNE:ProtoDUNE-SP ARAPUCA Figure 41. ARAPUCA cell efficiency as determined from beam muons (left) and beam electrons (right). Cells are of two types with the last four cells (channels 𝑗=9, . . . , 12) have double size but equal number of photosensors than the first eight. Top: average number of detected photons with beams at different momenta. Center: average number of photons incident on the cell surface from MC simulation of electron and muon beams at corresponding momenta. Bottom: efficiency of the cell from the detected-to-incident ratio. Statistical error bars are small, not visible inside the symbols. number of suitably placed photosensors, and inversely correlated with the length of the optically active surface of a module with fixed width due to the attenuation of internally reflected optical photons. As a crude characterization, the smallness of the ratio of number of photosensors to optically active surface area of the light-guide modules relative to the ARAPUCA cells underlies the corresponding ratios of efficiencies. A factor of two can be gained by instrumenting both ends of the light guides, but improvement beyond that would require modification to the module design to enable effective deployment of additional photosensors. – 52 – 2020 JINST 15 P12004 5 10 15 Module # 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Efficiency [%] Muons 3GeV/c 6GeV/c 7GeV/c DUNE:ProtoDUNE-SP Double-Shift Light Guide 5 10 15 Module # 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Efficiency [%] Electrons 3GeV/c 6GeV/c 7GeV/c DUNE:ProtoDUNE-SP Double-Shift Light Guide Figure 42. Efficiency measurements of 15 double-shift light-guide modules (PDS beam side), as determined from beam muons (left) and beam electrons (right) data at different momenta (only modules 𝑗=1, . . . , 10 in APA3, and 2 at the shorter distance from the shower and higher photon counting are displayed). 5 10 15 Module # 0.0 0.1 0.2 0.3 0.4 Efficiency [%] Muons 3GeV/c 6GeV/c 7GeV/c DUNE:ProtoDUNE-SP Dip-Coated Light Guide 5 10 15 Module # 0.0 0.1 0.2 0.3 0.4 Efficiency [%] Electrons 3GeV/c 6GeV/c 7GeV/c DUNE:ProtoDUNE-SP Dip-Coated Light Guide Figure 43. Efficiency measurements of 14 dip-coated light-guide modules (PDS beam side), as determined from beam muons (left) and beam electrons (right) data at different momenta (only modules 𝑗=1, . . . , 9in APA3, and 2 at the shorter distance from the shower and higher photon counting are displayed). Electron data: electrons with beam momenta of 2, 3, 6 and 7 GeV/𝑐provide data samples for a second independent set of efficiency measurements. Electrons deposit all their incident energy in showers localized in a limited portion of the LAr volume, unlike muons on long, throughgoing tracks. Electromagnetic showers develop in front of APA3, where the ARAPUCA module is positioned nearly at the height of the entering beam (see figure 68 with a 3D display of 7 GeV/𝑐 beam electron event). Light detected in the ARAPUCA cells, the MC estimate of the light arriving on the cells’ optical surfaces and the corresponding detector efficiency are shown in the right-panels of figure 41 (top), (center) and (bottom) respectively. For any given beam energy (open circle colors in the plots), the distribution of the detected photons by the cells along the bar exhibits a shower-like longitudinal profile, an indication of the position reconstruction capability of the segmented ARAPUCA module. The number of detected photons is also clearly correlated with the shower energy. The calorimetric energy reconstruction from scintillation light signals is discussed in section 7.1. The response of the light-guide modules (beam side) to beam electrons were also used for efficiency measurements with beam momenta of 3, 6 and 7 GeV/𝑐, excluding electron data at – 53 – 2020 JINST 15 P12004 2 GeV/𝑐with the modules in APA1 at the farthest distance from the shower and low photon counting. Results are shown in figure 42 (right) for the ten double-shift light-guide modules in APA3 and APA2 and in figure 43 (right) for the nine dip-coated light-guide modules. Efficiency: the photon detection efficiency was evaluated through 8 independent measurements using muon data and electron data at four different beam momenta, for each element of the PDS (12 cells in one ARAPUCA module, 15 double-shift light-guide modules and 14 dip-coated light-guide modules of the PDS beam side). By comparing the results, efficiency estimated from the electron data is found in all elements systematically higher than from the muon data, regardless of the energy of the particle [see figures 41 (bottom), 42 and 43]. The systematic difference may be due to bias in the MC simulation at the photon emission stage (e.g., an unaccounted deviation in scintillation yield for GeV-scale electrons and muons with respect to minimum-ionizing particles) and at the propagation stage (e.g., a difference due to the computational method used to approximate the number of photons reaching the PD optical window from localized volumes (EM showers) and long tracks (muons)). The mean value from all available measurements h𝜖𝑗ifor the 𝑗-th element is taken as the best estimate of the efficiency of that element, and the standard deviation 𝑠𝑗that measures the dispersion around the mean is taken as an estimate of the systematic uncertainty on the efficiency. The statistical uncertainty, evaluated from the standard errors of the mean numbers of detected and incident photons in the data and MC samples of muons and electrons at different energies, is negligible. Table 5. Efficiencies of the detector technologies in the ProtoDUNE-SP PD system: median value among detectors of the same type, determined from the average of independent measurements with beam muons and electrons at different energies. The error is from systematic uncertainty, with negligible statistical uncertainty. The number of detectors of different types examined correspond to the fraction of PDS elements in the beam side upstream APAs (# 3 and 2), selected to determine the median efficiency reported in the efficiency column. Noof PDS elements examined Detector Type Efficiency 8 ARAPUCA cell ˜𝜖𝐴=(2.00 ±0.25)% 4 ARAPUCA cell (double area) ˜𝜖𝐴2=(1.06 ±0.09)% 10 Double-shift module ˜𝜖𝐷𝑆 =(0.21 ±0.03)% 9 Dip-coated module ˜𝜖𝐷𝐶 =(0.08 ±0.02)% Comparing modules or cells of the same type, relative variations in efficiency are within ±6% for the ARAPUCA cells, ±20% for the double-shift modules and greater than ±25% for the dipcoated modules. The median efficiency value with its statistical and systematic uncertainty from each group of detectors (˜𝜖𝐴,˜𝜖𝐴2,˜𝜖𝐷𝑆,˜𝜖𝐷𝐶) is selected to characterize the different technologies implemented in the ProtoDUNE-SP PD system. These value are reported in table 5. The overall relative uncertainty on the efficiency for all detector types is thus found to be 8.5% .𝜎𝜖/𝜖.13.5%, as determined from the set of measurements described above. This appears compatible with the systematic error expected from uncertainty in the parameters of the photon emission and propagation used in the MC generation. – 54 – 2020 JINST 15 P12004 0 100 200 300 400 500 600 700 reco Z 300− 200− 100− 0 100 200 300 reco X 0 5 10 15 20 25 30 35 40 Y [cm]∆Data: Top Face DUNE:ProtoDUNE-SP 0 100 200 300 400 500 600 700 reco Z 300− 200− 100− 0 100 200 300 reco X 40− 35− 30− 25− 20− 15− 10− 5− 0 Y [cm]∆Data: Bottom Face DUNE:ProtoDUNE-SP 300−200−100−0 100 200 300 reco X 0 100 200 300 400 500 600 reco Y 40− 35− 30− 25− 20− 15− 10− 5− 0 Z [cm]∆Data: Upstream Face DUNE:ProtoDUNE-SP 300−200−100−0 100 200 300 reco X 0 100 200 300 400 500 600 reco Y 0 5 10 15 20 25 30 35 40 Z [cm]∆Data: Downstream Face DUNE:ProtoDUNE-SP Figure 49. Spatial distortions normal to the top detector face (upper left), bottom detector face (upper right), upstream detector face (lower left), and downstream detector face (lower right) in ProtoDUNE-SP data. The color axis represents the additive correction (in cm) one must apply to the start/end point of a track passing through the given detector face in order to correct its position to the true entry/exit point at the side of the detector. The first point above may be explained by a combination of potentially using an incorrect value for the argon ion drift velocity, which is not well known in liquid argon, and the possibility of liquid argon flow (not included in simulation) moving the argon ions around in the TPC in addition to their nominal drift in the applied electric field. The second and third points are potentially explained by the effects of liquid argon flow alone. These explanations are at present speculative; more detailed study is ongoing and will be reported in a future work. Given the significant impact of space charge effects on particle trajectories, energies and 𝑑𝐸/𝑑𝑥, as well as the inability for the dedicated space charge simulation to reproduce the observations seen in data, a data-driven simulation of space charge effects was produced for ProtoDUNE-SP using the results shown in figures 49 and 50 as a starting point. This data-driven map of space charge effects (both spatial distortions and electric field distortions, each with three components, for a total of six three-dimensional maps) can be “inverted” to remove effects of space charge in a calibration step for both events in actual data and those produced with this data-driven simulation. The data-driven spatial distortion and electric field distortion maps are produced using the following steps: – 61 – 2020 JINST 15 P12004 0 100 200 300 400 500 600 700 reco Z 300− 200− 100− 0 100 200 300 reco X 0 5 10 15 20 25 30 35 40 Y [cm]∆MC: Top Face DUNE:ProtoDUNE-SP 0 100 200 300 400 500 600 700 reco Z 300− 200− 100− 0 100 200 300 reco X 40− 35− 30− 25− 20− 15− 10− 5− 0 Y [cm]∆MC: Bottom Face DUNE:ProtoDUNE-SP 300−200−100−0 100 200 300 reco X 0 100 200 300 400 500 600 reco Y 40− 35− 30− 25− 20− 15− 10− 5− 0 Z [cm]∆MC: Upstream Face DUNE:ProtoDUNE-SP 300−200−100−0 100 200 300 reco X 0 100 200 300 400 500 600 reco Y 0 5 10 15 20 25 30 35 40 Z [cm]∆MC: Downstream Face DUNE:ProtoDUNE-SP Figure 50. Spatial distortions normal to the top detector face (upper left), bottom detector face (upper right), upstream detector face (lower left), and downstream detector face (lower right) in the original ProtoDUNE-SP Monte Carlo simulation. The color axis represents the additive correction (in cm) one must apply to the start/end point of a track passing through the given detector face in order to correct its position to the true entry/exit point at the side of the detector. •the two-dimensional transverse spatial offset maps at the four detector faces (top, bottom, upstream, and downstream) shown in figures 49 and 50 are used to form a “scale factor” map at each detector face by taking the ratio of the data map to the Monte Carlo map on a pixel-by-pixel basis; •thescale factormapsare used to rescale the simulated three-dimensional spatialdistortion map by linearly interpolating the scale factors between the top and bottom detector faces for spatial distortions in the 𝑦direction, linearly interpolating the scale factors between the upstream and downstream detector faces for spatial distortions in the 𝑧direction, and performing the average of the linear interpolations in these two directions for spatial distortions in the 𝑥direction; the voxel-by-voxel scale factor obtained in this way is used as a multiplicative factor to rescale the spatial distortion magnitude in the corresponding three-dimensional voxel (a “voxel” here refers to a volumetric pixel); •the resulting data-driven spatial distortion maps are then inverted in order to obtain “inverted spatial distortion maps” that can be used to calibrate out spatial distortions in reconstructed – 62 – 2020 JINST 15 P12004 data or Monte Carlo events via repositioning reconstructed ionization charge space points in three dimensions back to their point of original deposition (see more below); and •the gradient of the spatial distortion along the local drift direction, determined using the inverted spatial distortion maps, along with the known ionization electron drift velocity as a function of electric field, are used to obtain the electric field distortion maps (in three dimensions) using a method previously explored at MicroBooNE [60]. The result of this procedure is a set of three-dimensional spatial distortion and electric field maps that are included in the ProtoDUNE-SP simulation used in the first results showcased in this work. The electric field magnitude variations in a couple of slices of the ProtoDUNE-SP TPC are shown in figure 51, comparing to the prediction of the original space charge effect simulation. It is observed in figure 51 that the electric field magnitude variations in data are as large as 25% with respect to the nominal drift electric field; an approximate estimate of the systematic uncertainty on this number is 5% with respect to the nominal drift electric field magnitude (20% relative uncertainty with respect to the full systematic effect), driven by the uncertainty in extrapolating spatial offsets from the detector faces into the center of the detector. The simulation utilizes the data-driven spatial distortion maps to modify the reconstructed position of ionization charge to better represent data events, while the data-driven electric field distortion maps are used to improve the prediction of charge yield after electron-ion recombination, as this effect is dependent on the local electric field magnitude. Additionally, a calibration of particle 𝑑𝐸/𝑑𝑥 was developed for both tracks and the tracklike segments at the beginnings of electromagnetic showers measured by ProtoDUNE-SP. In this calibration, the spatial distortion map is used to correct for spatial squeezing/stretching of charge, impacting 𝑑𝑥, and the electric field distortion map is used to correct for the electric-field-dependence of electron-ion recombination in the liquid argon, impacting 𝑑𝐸. The performance of this first attempt at a calibration of space charge effects at ProtoDUNE-SP is demonstrated in several of the results presented in this work in sections 6.3 and 6.4. The time dependence of space charge effects has been observed to be relatively small during the period of time that ProtoDUNE-SP was taking beam data, on the order of 5% of the total spatial distortion magnitude. A detailed study of the time dependence of space charge effects at ProtoDUNE-SP will be presented in a future work. 6.2 Drift electron lifetime The liquid argon of the ProtoDUNE-SP detector contains impurities, such as water and oxygen, that can capture the ionized electrons as they drift towards the APA. Although the negative ions formed by the attached electrons still drift to the APA, they drift much more slowly than the unattached electrons and contribute negligibly to the signals measured on the APAs. The charge measured by the APAs then becomes reduced due to the impurities capturing the electrons, lowering and biasing the amount of charge measured on the wire planes. This effect is modeled as an exponential decay as a function of time: 𝑄(𝑡)=𝑄0exp (−(𝑡hit −𝑡0)/𝜏),(6.1) where 𝑄(𝑡)is the charge measured on a wire, 𝑄0is the initial charge created by the ionization of the argon accounting for recombination, 𝑡0is the time at which the ionization took place, 𝑡hit is the – 63 – 2020 JINST 15 P12004 300−200−100−0 100 200 300 [cm] true X 0 100 200 300 400 500 600 [cm] true Y 10− 5− 0 5 10 15 20 25 = 347 cm true Z [%]: 0 EE/∆ DUNE:ProtoDUNE-SP Data 0 100 200 300 400 500 600 700 [cm] true Z 300− 200− 100− 0 100 200 300 [cm] true X 10− 5− 0 5 10 15 20 25 = 305 cm true Y [%]: 0 EE/∆ DUNE:ProtoDUNE-SP Data 300−200−100−0 100 200 300 [cm] true X 0 100 200 300 400 500 600 [cm] true Y 10− 5− 0 5 10 15 20 25 = 347 cm true Z [%]: 0 EE/∆ DUNE:ProtoDUNE-SP MC 0 100 200 300 400 500 600 700 [cm] true Z 300− 200− 100− 0 100 200 300 [cm] true X 10− 5− 0 5 10 15 20 25 = 305 cm true Y [%]: 0 EE/∆ DUNE:ProtoDUNE-SP MC Figure 51. Two-dimensional slices of the three-dimensional electric field magnitude distortion map in both ProtoDUNE-SP data (top row) and the original ProtoDUNE-SP Monte Carlo simulation (bottom row); the local electric field distortion magnitude is shown as a percentage of the nominal drift electric field magnitude. Shown are slices in the 𝑧direction (left column) and 𝑦direction (right column), looking at the center of the detector in both slices. time the drifting charge arrived the APA, and 𝜏is the drift electron lifetime. A larger value of 𝜏 corresponds to higher liquid argon purity, as fewer drifting electrons will attach to impurities as they drift to the APA. Purity monitors located inside the cryostat, but outside the field cage, measure the drift electron lifetimes for the argon inside their drift volumes, and thus are not expected to measure exactly the drift electron lifetime in the TPC. Furtheremore, the electric field strength in the purity monitors is lower than the electric field strength inside the TPC. Since the rate of drift electron attachment to impurities depends on the electric field strength, the measured lifetimes in the purity monitors are expected to further differ from that in the TPC. In situ measurements of the drift electron lifetime fromsignalsintheTPCthereforeareneededinordertocalibratetheresultsofcharge-basedanalyses. The drift electron lifetime inside the TPC is measured by fitting the 𝑑𝑄/𝑑𝑥 of collection plane hits from cosmic-ray tracks as a function of drift times. A sample of cosmic-ray tracks that pass – 64 – 2020 JINST 15 P12004 through the front and back faces of the TPC and the CRT are selected. CRT data are used to calibrate track positions and to provide timestamps for TPC tracks. The electron lifetime measurement starts by matching a CRT track to a TPC track using the positions of X and Y from both tracks. The timestamp from the CRT hits serve as the 𝑡0for the TPC track. The 𝑑𝑄/𝑑𝑥 of a hit is defined to be the hit charge (𝑄) obtained from the area of a Gaussian fit to the deconvolved signal divided by the step length from the previous collection plane hit to the current collection plane hit. To avoid the space charge effect distortions on the location of a TPC hit, the CRT track is used to determine the hit’s position in X and Y as a function of Z, which is found through which collection plane wire the hit occurred. The track’s position is then fed into the electric field calibration map from figure 51. This field map corrects the 𝑑𝑄/𝑑𝑥 by a scale factor based on electric field deviations from the space charge effect. The electric field distortions from the space charge effect were observed to cause at most a 1.75% difference in 𝑑𝑄/𝑑𝑥 along the drift distance. These calibrated measurements of 𝑑𝑄calibrated/𝑑𝑥 are fitted to a Landau function convolved with a Gaussian to find the most probable value (MPV) for 100 μs bin in time. An example of such a fit is shown in figure 52. The function that describes the drift electron lifetime then can be quantified by evaluating 𝑑𝑄MPV/𝑑𝑥 as such: 𝑑𝑄(𝑡)MPV 𝑑𝑥 =𝑑𝑄0,MPV 𝑑𝑥 exp(−(𝑡hit −𝑡CRT)/𝜏)(6.2) where adjustments in the timing are made based on the timestamp provided by the CRT. / ndf 2 χ 70.58 / 61 Width 0.41± 11.14 MPV 0.5± 246.2 Area 5.555e+02± 3.016e+04 GSigma 0.847± 7.877 0 200 400 600 800 1000 tick/cm]×dQ/dx [(ADC count) 0 100 200 300 400 500 Number of Hits / ndf 2 χ 70.58 / 61 Width 0.41± 11.14 MPV 0.5± 246.2 Area 5.555e+02± 3.016e+04 GSigma 0.847± 7.877 / ndf 2 χ 70.58 / 61 Width 0.41± 11.14 MPV 0.5± 246.2 Area 5.555e+02± 3.016e+04 GSigma 0.847± 7.877 DUNE:ProtoDUNE-SP <1.8 ms hit 1.7<t Figure 52. Distribution of 𝑑𝑄/𝑑𝑥 for a slice of 100 μs for a lower purity run in early November 2018. Data was taken using the CRT once it became operational on November 1st, 2018 and runs were taken with the CRT during beam data-taking from November 1st, 2018 to November 11th, 2018, the last day of beam. Fits to the MPV of the 𝑑𝑄/𝑑𝑥 distributions as functions of hit time are shown starting the first day in November and for the final day of beam data-taking in figure 53. – 65 – 2020 JINST 15 P12004 During the end of October 2018, the pumps that circulate and purify the liquid argon were not operating due to an external electrical issue. The pumps resumed recirculating and purifying on November 1st and typically take approximately a week to return the TPC back to its previous state of liquid argon purity. Because of this circumstance, these drift electron lifetime measurements show the structure of charge attenuation for a lower purity run and a higher purity run during purity recovery, respectively. 0.5 1.0 1.5 2.0 Hit Time [ms] 150 200 250 300 350 tick/cm]× dQ/dx [(ADC count) / ndf 2 χ 13.59 / 15 Constant 0.8618± 288 Lifetime [ms] - e 0.2586± 10.39 / ndf 2 χ 13.59 / 15 Constant 0.8618± 288 Lifetime [ms] - e 0.2586± 10.39 DUNE:ProtoDUNE-SP Date: 1/11/2018 0.5 1.0 1.5 2.0 Hit Time [ms] 150 200 250 300 350 tick/cm]× dQ/dx [(ADC count) / ndf 2 χ 14.39 / 15 Constant 0.6341± 281.6 Lifetime [ms] - e 14.32± 88.95 / ndf 2 χ 14.39 / 15 Constant 0.6341± 281.6 Lifetime [ms] - e 14.32± 88.95 DUNE:ProtoDUNE-SP Date: 11/11/2018 Figure 53. Plot the MPV of the 𝑑𝑄/𝑑𝑥 distribution as a function of the hit time, fit to an exponential decay function on November 1st, 2018 during a period of lower purity (top) and on November 11th, 2018 during a period of higher purity (bottom). Only statistical errors are included. While the drift electron lifetime can represent the purity, another useful metric is the ratio of 𝑑𝑄/𝑑𝑥 at the anode and cathode or 𝑄𝑐 𝑄𝑎. This is calculated as follows: 𝑄𝑐 𝑄𝑎 =𝑒−𝑡full drift 𝜏(6.3) – 66 – 2020 JINST 15 P12004 where 𝑡full drift is the time it takes to drift from the cathode to the anode, which was measured to be 2.3 ms. Runs were taken with the CRT operating for the last week of beam data-taking. The 𝑄𝑐/𝑄𝑎was measured for each day possible and occurred during a rise in purity in the detector. The systematic uncertainties determined for this measurement are from the uncertainty in the SCE calibration and impacts diffusion have on the hits as a function of drift time. The SCE effect uncertainty is estimated by evaluating the electron lifetime using a different SCE calibration map measured using cosmic muons that cross the cathode and both anodes of the TPC. The difference between the lifetime value obtained using this alternate map and the lifetime value obtained using the SCE calibration discussed in section 6.1 is defined to be 1𝜎of the SCE systematic uncertainty. The uncertainty due to diffusion is estimated by turning diffusion off in the Monte Carlo simulation of the ProtoDUNE-SP detector, with a lifetime set at 35 ms. The difference in the electron lifetime values extracted with and without diffusion in the Monte Carlo simulation is taken to be the 1𝜎 variation due to the diffusion systematic, and is denoted 𝜎diff. The fractional change in the lifetime due to diffusion is 𝜎diff 𝜏=0.143, which corresponds to a difference in 𝑄𝑐/𝑄𝑎by 0.7%, assuming an electron lifetime of 35 ms. This value of the fractional uncertainty in the lifetime is assumed for all dates investigated. Towards the beginning of data-taking 𝑄𝑐/𝑄𝑎was measured as low as 0.801±0.026, which is equivalent to an electron lifetime of 10.4±1.5 ms, as seen in figure 54. Toward the end of datataking, higher purity was achieved with a 𝑄𝑐/𝑄𝑎of 0.9745±0.0063 on November 11th of 2018, a value close to a 100 ms drift electron lifetime. 2018 01/11 2018 02/11 2018 03/11 2018 04/11 2018 05/11 2018 06/11 2018 07/11 2018 08/11 2018 09/11 2018 10/11 2018 11/11 0.4 0.6 0.8 1.0 a /Q c Q DUNE:ProtoDUNE-SP 3 ms 10 ms 30 ms 100 ms Figure 54. Plot of 𝑄𝑐/𝑄𝑎during November 2018 as measured with TPC tracks calibrated with CRT data. Due to the run plan on November 6th, 2018, there was not enough data to make a precision measurement of 𝑄𝑐/𝑄𝑎on that day. Error bars include the statistical uncertainty, the uncertainty from calibrating the SCE, and the uncertainty from diffusion’s impact on the measurement. These electron lifetimes also approximate the amount of impurity in the detector as expressed in units of oxygen equivalent concentration. Measurements indicate that at approximately 500 V/cm, – 67 – 2020 JINST 15 P12004 the impurity can be described as follows: 𝑁O2=1 𝑘𝑎𝜏=300 ppt ms 𝜏(6.4) with 𝑁O2being the concentration equivalent if all impurities were from oxygen and 𝑘𝑎being the attachment constant for oxygen [15]. Considering the inverse relationship between the drift electron lifetime and the amount of oxygen equivalent impurity, the estimate predicts the impurity never went above 40 ppt equivalent of oxygen in the week of data-taking. At the end of beam data-taking on November 11th, 2018, the impurity in the detector can be estimated to be approximately 3.4±0.7 ppt oxygen equivalent. 6.3 Calibration based on cosmic-ray muons The goal of detector calibration is to convert the measured charge in units of ADC counts to energy in units of MeV, which provides important information for particle identification and energy measurements. In order to get reliable calorimetric information, a two-step calibration procedure is employed following the same method developed by the MicroBooNE collaboration [61]. In the first step, the detector response is equalized using throughgoing cosmic-ray muons. In the second step, the absolute energy scale is determined using stopping cosmic-ray muons. In both steps muons that cross the cathode are used because their 𝑡0can be reconstructed (section 4.5.2). The two steps are described in the following sections, using the results for Run 5770 that was taken on Nov. 3, 2018 as an example. 6.3.1 Charge calibration The charge deposition per unit length (𝑑𝑄/𝑑𝑥)in a LArTPC is affected by a number of factors including electronics gain variations, space charge effects, attenuation (due to electronegative impurities like O2and H2O), diffusion, and other effects. Some effects are calibrated out using measurements described in previous sections such as electronics gains (section 4.2) and space charge effects (section 6.1). Calibrating the electron lifetime in situ via cathode-crossing muons is complicated by the very complex space charge effects. Using the CRT allowed a much more precise calibration, but that system was not operable until late in the run and therefore those more precise lifetime measurements were not available for runs taken before then. The effect of diffusion has not been measured yet. In the equalization step, cathode-crossing cosmic-ray muons are used to calibrate the residual nonuniformity in the 𝑑𝑄/𝑑𝑥 values throughout the TPC after the gain and space charge effect calibrations. The following requirements are applied for track selection: •Fiducial volume requirements: the fiducial volume FV1 is defined as a rectangular prism shaped as follows: the boundary from the anode planes is 10 cm, the boundary from the upstream and downstream ends is 40 cm, and the boundary from the top and bottom of the TPC is 40 cm. In order for a track to be selected, its start point and its end point must be outside FV1. •Angular requirements: the reconstruction capability of LArTPCs is limited for tracks that are parallel to the APA wires or contained in a plane containing a wire and the electric field direction. Figure 55 shows the 𝑑𝑄/𝑑𝑥 distribution as a function of 𝜃𝑥𝑧 and 𝜃𝑦𝑧, the two – 68 – 2020 JINST 15 P12004 angles are illustrated in figure 27. To get a sample of well reconstructed tracks, tracks with 65◦<|𝜃𝑥𝑧 |<115◦or 70◦<|𝜃𝑦𝑧 |<110◦are removed, as indicated by the dashed lines in figure 55. 0 50 100 150 200 250 300 350 400 tick/cm]×dQ/dx [(ADC count) 200−100−0 100 200 [deg] XZ θ 200− 100− 0 100 200 [deg] YZ θ x>0 Cosmics data DUNE:ProtoDUNE-SP (a) 𝑥 > 0 0 50 100 150 200 250 300 350 400 tick/cm]×dQ/dx [(ADC count) 200−100−0 100 200 [deg] XZ θ 200− 100− 0 100 200 [deg] YZ θ x<0 Cosmics data DUNE:ProtoDUNE-SP (b) 𝑥 < 0 Figure 55. Average 𝑑𝑄/𝑑𝑥 distributions for ProtoDUNE-SP Run 5770 as functions of 𝜃𝑥𝑧 and 𝜃𝑦𝑧 in the collection plane. The color scale represents average 𝑑𝑄/𝑑𝑥 for a track. The regions inside the dashed lines show the track incident angles excluded for the collection plane. 106764 throughgoing cosmic ray muon tracks were used in making the plots, which constitutes 24.8% of the total number of cathode-crossing tracks in Run 5770. Tracks passing the above selection criteria are used for 𝑑𝑄/𝑑𝑥 calibration. Corrections are obtained in the 𝑦𝑧 plane and as a function of the drift distance. •YZ correction factors: 𝑑𝑄/𝑑𝑥 values in the 𝑦𝑧 plane are affected by many factors including non-uniform wire response caused by nearby dead channels or disconnected wires, detector features such as the electron diverters and the wire support combs, and transverse diffusion. Figures 56(a) and 56(b) show the 𝑑𝑄/𝑑𝑥 distribution in the 𝑦𝑧 plane separately for the 𝑥 > 0 drift volume and the 𝑥 < 0drift volume. The vertical stripes in the 𝑥 < 0plot show places where charge has been collected or distorted by the grounded electron diverters. The first APA on the left in figure 56(b) has a lower-than average 𝑑𝑄/𝑑𝑥 because of the partiallycharged disconnected G plane. The wire support combs also distort the 𝑑𝑄/𝑑𝑥 averages [5], but only by around 5%, and only in very localized positions that are narrower than the bin sizes in the figure. To correct for these non-uniformities we divide the 𝑦𝑧 plane in the two ProtoDUNE-SP drift volumes into a number of 5×5cm2bins. Considering the 𝑑𝑄/𝑑𝑥 values of all the hits lying in a particular bin, the median 𝑑𝑄/𝑑𝑥 value is calculated and denoted (𝑑𝑄/𝑑𝑥)local YZ . Further, the median 𝑑𝑄/𝑑𝑥 value is calculated considering the hits throughout a drift volume, which is denoted (𝑑𝑄/𝑑𝑥)global YZ . The YZ correction factor is then defined as 𝐶(𝑦, 𝑧)=(𝑑𝑄/𝑑𝑥)global YZ (𝑑𝑄/𝑑𝑥)local YZ .(6.5) Figures 57(a) and 57(b) show the YZ correction factors for ProtoDUNE-SP Run 5770. •X correction factors: the 𝑑𝑄/𝑑𝑥 values along the drift direction are affected by factors such as attenuation due to electronegative impurities and longitudinal diffusion. Figure 58(a) – 69 – 2020 JINST 15 P12004 shows the 𝑑𝑄/𝑑𝑥 distribution as a function of 𝑥. The total drift volume is divided into 5 cm bins in the 𝑥coordinate. The 𝑑𝑄/𝑑𝑥 values are first corrected using YZ correction factors based on the 𝑦and 𝑧coordinates of the hit. After the YZ correction, the median 𝑑𝑄/𝑑𝑥 value (𝑑𝑄/𝑑𝑥)local Xis calculated for each bin. The median 𝑑𝑄/𝑑𝑥 value for the whole TPC is denoted (𝑑𝑄/𝑑𝑥)global X. The X correction factor is defined to be 𝐶(𝑥)=(𝑑𝑄/𝑑𝑥)global X (𝑑𝑄/𝑑𝑥)local X .(6.6) Figure 58(b) shows the X correction factors for ProtoDUNE-SP Run 5770. The 𝑑𝑄/𝑑𝑥 value is then normalized to the average value at the two anodes by defining the normalization factor 𝑁𝑄=(𝑑𝑄/𝑑𝑥)anode (𝑑𝑄/𝑑𝑥)global .(6.7) Finally, the corrected 𝑑𝑄/𝑑𝑥 value is given by, (𝑑𝑄/𝑑𝑥)corrected =𝑁𝑄𝐶(𝑦, 𝑧)𝐶(𝑥)(𝑑𝑄/𝑑𝑥)reconstructed (6.8) 50 100 150 200 250 300 350 400 tick/cm]×dQ/dx [(ADC count) 0 200 400 600 Z Coordinate [cm] 0 100 200 300 400 500 600 Y Coordinate [cm] DUNE:ProtoDUNE-SP x>0 Cosmics data (a) 𝑑𝑄/𝑑𝑥 distribution in the 𝑦𝑧 plane, 𝑥 > 0 50 100 150 200 250 300 350 400 tick/cm]×dQ/dx [(ADC count) 0 200 400 600 Z Coordinate [cm] 0 100 200 300 400 500 600 Y Coordinate [cm] DUNE:ProtoDUNE-SP x<0 Cosmics data (b) 𝑑𝑄/𝑑𝑥 distribution in the 𝑦𝑧 plane, 𝑥 < 0 Figure 56. 𝑑𝑄/𝑑𝑥 distributions for ProtoDUNE-SP Run 5770 in the 𝑦𝑧 plane, for 𝑥 > 0(a),𝑥 < 0(b), using cosmic-ray cathode-crossing muons. A sample of 99689 throughgoing cosmic ray muon tracks was used in making the plots, which constitutes 23.2% of the total number of cathode-crossing tracks in Run 5770. Figure 59 shows the 𝑑𝑄/𝑑𝑥 distribution for throughgoing cosmic-ray muons before and after charge calibration. Once the detector response is equalized, a sample of stopping cosmic-ray muons are selected to determine the absolute energy scale. 6.3.2 Energy scale calibration The conversion between ADC counts and the number of electrons is primarily determined by the electronics response, including both the gain and the shaping time, and the field response. Even though the electronics gain is measured by the charge injection system (section 4.2) and the field response is calculated with Garfield (section 4.4), the estimated uncertainty on the measurement and calculation is at least a few percent. On the other hand, the energy loss per unit length for a – 70 – 2020 JINST 15 P12004 0 50 100 150 200 Residual Range [cm] 0 2 4 6 8 10 dE/dX [MeV/cm] 0 50 100 150 200 250 DUNE:ProtoDUNE-SP Expectation Stopping Beam Muons - Data (1 GeV/c) (a) 0 20 40 60 80 100 120 Stopping Beam Muons - MC (1 GeV/c) 0 50 100 150 200 Residual Range [cm] 0 2 4 6 8 10 dE/dX [MeV/cm] DUNE:ProtoDUNE-SP Expectation Stopping Beam Muons - MC (1 GeV/c) (b) Figure 63. 𝑑𝐸/𝑑𝑥 vs residual range for selected stopping muons in the 1GeV/𝑐beam after applying the calibration derived using cosmic-ray muons, in data (a) and MC (b). The expected most probable value of 𝑑𝐸/𝑑𝑥 is plotted as a function of residual range for both. stopping muons is shown for data in figure 63(a) and for MC in figure 63(b). A clear Bragg peak is seen at low residual range, as expected. The measured distribution displays good agreement with the theoretical MPV curve for a stopping muon in argon, in both the minimum ionization region and Bragg peak region of residual range. 6.4.2 Identification and calorimetric energy reconstruction of 1 GeV/𝒄beam protons To understand the detector response to protons interacting in a LArTPC, an analysis procedure, including the selection of beam protons, detector calibration and calorimetric analysis, has been developed. This section describes the results obtained from ProtoDUNE-SP Run 5387. Stopping protons are used for the detector characterization in terms of calorimetry and particle identification. Protons are selected using the same beam-TPC matching criteria described in section 6.4.1. For the 1 GeV/𝑐beam momentum runs considered here, the beam line PID conditions for protons can be found in table 2. The measured beam momentum is used to approximate the stopping range under the assumption it is a proton using the CSDA range. Figure 64 shows the distribution of the reconstructed proton track lengths, divided by their expected CSDA ranges. The distribution peaks at 0.88, which is dominated by the stopping protons. The peak position is less than one because of the energy loss upstream and the SCE. The tail on the left of the distribution is due to the interacting protons, since their drift distances inside the LAr are shorter than those of the stopping protons. The ratio cut, 0.74 ≤(reconstructed proton track length/CSDA range) ≤1.09, is used to select the stopping protons. After the proton event selection, the data-driven corrections, described in section 6.1, are applied to correct for both the spatial and the 𝐸-field distortions due to the SCE. After these corrections, the 𝑑𝑄/𝑑𝑥 values (ADC/cm) of the stopping protons are converted to the corresponding 𝑑𝐸/𝑑𝑥 values (MeV/cm) using the calibration constants described in section 6.3. The same analysis procedure is applied to a Monte Carlo sample. Figures 65(a) and 65(b) show the energy loss versus the residual range of the stopping proton candidates for data and MC, respectively. The data and MC after the calibration procedure show – 77 – 2020 JINST 15 P12004 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Reconstructed Track Length/CSDA Range 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Relative Frequency Cut to select stopping protons DUNE:ProtoDUNE-SP Protons (1 GeV/c) Figure 64. The distribution of the reconstructed proton track length divided by the associated CSDA range. The histogram is normalized such that the maximum frequency is one. The incident beam momentum is 1 GeV/𝑐. The cut to select the stopping protons is indicated. good agreement with the expected MPVs. The distributions of 𝑑𝐸/𝑑𝑥 in the data and the MC are shown in figure 65(c). The MPVs of the 𝑑𝐸/𝑑𝑥 distributions between data and MC agree to better than 1%. 6.4.3 𝒅𝑬/𝒅𝒙 for 1 GeV/𝒄electrons It is important to understand the LArTPC response to electromagnetic showers since DUNE will measure electrons coming from oscillated neutrinos, produced via charged current interactions. Accurate measurement of the calorimetric response of electrons in the ProtoDUNE-SP TPC allows a more precise understanding of 𝑒/𝛾separation and estimation of electron neutrino energy. The 𝑑𝐸/𝑑𝑥 for a photon-induced shower is expected to be twice the 𝑑𝐸/𝑑𝑥 of a single electron at the beginning of the shower, due to the photon conversion into an electron-positron pair. This has been verified in a LArTPC by the ArgoNeuT collaboration [65]. To successfully select 𝜈𝑒charged-current interactions in DUNE, a 𝑑𝐸/𝑑𝑥 metric can be used to remove electromagnetic-like background from interactions such as neutral-current 𝜋0production where the photons from 𝜋0decay can mimic an electron shower. Electrons are selected in Run 5809 taken on Nov. 8, 2018 using the same beam-TPC matching criteria described in section 6.4.1. For the 1 GeV/𝑐beam momentum runs considered here, the beam line PID conditions for electrons can be found in table 2. The reconstruction of electron-induced showers in the detector follows the same procedure as in track-like events. Signal processing (including deconvolution and noise removal) is followed by hit finding and 2D cluster formation. The reconstruction framework Pandora [46] is used to reconstruct 3D showers. The position and the direction of the shower are used to define the beginning of the shower, which is before the electromagnetic cascade develops. To ensure that the electron candidate has not developed a cascade shower before entering the active TPC volume, a completeness cut was – 78 – 2020 JINST 15 P12004 0 100 200 300 400 500 600 700 0 20 40 60 80 100 120 Residual Range [cm] 0 5 10 15 20 25 30 dE/dx [MeV/cm] Expectation DUNE:ProtoDUNE-SP Proton Data (1 GeV/c) (a) 0 100 200 300 400 500 0 20 40 60 80 100 120 Residual Range [cm] 0 5 10 15 20 25 30 dE/dx [MeV/cm] Expectation DUNE:ProtoDUNE-SP Proton MC (1 GeV/c) (b) 0 5 10 15 dE/dx [MeV/cm] 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Relative Frequency Data MC DUNE:ProtoDUNE-SP Protons (1 GeV/c) (c) Figure 65. Stopping proton 𝑑𝐸/𝑑𝑥 versus residual range distributions for the ProtoDUNE-SP beam data (a) and MC (b), the expected most probable values are shown in red. The 𝑑𝐸/𝑑𝑥 distributions after the SCE corrections of data and MC are shown in (c). The histograms in (c) are normalized such that the maximum frequency is one. required. The completeness is defined as the reconstructed shower energy divided by the incoming electron’s momentum. Based on MC studies we expect to have losses due to energy loss upstream and due to signal processing thresholds. If the completeness is at least 80%, the event is selected. To measure 𝑑𝐸/𝑑𝑥, first, the charge deposition per unit length 𝑑𝑄/𝑑𝑥 is measured on a single wire at the collection plane. To calculate the effective pitch 𝑑𝑥 between hits, the direction of the shower is used to measure the actual distance that the electron traverses in the TPC between adjacent wires. Then, following the discussion in section 6.1 the SCE corrections are applied. The conversion from 𝑑𝑄/𝑑𝑥 to 𝑑𝐸/𝑑𝑥 uses the calibration constants described in section 6.3. To measure 𝑑𝐸/𝑑𝑥 at the beginning of the shower, only hits within 4 cm along the direction of the shower and 1 cm perpendicular to the shower are considered and the median 𝑑𝐸/𝑑𝑥 is computed. The same analysis procedure is applied to the Monte Carlo sample. The 𝑑𝐸/𝑑𝑥 distributions for 1 GeV/𝑐electron candidates are shown in figure 66. The 𝑑𝐸/𝑑𝑥 distributions in figure 66 follow the expected Gaussian-convolved Landau distribution with the 𝑑𝐸/𝑑𝑥 peak value corresponding to one single ionizing particle. This results demonstrates an understanding of the 𝑑𝐸/𝑑𝑥 metric for electrons that would be a valuable input for future analyses. – 79 – 2020 JINST 15 P12004 0 2 4 6 8 10 dE/dx [MeV/cm] 0.0 0.2 0.4 0.6 0.8 1.0 Relative Frequency Data MC DUNE:ProtoDUNE-SP Positrons (1 GeV/c) Figure 66. 𝑑𝐸/𝑑𝑥 at the beginning of the shower. The histograms are normalized such that the maximum frequency is one. Electrons are selected using the same beam-TPC matching criteria described in section 6.4.1. For the 1 GeV/𝑐beam momentum runs considered here, the beam line PID conditions for electrons can be found in table 2. The 𝑑𝐸/𝑑𝑥 distributions for various particle all show a good agreement between data and MC in the peak. However, the resolution of 𝑑𝐸/𝑑𝑥 is slightly overestimated in the MC. This could be due to the imperfect modeling of physics processes and/or detector effects. 6.4.4 Particle identification: protons and muons Robust particle identification (PID) is of fundamental importance for the physics goals of ProtoDUNE-SP and the future DUNE experiment. The calorimetric-based PID method in a LArTPC uses the reconstructed energy deposits as a function of residual range for the stopping particles. Event selections of the stopping muons and the stopping protons are described in section 6.3.2 and section 6.4.2, respectively. The stopping protons and muons are shown in figure 67(a). The highly ionizing protons are clearly separated from the muons over the entire range from their stopping points. Based on the obtained calorimetry information, a likelihood-based parameter, 𝜁, is adopted to quantify the PID performance of the ProtoDUNE-SP detector. The method is to compare the particle species with respect to the stopping proton hypothesis. The parameter 𝜁is defined to be: 𝜁=1 𝑛T∑︁ 𝑗(𝑑𝐸 𝑑𝑥 )𝑗(Data)−(𝑑𝐸 𝑑𝑥 )𝑗(MC Proton)2 √︃𝜎(𝑑𝐸 𝑑𝑥 )𝑗(Data)2+𝜎(𝑑𝐸 𝑑𝑥 )𝑗(MC Proton)2,(6.11) where 𝑗is the 𝑗-th measurement before the end of the track, 𝜎(𝑑𝐸 𝑑𝑥 )𝑗is the associated 𝑑𝐸 𝑑𝑥 error of the 𝑗-th hit, 𝑗covers the measurements over the last 26 cm of the track, and 𝑛Tis the total number of hits. Only the collection plane information is used. Figure 67(b) shows the 𝜁distributions of the stopping protons and the stopping muons. The protons and the muons are well-separated. The PID performance for pions is expected to be similar to that of muons. – 80 – 2020 JINST 15 P12004 0 20 40 60 80 100 120 Residual Range [cm] 5 10 15 dE/dx [MeV/cm] 0 20 40 60 80 100 120 140 160 Proton Expectation Muon Expectation DUNE:ProtoDUNE-SP Data (a) 0 50 100 150 200 ζ 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Relative Frequency Proton Data Muon Data Proton MC Muon MC DUNE:ProtoDUNE-SP (b) Figure 67. (a) 𝑑𝐸/𝑑𝑥 versus residual range after the SCE corrections for the stopping protons (upper band) and the muons (lower band). The solid lines represent the expected most probable values for the protons (red) and the muons (blue). (b) The 𝜁distributions of the stopping protons and muons. The histograms are normalized such that the maximum frequency is one. 7 Photon detector response Homogeneous calorimeters are instrumented targets where the kinetic energy (𝐸) of incident particles is absorbed and transformed into a detectable signal. The deposited energy is typically detected in the form of charge or light. When the energy is large enough, a shower of secondary particles is produced (through electromagnetic or strong processes) with progressively reduced energy. If the shower is fully contained and the output signal is efficiently collected, the calorimetric energy resolution is expected to be good, improving with energy as 1/√𝐸. Calorimeters can also provide information on shower position, direction and size as well as arrival time 𝑡0of the particle. A LArTPC is a sophisticated version of a homogeneous calorimeter, with additional imaging and particle identification capabilities. Energy deposition in the liquid argon target yields free charge from ionization and it also yields fast scintillation light. The best energy resolution would be obtained by collecting both the charge and the light signals, which are anticorrelated by the randomness of the recombination processes. With detectors based on LArTPC technology, calorimetry typically relies only on the charge signal collection, while the use of the light signal is limited to 𝑡0determination and triggering purposes. A first attempt to extend the use of scintillation light for calorimetry was recently performed in a low energy range with a small sized LArTPC [66]. Operating protoDUNE-SP on the H4-VLE charged particle test beam offers the opportunity to directly probe with light the calorimetric response of liquid argon to fully contained EM and hadronic showers in the subto few-GeV energy range. 7.1 Calorimetric energy reconstruction from scintillation light and energy resolution As described in section 5.1, in ProtoDUNE-SP the photon detection system comprises a series of optical modules positioned inside the APA frames, behind the TPC wire planes and the grounded mesh. The active liquid argon volume is only on one side of the APA’s, on the side facing the central cathode. The total photo-sensitive area is ∼1.5% of the boundary surface of the LAr volume. The relatively modest optical coverage and the one-sided geometry of the PD system, compared – 81 – 2020 JINST 15 P12004 for example to the 4𝜋coverage of scintillation or Cherenkov detectors, are expected to limit the light yield and the uniformity of the calorimetric response along the drift direction. In this section, beam electrons and data from the ARAPUCA module in the beam side of the PDS are utilized to investigate the light yield and resolution of the ProtoDUNE-SP PD system. Figure 68. 3D event display of a real 7 GeV beam electron in the TPC volume [only tracks inside a predefined sub-volume (red box) are shown]. The beam electron enters near the cathode and an EM shower develops in LAr along the beam direction, about 10◦downward and 11◦toward the anode plane, where the PDS beam side modules are located inside the APA frames. Scintillation photons from energy deposits along the shower are detected by the ARAPUCA module. 7.1.1 Beam electrons and EM showers In a sequence of beam runs, data were collected with incident positive electrons (𝑒+) with energies of 0.3, 0.5, 1, 2, 3, 6 and 7 GeV, providing a large sample of EM showers developing in the LAr volume. The beam is delivered with a typical momentum spread of ±5% around the nominal setting. The beam line instrumentation (see section 3.3.1) provides an event-by-event particle identification and momentum measurement with a precision of Δ𝑝/𝑝≃2.5% [27]. Light signal from the single beam side ARAPUCA module is used for this calorimetric response study. The ARAPUCA module is positioned inside the upstream APA3 frame, nearly at the same height 𝑦of the entering beam and oriented along the 𝑧axis. In figure 68 a 3D display of a 7 GeV electron event from the ProtoDUNE-SP data sample is shown as reconstructed by the TPC. The EM shower develops immediately downstream of the beam entry point in the LArTPC volume in front of the ARAPUCA module, at ∼3 m distance in the 𝑥(drift) direction, and propagates longitudinally along the beam direction, about 10◦downward and 11◦toward the anode plane. Scintillation light is emitted isotropically from every location at which ionization occurs along the shower. The total photo-sensitive area of the ARAPUCA module is ∼0.5×10−3of the surface surrounding the LArTPC active volume (beam side). Summing over the twelve cells in the ARAPUCA module, – 82 – 2020 JINST 15 P12004 0.3 0.5 1.0 2.0 3.0 6.0 7.0 0 1 2 3 4 5 6 7 8 [GeV] e-beam E 0 100 200 300 400 500 600 700 800 900 Ph-detected N 8102 17378 17115 11879 9845 10869 6205 Number of events per beam momentum value Nominal beam momentum [GeV/c] ProtoDUNE - SP Electrons ARAPUCA DUNE:ProtoDUNE - SP Figure 69. Distributions of the incident beam electron energies (left) and corresponding detected photon spectra (right) for the collected seven nominal beam energies (Gaussian fit superimposed — red line). Fit parameters are used in figure 70. The photon spectra are relative to the sum of photons detected by the 12 cells of the ARAPUCA PD module. the total number of photons detected 𝑁Ph =Í𝑁Det 𝑗is evaluated event by event, for each beam run. Monte Carlo events, already used for the efficiency study, are also available, generated as described in section 5.3 with incident electrons as in the beam runs (same energy distributions and direction). Scintillation light from the EM shower development in LAr is propagated to the photo-detector(s) and the total number of photons incident on the surface of the ARAPUCA module is evaluated for each event of the MC simulated momentum run. In figure 69, the energy distribution (left) of the incident electrons from the beam spectrometer of the CERN H4-VLE beam line for each beam run and the corresponding calorimetric response from the ARAPUCA detector (right) expressed by the number of detected photons are shown. A Gaussian fit of both distributions (red lines in figure 69) gives the average electron energy h𝐸eiand the corresponding average photon counting h𝑁Phi, and their spreads (𝜎𝐸, 𝜎𝑁), for each run. The average number of detected photons as a function of the beam energy is shown in figure 70. This relationship gives the calorimetric energy response from light data. Correspondingly, the response as obtained from the MC simulation, expressed by the average number of detectable photons (incident at the detector surface) from EM showers at given electron beam energy, is presented in figure 71 (left). To a first approximation, the average light response is a linear function of the energy over the entire range of tested beam energies as shown in figure 70 (left) with the result of the linear fit. – 83 – 2020 JINST 15 P12004 The slope of the fit 𝑝1gives the light yield 𝑌light =102.1photons/GeV. The quoted 𝑌light is relative to a diffuse light source (EM shower) at a distance of about 3 m (see figure 68). The non-zero (negative) intercept (𝑝0=−8.4photons from the fit) corresponds to an incident energy offset of −82 ±14 MeV from the nominal value for all beam energies. From the CERN H4-VLE beam line MC simulations (section 3.2 and 3.3) beam electrons are expected to release 10-20 MeV in the material in the portion of the beam line downstream the spectrometer and additional ∼20-30 MeV while crossing the materials inside the cryostat from the end of the beam pipe and the active volume of the TPC (cryostat insulation and membrane, beam tube and a thin LAr layer in between). The observed energy offset from the linear fit of the light response provides direct evidence, though in slight excess, of the expected energy loss of beam electrons before entering the LArTPC. A slight deviation from linearity is observed in the light response at higher incident energies, both in the data (figure 70 — left) and in the Monte Carlo (figure 71 — left). This is due to the light response dependence on source-to-detector distance. At higher energies, the longitudinal shower profile extends deeper in the LAr volume along the beam direction and closer to the ARAPUCA module, with some increase of visibility. Based on the reconstruction of the shower profile at the different incident electron energies (see figure 41 — top-right), a geometry correction to the cells’ acceptance has been calculated and a normalization factor applied to the data at different energies. Most of the nonlinearity was then removed. The intercept of the linear fit after correction indicates an energy offset of -56 ±14 MeV, in better agreement with the expected beam electron energy loss in the materials before entering the TPC — details of this study can be found in [67]. 0 2 4 6 8 [GeV] beam 〉 e E〈 0 200 400 600 800 detected 〉 Ph N〈 0 p 1.4±8.4 − 1 p 1.5± 102.1 0 p 1.4±8.4 − 1 p 1.5± 102.1 / ndf 2 χ 3.14 / 5 Prob 0.68 / ndf 2 χ 3.14 / 5 Prob 0.68 Electrons Detected photons Linear Fit DUNE:ProtoDUNE-SP ARAPUCA 0 2 4 6 8 [GeV] beam 〉 e E〈 0.0 0.1 0.2 0.3 0.4 detected 〉 Ph N〈/ N σ 0 k 0.003± 0.062 1 k 0.008± 0.099 2 k 0.009± 0.057 0 k 0.003± 0.062 1 k 0.008± 0.099 2 k 0.009± 0.057 / ndf 2 χ 5.13 / 4 Prob 0.27 / ndf 2 χ 5.13 / 4 Prob 0.27 Electrons Detected photons Fit function Eq.(7.1) DUNE:ProtoDUNE-SP ARAPUCA Figure 70. Number of detected photons (Gaussian fit mean value, from figure 69 right) as a function of incident electron energy (Gaussian fit mean value, from figure 69 - left) (left panel). Reconstructed energy resolution from the detected photon distributions (Gaussian fit standard deviation to the mean ratio) (right panel). A line of slope 𝑝1and intercept 𝑝0is fit to the data in the left-hand plot, and the function in equation (7.1) is fit to the data in the right-hand plot. Based on the linearity of the light response, the relative calorimetric energy resolution 𝜎𝐸/𝐸e is obtained from the 𝜎𝑁/𝑁Ph ratio of the light response (figure 70 — right). The energy resolution, as for a homogeneous calorimeter, can be expressed in a general form [68] depending on three different contributions: 𝜎𝐸 𝐸=𝑘0⊕𝑘1 √𝐸⊕𝑘2 𝐸(7.1) where the symbol ⊕indicates a quadratic sum and 𝐸is in GeV. The terms on the right-hand – 84 – 2020 JINST 15 P12004 side are referred to below as the “constant term”, the “stochastic term” and the “noise term.” The relative weight of the three terms depends on the energy of the incident particle. The stochastic term contribution to the energy resolution comes from the statistical fluctuations in the number of photonsdetected. The relativelylargevalue (𝑘1=9.9%)—whencomparedto typicalhomogeneous calorimeters — is ascribed to the limited photo-sensitive coverage of the ARAPUCA module. The noisetermcomesfromthe electronicnoise ofthe readoutchain. Itsvalue(𝑘2=0.057 GeV)isexactly as expected from the measured signal-to-noise-ratio of the ARAPUCA readout (section 5.2.2). The constant term is large (𝑘0=6.2%) and due to different contributions. The main one comes from the incident beam electron energy spread (figure 69 — left), depending on the actual beam line configuration (collimators aperture at different momentum setting). The mean value of the relative energy spread 𝜎𝐸/𝐸𝑒=(5.8±0.4)%. An additional contribution comes from fluctuations in the energy loss in the materials before electrons enter the TPC. Since the energy loss occurs downstream of the momentum spectrometer, this energy degradation and its fluctuation do not appear in the incident beam energy spectra and it is evaluated by simulations (∼2%). Other contributions to the resolution, such as non-uniformity on the detector illumination, channel to channel response variation and possible shower leakage across the cathode, have been investigated and shown to be negligible [67]. The light yield and resolution response obtained from a single ARAPUCA 0 2 4 6 8 [GeV] beam 〉 e E〈 0 10 20 30 40 3 10× incident 〉 Ph N〈 0 q 78±693 − 1 q 86± 6051 0 q 78±693 − 1 q 86± 6051 / ndf 2 χ 8.37 / 5 Prob 0.14 / ndf 2 χ 8.37 / 5 Prob 0.14 Electrons Simulation DUNE:ProtoDUNE-SP ARAPUCA 0 2 4 6 8 [GeV] beam 〉 e E〈 0.6 0.8 1.0 1.2 1.4 simulated 〉 Ph N〈 / detected 〉 Ph N〈 Electrons DUNE:ProtoDUNE-SP ARAPUCA Figure 71. Monte Carlo simulation of light response to EM showers: average number of incident photons at the ARAPUCA bar surface from simulation vs average electron beam energy (left) and Data/MC comparison (right) by the ratio of average number of photons detected to the corresponding number of photons from MC simulation, at the different beam energies. module is adequate. An extrapolation of the performance to a PDS system consisting entirely of ARAPUCA modules, obtained by scaling the detected photon response of the light guide bars with the ratio of the ARAPUCA-to-light guide efficiencies (presented in section 5.3.1), indicates that it can perform calorimetric energy reconstruction with an expected light yield of 1.9 photons/MeV. This performance exceeds the specifications of the DUNE Far Detector [5] by almost a factor of four. A comparison of electron data and the corresponding MC simulation can be used to validate the simulation of the light propagation and collection. The average number of photons incident on the surface of the ARAPUCA module from the MC (shown in figure 71 — left) is scaled by a normalization factor 𝜂, an average value over the ARAPUCA cells’ efficiency shown in section 5.3.1, to give the simulated detected photons h𝑁Phisimulated =𝜂h𝑁Phiincident. The ratio h𝑁Phidetected/h𝑁Phisimulated is shown as a function of incident beam energy in figure 71 (right). A – 85 – 2020 JINST 15 P12004 systematic deviation, between 5 and ≤10%, is found for electrons with energies between 0.3GeV and 1 GeV. This deviation is attributed to the statistical limitations on the visibility values of the photon library used in the Monte Carlo for converting the energy deposited along the EM shower into the number of photons impinging upon the ARAPUCA module. 8 Conclusions This paper summarizes the first results on the performance of the ProtoDUNE-SP LArTPC using large samples of data from a test-beam run at the CERN Neutrino Platform. The dedicated H4-VLE beam line delivers electrons, pions, protons and kaons in the 0.3 – 7 GeV/𝑐momentum range, which are crucial to the study of detector performance and the measurement of particle-argon cross sections. In table 7the detector’s high-level performance parameters from studies and findings presented in this report are shown and they are compared with the corresponding DUNE SP Far Detector design specifications. For each of the categories shown, the ProtoDUNE-SP performance meets or exceeds the DUNE specification, in several cases by a large margin. This successful performance demonstrates the effectiveness of the single-phase detector design and the execution of the fabrication, assembly, installation, commissioning, and operations phases [11]. Table 7. ProtoDUNE-SP performance for main parameters and corresponding DUNE specifications. Detector parameter ProtoDUNE-SP performance DUNE specification Average drift electric field 500 V/cm 250 V/cm (min) 500 V/cm (nominal) LAr e-lifetime >20 ms >3 ms TPC+CE Noise (C) 550 e, (I) 650 e ENC (raw) <1000 e ENC Signal-to-noise hSNRi(C) 48.7, (I) 21.2 (w/CNR) CE dead channels 0.2% <1% PDS light yield 1.9 photons/MeV >0.5 photons/MeV (@3.3 m distance) (@cathode distance — 3.6 m) PDS time resolution 14 ns <100 ns The electric field in the TPC drift volume was stable at the nominal level of 500 V/cm with >99.5% of uptime during the data taking periods with beam and cosmics. A drift electron lifetime in LAr in excess of 20 ms has been achieved and it was sustained for an extended period of data-taking. It reached approximately (89 ±22)ms for the last day of beam data-taking. This corresponds to a concentration of impurity in the liquid argon of 3.4±0.7 ppt oxygen equivalent. The DUNE Far Detector specification is for the impurity concentration to be less than 100 ppt O2equivalent. The TPC and cold electronics show excellent signal-to-noise performance. The signal-to-noise ratios corresponding to the most-probable-value ionization of a minimum ionizing particle are 40.3, 15.1 and 18.6, for collection, U and V wires, respectively, after noise filtering and signal processing. – 86 – 2020 JINST 15 P12004 F. Gonnella,13 J.A. Gonzalez-Cuevas,6M.C. Goodman,4O. Goodwin,123 S. Goswami,149 C. Gotti,83 E. Goudzovski,13 C. Grace,117 M. Graham,160 E. Gramellini,189 R. Gran,129 E. Granados,70 A. Grant,48 C. Grant,15 D. Gratieri,63 P. Green,123 S. Green,31 L. Greenler,188 M. Greenwood,141 J. Greer,16 W.C. Griffith,171 M. Groh,98 J. Grudzinski,4K. Grzelak,184 W. Gu,17 V. Guarino,4R. Guenette,72 A. Guglielmi,86 B. Guo,167 K.K. Guthikonda,109 R. Gutierrez,3P. Guzowski,123 M.M. Guzzo,32 S. Gwon,36 K. Haaf,61 A. Habig,129 A. Hackenburg,189 H. Hadavand,175 R. Haenni,11 A. Hahn,61 J. Haigh,185 J. Haiston,165 T. Hamernik,61 P. Hamilton,95 J. Han,151 K. Harder,159 D.A. Harris,61,191 J. Hartnell,171 T. Hasegawa,107 R. Hatcher,61 E. Hazen,15 A. Heavey,61 K.M. Heeger,189 J. Heise,161 K. Hennessy,118 S. Henry,156 M.A. Hernandez Morquecho,70 K. Herner,61 L. Hertel,26 A.S. Hesam,21 J. Hewes,37 A. Higuera,74 T. Hill,93 S.J. Hillier,13 A. Himmel,61 J. Hoff,61 C. Hohl,10 A. Holin,180 E. Hoppe,143 G.A. Horton-Smith,110 M. Hostert,51 A. Hourlier,124 B. Howard,61 R. Howell,156 J. Hrivnak,21 J. Huang,176 J. Huang,25 J. Hugon,120 G. Iles,95 N. Ilic,177 A.M. Iliescu,79 R. Illingworth,61 A. Ioannisian,190 R. Itay,160 A. Izmaylov,77 E. James,61 B. Jargowsky,26 F. Jediny,44 C. Jesùs-Valls,76 X. Ji,17 L. Jiang,183 S. Jiménez,22 A. Jipa,18 A. Joglekar,28 C. Johnson,41 R. Johnson,37 B. Jones,175 S. Jones,180 C.K. Jung,169 T. Junk,61,∗ Y. Jwa,42 M. Kabirnezhad,142 A. Kaboth,159 I. Kadenko,113 F. Kamiya,59 G. Karagiorgi,42 A. Karcher,117 M. Karolak,20 Y. Karyotakis,47 S. Kasai,112 S.P. Kasetti,120 L. Kashur,41 N. Kazaryan,190 E. Kearns,15 P. Keener,147 K.J. Kelly,61 E. Kemp,32 C. Kendziora,61 W. Ketchum,61 S.H. Kettell,17 M. Khabibullin,89 A. Khotjantsev,89 A. Khvedelidze,65 D. Kim,21 B. King,61 B. Kirby,17 M. Kirby,61 J. Klein,147 K. Koehler,188 L.W. Koerner,74 S. Kohn,24,117 P.P. Koller,11 M. Kordosky,187 T. Kosc,90 U. Kose,21 V.A. Kostelecký,98 K. Kothekar,16 F. Krennrich,101 I. Kreslo,11 Y. Kudenko,89 V.A. Kudryavtsev,164 S. Kulagin,89 J. Kumar,73 R. Kumar,154 C. Kuruppu,167 V. Kus,44 T. Kutter,120 B. Lacarelle,21 A. Lambert,117 K. Lande,147 C.E. Lane,49 K. Lang,176 T. Langford,189 P. Lasorak,171 D. Last,147 C. Lastoria,22 A. Laundrie,188 A. Lawrence,117 I. Lazanu,18 R. LaZur,41 T. Le,178 J. Learned,73 P. LeBrun,90 G. Lehmann Miotto,21 R. Lehnert,98 M.A. Leigui de Oliveira,59 M. Leitner,117 M. Leyton,76 L. Li,26 S. Li,17 S.W. Li,160 T. Li,54 Y. Li,17 H. Liao,110 C.S. Lin,117 S. Lin,120 A. Lister,188 B.R. Littlejohn,94 J. Liu,26 T. Liu,35 S. Lockwitz,61 T. Loew,117 M. Lokajicek,43 I. Lomidze,65 K. Long,95 K. Loo,106 D. Lorca,11 T. Lord,185 J.M. LoSecco,139 W.C. Louis,119 K.B. Luk,24,117 X. Luo,29 N. Lurkin,13 T. Lux,76 V.P. Luzio,59 D. MacFarland,160 A.A. Machado,32 P. Machado,61 C.T. Macias,98 J.R. Macier,61 A. Maddalena,67 P. Madigan,24,117 S. Magill,4K. Mahn,126 A. Maio,114,55 J.A. Maloney,45 G. Mandrioli,79 J. Maneira,114,55 L. Manenti,180 S. Manly,156 A. Mann,178 K. Manolopoulos,159 M. Manrique Plata,98 A. Marchionni,61 W. Marciano,17 D. Marfatia,73 C. Mariani,183 J. Maricic,73 F. Marinho,58 A.D. Marino,40 M. Marshak,130 C. Marshall,117 J. Marshall,185 J. Marteau,90 J. Martin-Albo,77 N. Martinez,110 D.A. Martinez Caicedo ,165 S. Martynenko,169 K. Mason,178 A. Mastbaum,158 M. Masud,77 S. Matsuno,73 J. Matthews,120 C. Mauger,147 N. Mauri,79,14 K. Mavrokoridis,118 R. Mazza,83 A. Mazzacane,61 E. Mazzucato,20 E. McCluskey,61 N. McConkey,123 K.S. McFarland,156 C. McGrew,169 A. McNab,123 A. Mefodiev,89 P. Mehta,104 P. Melas,7M. Mellinato,83,127 O. Mena,77 S. Menary,191 H. Mendez,153 A. Menegolli,87,146 G. Meng,86 M.D. Messier,98 W. Metcalf,120 M. Mewes,98 H. Meyer,186 T. Miao,61 G. Michna,166 T. Miedema,135,155 J. Migenda,164 R. Milincic,73 W. Miller,130 J. Mills,178 C. Milne,93 O. Mineev,89 O.G. Miranda,38 S. Miryala,17 C.S. Mishra,61 S.R. Mishra,167 A. Mislivec,130 D. Mladenov,21 I. Mocioiu,148 K. Moffat,51 N. Moggi,79,14 R. Mohanta,75 T.A. Mohayai,61 N. Mokhov,61 J. Molina,6 L. Molina Bueno,53 A. Montanari,79 C. Montanari,87,146 D. Montanari,61 L.M. Montano Zetina,38 J. Moon,124 M. Mooney,41 A. Moor,31 D. Moreno,3B. Morgan,185 C. Morris,74 C. Mossey,61 E. Motuk,180 C.A. Moura,59 J. Mousseau,125 W. Mu,61 L. Mualem,30 J. Mueller,41 M. Muether,186 S. Mufson,98 F. Muheim,54 A. Muir,48 M. Mulhearn,25 H. Muramatsu,130 S. Murphy,53 J. Musser,98 J. Nachtman,100 S. Nagu,121 M. Nalbandyan,190 R. Nandakumar,159 D. Naples,151 S. Narita,102 D. Navas-Nicolás,22 N. Nayak,26 M. Nebot-Guinot,54 L. Necib,30 K. Negishi,102 J.K. Nelson,187 J. Nesbit,188 M. Nessi,21 D. Newbold,159 M. Newcomer,147 D. Newhart,61 R. Nichol,180 E. Niner,61 K. Nishimura,73 A. Norman,61 A. Norrick,61 R. Northrop,35 P. Novella,77 J.A. Nowak,116 M. Oberling,4A. Olivares Del Campo,51 – 93 – 2020 JINST 15 P12004 A. Olivier,156 Y. Onel,100 Y. Onishchuk,113 J. Ott,26 L. Pagani,25 S. Pakvasa,73 O. Palamara,61 S. Palestini,21 J.M. Paley,61 M. Pallavicini,81,64 C. Palomares,22 E. Pantic,25 V. Paolone,151 V. Papadimitriou,61 R. Papaleo,88 A. Papanestis,159 S. Paramesvaran,16 S. Parke,61 Z. Parsa,17 M. Parvu,18 S. Pascoli,51 L. Pasqualini,79,14 J. Pasternak,95 J. Pater,123 C. Patrick,180 L. Patrizii,79 R.B. Patterson,30 S.J. Patton,117 T. Patzak,145 A. Paudel,110 B. Paulos,188 L. Paulucci,59 Z. Pavlovic,61 G. Pawloski,130 D. Payne,118 V. Pec,164 S.J. M. Peeters,171 Y. Penichot,20 E. Pennacchio,90 A. Penzo,100 O.L. G. Peres,32 J. Perry,54 D. Pershey,50 G. Pessina,83 G. Petrillo,160 C. Petta,33,80 R. Petti,167 F. Piastra,11 L. Pickering,126 F. Pietropaolo,21,86 J. Pillow,185 J. Pinzino,177 R. Plunkett,61 R. Poling,130 X. Pons,21 N. Poonthottathil,101 S. Pordes,61 M. Potekhin,17 R. Potenza,33,80 B.V. K.S. Potukuchi,103 J. Pozimski,95 M. Pozzato,79,14 S. Prakash,32 T. Prakash,117 S. Prince,72 G. Prior,114 D. Pugnere,90 K. Qi,169 X. Qian,17 J.L. Raaf,61 R. Raboanary,2V. Radeka,17 J. Rademacker,16 B. Radics,53 A. Rafique,4E. Raguzin,17 M. Rai,185 M. Rajaoalisoa,37 I. Rakhno,61 H.T. Rakotondramanana,2L. Rakotondravohitra,2Y.A. Ramachers,185 R. Rameika,61 M.A. Ramirez Delgado,70 B. Ramson,61 A. Rappoldi,87,146 G. Raselli,87,146 P. Ratoff,116 S. Ravat,21 H. Razafinime,2J.S. Real,69 B. Rebel,188,61 D. Redondo,22 M. Reggiani-Guzzo,32 T. Rehak,49 J. Reichenbacher,165 S.D. Reitzner,61 A. Renshaw,74 S. Rescia,17 F. Resnati,21 A. Reynolds,142 G. Riccobene,88 L.C. J. Rice,151 K. Rielage,119 A. Rigamonti,21 Y. Rigaut,53 D. Rivera,147 L. Rochester,160 M. Roda,118 P. Rodrigues,142 M.J. Rodriguez Alonso,21 J. Rodriguez Rondon,165 A.J. Roeth,50 H. Rogers,41 S. Rosauro-Alcaraz,122 M. Rosenthal,21 M. Rossella,87,146 J. Rout,104 S. Roy,71 A. Rubbia,53 C. Rubbia,66 B. Russell,117 J. Russell,160 D. Ruterbories,156 R. Saakyan,180 S. Sacerdoti,145 T. Safford,126 N. Sahu,97 P. Sala,84,21 G. Salukvadze,21 N. Samios,17 M.C. Sanchez,101 D.A. Sanders,131 D. Sankey,159 S. Santana,153 M. Santos-Maldonado,153 N. Saoulidou,7P. Sapienza,88 C. Sarasty,37 I. Sarcevic,5G. Savage,61 V. Savinov,151 A. Scaramelli,87 A. Scarff,164 A. Scarpelli,17 H. Schellman,141,61 P. Schlabach,61 D. Schmitz,35 K. Scholberg,50 A. Schukraft,61 E. Segreto,32 E. Seltskaya,21 J. Sensenig,147 I. Seong,26 A. Sergi,13 F. Sergiampietri,169 D. Sgalaberna,53 M.H. Shaevitz,42 S. Shafaq,104 M. Shamma,28 H.R. Sharma,103 R. Sharma,17 T. Shaw,61 C. Shepherd-Themistocleous,159 S. Shin,105 D. Shooltz,126 R. Shrock,169 L. Simard,115 N. Simos,17 J. Sinclair,11 G. Sinev,50 J. Singh,121 J. Singh,121 V. Singh,23,9 R. Sipos,21 F.W. Sippach,42 G. Sirri,79 A. Sitraka,165 K. Siyeon,36 D. Smargianaki,169 A. Smith,50 A. Smith,31 E. Smith,98 P. Smith,98 J. Smolik,44 M. Smy,26 P. Snopok,94 M. Soares Nunes,32 H. Sobel,26 M. Soderberg,172 C.J. Solano Salinas,99 S. Söldner-Rembold,123 N. Solomey,186 V. Solovov,114 W.E. Sondheim,119 M. Sorel,77 J. Soto-Oton,22 A. Sousa,37 K. Soustruznik,34 F. Spagliardi,142 M. Spanu,17 J. Spitz,125 N.J. C. Spooner,164 K. Spurgeon,172 R. Staley,13 M. Stancari,61 L. Stanco,86 D. Stefan,21 H.M. Steiner,117 J. Stewart,17 B. Stillwell,35 J. Stock,165 F. Stocker,21 T. Stokes,120 M. Strait,130 T. Strauss,61 S. Striganov,61 A. Stuart,39 R. Sulej,132,61 D. Summers,131 A. Surdo,82 V. Susic,10 L. Suter,61 C.M. Sutera,33,80 R. Svoboda,25 B. Szczerbinska,174 A.M. Szelc,123 R. Talaga,4H. A. Tanaka,160 B. Tapia Oregui,176 A. Tapper,95 S. Tariq,61 E. Tatar,93 R. Tayloe,98 A.M. Teklu,169 M. Tenti,79 K. Terao,160 C.A. Ternes,77 F. Terranova,83,127 G. Testera,81 A. Thea,159 J.L. Thompson,164 C. Thorn,17 S.C. Timm,61 A. Tonazzo,145 M. Torti,83,127 M. Tortola,77 F. Tortorici,33,80 D. Totani,61 M. Toups,61 C. Touramanis,118 J. Trevor,30 W.H. Trzaska,106 Y.T. Tsai,160 Z. Tsamalaidze,65 K.V. Tsang,160 N. Tsverava,65 S. Tufanli,21 C. Tull,117 E. Tyley,164 M. Tzanov,120 M.A. Uchida,31 J. Urheim,98 T. Usher,160 M.R. Vagins,111 P. Vahle,187 G.A. Valdiviesso,56 E. Valencia,187 Z. Vallari,30 J.W. F. Valle,77 S. Vallecorsa,21 R. Van Berg,147 R.G. Van de Water,119 D. Vanegas Forero,32 F. Varanini,86 D. Vargas,76 G. Varner,73 J. Vasel,98 G. Vasseur,20 K. Vaziri,61 S. Ventura,86 A. Verdugo,22 S. Vergani,31 M.A. Vermeulen,135 M. Verzocchi,61 H. Vieira de Souza,32 C. Vignoli,67 C. Vilela,169 B. Viren,17 T. Vrba,44 T. Wachala,134 A.V. Waldron,95 M. Wallbank,37 H. Wang,27 J. Wang,25 Y. Wang,27 Y. Wang,169 K. Warburton,101 D. Warner,41 M. Wascko,95 D. Waters,180 A. Watson,13 P. Weatherly,49 A. Weber,159,142 M. Weber,11 H. Wei,17 A. Weinstein,101 D. Wenman,188 M. Wetstein,101 M.R. While,165 A. White,175 L.H. Whitehead,31 D. Whittington,172 M.J. Wilking,169 C. Wilkinson,11 Z. Williams,175 F. Wilson,159 R.J. Wilson,41 J. Wolcott,178 T. Wongjirad,178 K. Wood,169 L. Wood,143 E. Worcester,17 M. Worcester,17 C. Wret,156 W. Wu,61 W. Wu,26 Y. Xiao,26 G. Yang,169 – 94 – 2020 JINST 15 P12004 T. Yang,61,∗N. Yershov,89 K. Yonehara,61 T. Young,136 B. Yu,17 H.W. Yu,17 H.Z. Yu,170 J. Yu,175 Z.Y. Yu,78 R. Zaki,191 J. Zalesak,43 L. Zambelli,47 B. Zamorano,68 A. Zani,21,84 L. Zazueta,187 G.P. Zeller,61 J. Zennamo,61 K. Zeug,188 C. Zhang,17 M. Zhao,17 E. Zhivun,17 G. Zhu,140 E.D. Zimmerman,40 M. Zito,20 S. Zucchelli,79,14 J. Zuklin,43 V. Zutshi,137 and R. Zwaska61 ∗Corresponding author 1University of Amsterdam, NL-1098 XG Amsterdam, The Netherlands 2University of Antananarivo, Antananarivo 101, Madagascar 3Universidad Antonio Nariño, Bogotá, Colombia 4Argonne National Laboratory, Argonne, IL 60439, U.S.A. 5University of Arizona, Tucson, AZ 85721, U.S.A. 6Universidad Nacional de Asunción, San Lorenzo, Paraguay 7University of Athens, Zografou GR 157 84, Greece 8Universidad del Atlántico, Atlántico, Colombia 9Banaras Hindu University, Varanasi — 221 005, India 10 University of Basel, CH-4056 Basel, Switzerland 11 University of Bern, CH-3012 Bern, Switzerland 12 Beykent University, Istanbul, Turkey 13 University of Birmingham, Birmingham B15 2TT, United Kingdom 14 Università del Bologna, 40127 Bologna, Italy 15 Boston University, Boston, MA 02215, U.S.A. 16 University of Bristol, Bristol BS8 1TL, United Kingdom 17 Brookhaven National Laboratory, Upton, NY 11973, U.S.A. 18 University of Bucharest, Bucharest, Romania 19 Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro, RJ 22290-180, Brazil 20 CEA/Saclay, IRFU Institut de Recherche sur les Lois Fondamentales de l’Univers, F-91191 Gif-sur-Yvette CEDEX, France 21 CERN, The European Organization for Nuclear Research, 1211 Meyrin, Switzerland 22 CIEMAT, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, E-28040 Madrid, Spain 23 Central University of South Bihar, Gaya – 824236, India 24 University of California Berkeley, Berkeley, CA 94720, U.S.A. 25 University of California Davis, Davis, CA 95616, U.S.A. 26 University of California Irvine, Irvine, CA 92697, U.S.A. 27 University of California Los Angeles, Los Angeles, CA 90095, U.S.A. 28 University of California Riverside, Riverside CA 92521, U.S.A. 29 University of California Santa Barbara, Santa Barbara, California 93106 U.S.A. 30 California Institute of Technology, Pasadena, CA 91125, U.S.A. 31 University of Cambridge, Cambridge CB3 0HE, United Kingdom 32 Universidade Estadual de Campinas, Campinas — SP, 13083-970, Brazil 33 Università di Catania, 2 — 95131 Catania, Italy 34 Institute of Particle and Nuclear Physics of the Faculty of Mathematics and Physics of the Charles University, 180 00 Prague 8, Czech Republic 35 University of Chicago, Chicago, IL 60637, U.S.A. 36 Chung-Ang University, Seoul 06974, South Korea 37 University of Cincinnati, Cincinnati, OH 45221, U.S.A. 38 Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Mexico City, Mexico 39 Universidad de Colima, Colima, Mexico 40 University of Colorado Boulder, Boulder, CO 80309, U.S.A. 41 Colorado State University, Fort Collins, CO 80523, U.S.A. 42 Columbia University, New York, NY 10027, U.S.A. 43 Institute of Physics, Czech Academy of Sciences, 182 00 Prague 8, Czech Republic – 95 – 2020 JINST 15 P12004 44 Czech Technical University, 115 19 Prague 1, Czech Republic 45 Dakota State University, Madison, SD 57042, U.S.A. 46 University of Dallas, Irving, TX 75062-4736, U.S.A. 47 Laboratoire d’Annecy-le-Vieux de Physique des Particules, CNRS/IN2P3 and Université Savoie Mont Blanc, 74941 Annecy-le-Vieux, France 48 Daresbury Laboratory, Cheshire WA4 4AD, United Kingdom 49 Drexel University, Philadelphia, PA 19104, U.S.A. 50 Duke University, Durham, NC 27708, U.S.A. 51 Durham University, Durham DH1 3LE, United Kingdom 52 Universidad EIA, Antioquia, Colombia 53 ETH Zurich, Zurich, Switzerland 54 University of Edinburgh, Edinburgh EH8 9YL, United Kingdom 55 Faculdade de Ciências da Universidade de Lisboa — FCUL, 1749-016 Lisboa, Portugal 56 Universidade Federal de Alfenas, Poços de Caldas — MG, 37715-400, Brazil 57 Universidade Federal de Goias, Goiania, GO 74690-900, Brazil 58 Universidade Federal de São Carlos, Araras — SP, 13604-900, Brazil 59 Universidade Federal do ABC, Santo André — SP, 09210-580 Brazil 60 Universidade Federal do Rio de Janeiro, Rio de Janeiro — RJ, 21941-901, Brazil 61 Fermi National Accelerator Laboratory, Batavia, IL 60510, U.S.A. 62 University of Florida, Gainesville, FL 32611-8440, U.S.A. 63 Fluminense Federal University, 9 Icaraí Niterói — RJ, 24220-900, Brazil 64 Università degli Studi di Genova, Genova, Italy 65 Georgian Technical University, Tbilisi, Georgia 66 Gran Sasso Science Institute, L’Aquila, Italy 67 Laboratori Nazionali del Gran Sasso, L’Aquila AQ, Italy 68 University of Granada & CAFPE, 18002 Granada, Spain 69 University Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, 38000 Grenoble, France 70 Universidad de Guanajuato, Guanajuato, C.P. 37000, Mexico 71 Harish-Chandra Research Institute, Jhunsi, Allahabad 211 019, India 72 Harvard University, Cambridge, MA 02138, U.S.A. 73 University of Hawaii, Honolulu, HI 96822, U.S.A. 74 University of Houston, Houston, TX 77204, U.S.A. 75 University of Hyderabad, Gachibowli, Hyderabad — 500 046, India 76 Institut de Fìsica d’Altes Energies, Barcelona, Spain 77 Instituto de Fisica Corpuscular, 46980 Paterna, Valencia, Spain 78 Institute of High Energy Physics, 100049 Beijing, China 79 Istituto Nazionale di Fisica Nucleare Sezione di Bologna, 40127 Bologna BO, Italy 80 Istituto Nazionale di Fisica Nucleare Sezione di Catania, I-95123 Catania, Italy 81 Istituto Nazionale di Fisica Nucleare Sezione di Genova, 16146 Genova GE, Italy 82 Istituto Nazionale di Fisica Nucleare Sezione di Lecce, 73100 — Lecce, Italy 83 Istituto Nazionale di Fisica Nucleare Sezione di Milano Bicocca, 3 — I-20126 Milano, Italy 84 Istituto Nazionale di Fisica Nucleare Sezione di Milano, 20133 Milano, Italy 85 Istituto Nazionale di Fisica Nucleare Sezione di Napoli, I-80126 Napoli, Italy 86 Istituto Nazionale di Fisica Nucleare Sezione di Padova, 35131 Padova, Italy 87 Istituto Nazionale di Fisica Nucleare Sezione di Pavia, I-27100 Pavia, Italy 88 Istituto Nazionale di Fisica Nucleare Laboratori Nazionali del Sud, 95123 Catania, Italy 89 Institute for Nuclear Research of the Russian Academy of Sciences, Moscow 117312, Russia 90 Institut de Physique des 2 Infinis de Lyon, 69622 Villeurbanne, France 91 Institute for Research in Fundamental Sciences, Tehran, Iran 92 Instituto Superior Técnico — IST, Universidade de Lisboa, Portugal 93 Idaho State University, Pocatello, ID 83209, U.S.A. 94 Illinois Institute of Technology, Chicago, IL 60616, U.S.A. – 96 – 2020 JINST 15 P12004 95 Imperial College of Science Technology and Medicine, London SW7 2BZ, United Kingdom 96 Indian Institute of Technology Guwahati, Guwahati, 781 039, India 97 Indian Institute of Technology Hyderabad, Hyderabad, 502285, India 98 Indiana University, Bloomington, IN 47405, U.S.A. 99 Universidad Nacional de Ingeniería, Lima 25, Perú 100 University of Iowa, Iowa City, IA 52242, U.S.A. 101 Iowa State University, Ames, Iowa 50011, U.S.A. 102 Iwate University, Morioka, Iwate 020-8551, Japan 103 University of Jammu, Jammu-180006, India 104 Jawaharlal Nehru University, New Delhi 110067, India 105 Jeonbuk National University, Jeonrabuk-do 54896, South Korea 106 University of Jyvaskyla, FI-40014, Finland 107 High Energy Accelerator Research Organization (KEK), Ibaraki, 305-0801, Japan 108 Korea Institute of Science and Technology Information, Daejeon, 34141, South Korea 109 K L University, Vaddeswaram, Andhra Pradesh 522502, India 110 Kansas State University, Manhattan, KS 66506, U.S.A. 111 Kavli Institute for the Physics and Mathematics of the Universe, Kashiwa, Chiba 277-8583, Japan 112 National Institute of Technology, Kure College, Hiroshima, 737-8506, Japan 113 Kyiv National University, 01601 Kyiv, Ukraine 114 Laboratório de Instrumentação e Física Experimental de Partículas, 1649-003 Lisboa and 3004-516 Coimbra, Portugal 115 Laboratoire de l’Accélérateur Linéaire, 91440 Orsay, France 116 Lancaster University, Lancaster LA1 4YB, United Kingdom 117 Lawrence Berkeley National Laboratory, Berkeley, CA 94720, U.S.A. 118 University of Liverpool, L69 7ZE, Liverpool, United Kingdom 119 Los Alamos National Laboratory, Los Alamos, NM 87545, U.S.A. 120 Louisiana State University, Baton Rouge, LA 70803, U.S.A. 121 University of Lucknow, Uttar Pradesh 226007, India 122 Madrid Autonoma University and IFT UAM/CSIC, 28049 Madrid, Spain 123 University of Manchester, Manchester M13 9PL, United Kingdom 124 Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A. 125 University of Michigan, Ann Arbor, MI 48109, U.S.A. 126 Michigan State University, East Lansing, MI 48824, U.S.A. 127 Università del Milano-Bicocca, 20126 Milano, Italy 128 Università degli Studi di Milano, I-20133 Milano, Italy 129 University of Minnesota Duluth, Duluth, MN 55812, U.S.A. 130 University of Minnesota Twin Cities, Minneapolis, MN 55455, U.S.A. 131 University of Mississippi, University, MS 38677 U.S.A. 132 National Centre for Nuclear Research, A. Soltana 7, 05 400 Otwock, Poland 133 University of New Mexico, Albuquerque, NM 87131, U.S.A. 134 H. Niewodniczański Institute of Nuclear Physics, Polish Academy of Sciences, Cracow, Poland 135 Nikhef National Institute of Subatomic Physics, 1098 XG Amsterdam, Netherlands 136 University of North Dakota, Grand Forks, ND 58202-8357, U.S.A. 137 Northern Illinois University, DeKalb, Illinois 60115, U.S.A. 138 Northwestern University, Evanston, Il 60208, U.S.A. 139 University of Notre Dame, Notre Dame, IN 46556, U.S.A. 140 Ohio State University, Columbus, OH 43210, U.S.A. 141 Oregon State University, Corvallis, OR 97331, U.S.A. 142 University of Oxford, Oxford, OX1 3RH, United Kingdom 143 Pacific Northwest National Laboratory, Richland, WA 99352, U.S.A. 144 Universtà degli Studi di Padova, I-35131 Padova, Italy 145 Université de Paris, CNRS, Astroparticule et Cosmologie, F-75006, Paris, France – 97 – 2020 JINST 15 P12004 146 Università degli Studi di Pavia, 27100 Pavia PV, Italy 147 University of Pennsylvania, Philadelphia, PA 19104, U.S.A. 148 Pennsylvania State University, University Park, PA 16802, U.S.A. 149 Physical Research Laboratory, Ahmedabad 380 009, India 150 Università di Pisa, I-56127 Pisa, Italy 151 University of Pittsburgh, Pittsburgh, PA 15260, U.S.A. 152 Pontificia Universidad Católica del Perú, Lima, Perú 153 University of Puerto Rico, Mayaguez 00681, Puerto Rico, U.S.A. 154 Punjab Agricultural University, Ludhiana 141004, India 155 Radboud University, NL-6525 AJ Nijmegen, Netherlands 156 University of Rochester, Rochester, NY 14627, U.S.A. 157 Royal Holloway College London, TW20 0EX, United Kingdom 158 Rutgers University, Piscataway, NJ, 08854, U.S.A. 159 STFC Rutherford Appleton Laboratory, Didcot OX11 0QX, United Kingdom 160 SLAC National Accelerator Laboratory, Menlo Park, CA 94025, U.S.A. 161 Sanford Underground Research Facility, Lead, SD, 57754, U.S.A. 162 Università del Salento, 73100 Lecce, Italy 163 Universidad Sergio Arboleda, 11022 Bogotá, Colombia 164 University of Sheffield, Sheffield S3 7RH, United Kingdom 165 South Dakota School of Mines and Technology, Rapid City, SD 57701, U.S.A. 166 South Dakota State University, Brookings, SD 57007, U.S.A. 167 University of South Carolina, Columbia, SC 29208, U.S.A. 168 Southern Methodist University, Dallas, TX 75275, U.S.A. 169 Stony Brook University, SUNY, Stony Brook, New York 11794, U.S.A. 170 Sun Yat-Sen University, 510275 Guangzhou, China 171 University of Sussex, Brighton, BN1 9RH, United Kingdom 172 Syracuse University, Syracuse, NY 13244, U.S.A. 173 University of Tennessee at Knoxville, TN, 37996, U.S.A. 174 Texas A&M University — Corpus Christi, Corpus Christi, TX 78412, U.S.A. 175 University of Texas at Arlington, Arlington, TX 76019, U.S.A. 176 University of Texas at Austin, Austin, TX 78712, U.S.A. 177 University of Toronto, Toronto, Ontario M5S 1A1, Canada 178 Tufts University, Medford, MA 02155, U.S.A. 179 Universidade Federal de São Paulo, 09913-030, São Paulo, Brazil 180 University College London, London, WC1E 6BT, United Kingdom 181 Valley City State University, Valley City, ND 58072, U.S.A. 182 Variable Energy Cyclotron Centre, 700 064 West Bengal, India 183 Virginia Tech, Blacksburg, VA 24060, U.S.A. 184 University of Warsaw, 00-927 Warsaw, Poland 185 University of Warwick, Coventry CV4 7AL, United Kingdom 186 Wichita State University, Wichita, KS 67260, U.S.A. 187 William and Mary, Williamsburg, VA 23187, U.S.A. 188 University of Wisconsin Madison, Madison, WI 53706, U.S.A. 189 Yale University, New Haven, CT 06520, U.S.A. 190 Yerevan Institute for Theoretical Physics and Modeling, Yerevan 0036, Armenia 191 York University, Toronto M3J 1P3, Canada – 98 –