Measurements of sensor radiation damage in the ATLAS inner detector using leakage currents

Journal of Instrumentation PAPER • OPEN ACCESS Measurements of sensor radiation damage in the ATLAS inner detector using leakage currents To cite this article: The ATLAS collaboration et al 2021 JINST 16 P08025 View the article online for updates and enhancements. You may also like Operation and performance of the ATLAS semiconductor tracker in LHC Run 2 The ATLAS collaboration, Georges Aad, Brad Abbott et al. - The ATLAS Fast TracKer system The ATLAS collaboration, G. Aad, B. Abbott et al. - Performance of the ATLAS RPC detector and Level-1 muon barrel trigger at (s)=13 TeV The ATLAS collaboration, G. Aad, B. Abbott et al. - This content was downloaded from IP address 95.136.125.231 on 04/02/2022 at 20:54 2021 JINST 16 P08025 Published by IOP Publishing for Sissa Medialab Received:June 18, 2021 Accepted:June 23, 2021 Published:August 11, 2021 Measurements of sensor radiation damage in the ATLAS inner detector using leakage currents The ATLAS collaboration E-mail: [email protected] Abstract: Non-ionizing energy loss causes bulk damage to the silicon sensors of the ATLAS pixel and strip detectors. This damage has important implications for data-taking operations, chargedparticle track reconstruction, detector simulations, and physics analysis. This paper presents simulations and measurements of the leakage current in the ATLAS pixel detector and semiconductor tracker as a function of location in the detector and time, using data collected in Run 1 (2010–2012) and Run 2 (2015–2018) of the Large Hadron Collider. The extracted fluence shows a much stronger |𝑧|-dependence in the innermost layers than is seen in simulation. Furthermore, the overall fluence on the second innermost layer is significantly higher than in simulation, with better agreement in layers at higher radii. These measurements are important for validating the simulation models and can be used in part to justify safety factors for future detector designs and interventions. Keywords: Radiation damage to detector materials (solid state); Detector modelling and simulations I (interaction of radiation with matter, interaction of photons with matter, interaction of hadrons with matter, etc) ArXiv ePrint:2106.09287 c 2021 CERN for the benefit of the ATLAS collaboration. 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/10.1088/1748-0221/16/08/P08025 2021 JINST 16 P08025 Contents 1 Introduction 1 2 The ATLAS inner-detector silicon sensors and radiation damage effects 2 3 Simulations 4 3.1 Radiation simulations 4 3.1.1 Fluka 4 3.1.2 Geant45 3.2 Modelling leakage current and annealing 5 4 Measurements 6 4.1 General inputs and corrections 6 4.1.1 Luminosity 6 4.1.2 Temperature corrections 6 4.2 Optimal 𝐸eff study with the silicon sensors on the pixel layers and disks 7 4.3 Innermost pixel layer (IBL) 7 4.3.1 Results 9 4.4 Outer pixel layers and disks 9 4.4.1 Measurement subsystems 9 4.4.2 Precision and systematic uncertainties 11 4.4.3 Results 12 4.5 Strip detector and disks 13 4.5.1 Temperature measurement 14 4.5.2 Time evolution of leakage current 15 4.5.3 Lateral distribution of leakage current 16 5 Leakage current and fluence comparisons 18 5.1 Discussion 20 6 Conclusions and outlook 25 The ATLAS collaboration 30 1 Introduction The ATLAS pixel and strip detectors are the subdetectors in closest proximity to the interaction point and are exposed to an unprecedented amount of radiation. Monitoring and modelling the bulk radiation damage in the pixel and strip detector sensors is crucial for many aspects of the ATLAS experiment [1] including radiation protection, determination of operational conditions, offline data –1– 2021 JINST 16 P08025 analysis, and upgrade design. Understanding the impact of radiation damage will help extend the lifetime and optimal use of the detectors. Sensors designed for the high-luminosity phase of the Large Hadron Collider (LHC) will need to cope with about an order of magnitude more fluence than the present detector and thus investigations with the current detector will provide valuable input to preparations for those data-taking conditions in the near future. One of the best-characterized methods for monitoring silicon radiation damage is based on measuring the sensor leakage current. This paper documents measurements of the sensor leakage current in the ATLAS pixel and strip detectors for the entire first and second runs of the LHC. Models of thermal annealing combined with temperature and luminosity histories in data are used to extract the measured fluence across detector regions. These data are compared with simulations of radiation damage that model particle production, propagation through the sensors, and bulk damage. Thermal annealing models can be used to extract the fluence from data for comparison with simulations or annealing models can be combined with simulations to compare with measured leakage currents directly. Complementary studies related to the detailed modelling of the sensor response to deposited charge from minimum-ionizing particles can be found in ref. [2]. Thispaperisorganizedasfollows. Section2introducestheATLASpixelandstrip detectors and reviews the effects of radiation damage on their sensors. Next, section 3describes how the complex radiation fields inside the ATLAS inner detector can be modelled and how the corresponding damage leads to changes in the sensor leakage current. Technical details of the measurements for each subdetector are presented in section 4. All of the measurements are compared in section 5. The paper ends with the conclusions and outlook in section 6. 2 The ATLAS inner-detector silicon sensors and radiation damage effects The ATLAS inner detector is composed of three subdetectors immersed in a 2 T magnetic field for measuring the trajectories of charged particles. The two innermost subdetectors are based on silicon pixel and strip sensors, respectively. Figure 1illustrates the radial extent of the pixel detector, the strip detector (called the semiconductor tracker or SCT) and transition radiation tracker (TRT). The ATLAS pixel detector [1,3–5] consists of four barrel layers and two identical endcap regions, each with three disk layers. The layers are composed primarily of 𝑛+-in-𝑛planar oxygenated [6,7] silicon sensors. The four barrel layers, labelled Insertable 𝐵-Layer (IBL) [4,5], 𝐵-Layer, Layer-1 and Layer-2, are arranged in concentric cylinders at radii of 33.25, 50.5, 88.5, and 122.5 mm from the beam axis. While most of the pixel detector was installed before the start of LHC Run 1, the innermost barrel layer, IBL, was installed during the shutdown (LS1) between LHC Run 1 and Run 2. Most of the sensors in the pixel detector (IBL) are 250 (200)µm thick with a traditional planar geometry. At high |𝑧|values1the IBL contains 𝑛+-in-𝑝3D sensors [8] that are 230 µm thick. The pixel pitch is 50 ×250 µm2for the IBL and 50 ×400 µm2for the other pixel layers. 1ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the 𝑧-axis coinciding with the axis of the beam pipe. The 𝑥-axis points from the IP towards the centre of the LHC ring, and the 𝑦-axis points upward. Cylindrical coordinates (𝑟,𝜙) are used in the transverse plane, 𝜙being the azimuthal angle around the 𝑧-axis. The pseudorapidity is defined in terms of the polar angle 𝜃as 𝜂=−lntan(𝜃/2). –2– 2021 JINST 16 P08025 Figure 1. A schematic view of the ATLAS inner detector. Radially outward from the collision point are the ATLAS insertable B-layer (IBL), the other layers of the pixel detector, the semiconductor microstrip tracker SCT, and the transition radiation tracker (TRT). A red curved line represents a charged particle traversing the various layers and bending in the 2 T magnetic field. The innermost pixel layer is called the Insertable 𝐵-Layer and was added to the detector between the first and second runs of the LHC. The SCT [1,9–12] consists of four barrel layers and two endcaps, with nine disks each. The four barrel layers are located at 29.9, 37.1, 44.3, and 51.4 cm from the centre of the ATLAS detector and the disks are located at |𝑧|values ranging from 74.9 cm to 272 cm in order to provide tracking coverage up to |𝜂|=2.5. All of the modules are composed of pairs of sensors offset by 40 mrad; this stereo angle provides space-point information. The sensors have a pitch of 80 𝜇m and are 285 𝜇m thick. Each sensor is constructed of high-resistivity 𝑛-type bulk silicon with 𝑝-type implants. Radiation damage in the sensor bulk is primarily caused by displacement of a silicon atom from of its lattice site, resulting in a silicon interstitial site and a leftover vacancy (Frenkel pair) [13,14]. These primary defects build, depending on the recoil energy, cluster defects and point defects in the silicon lattice that produce energy levels in the band gap. When activated and occupied, these states increase the sensor leakage current (𝐼leak), which is proportional to the fluence received: Δ𝐼leak =𝛼Φeq 𝑉, where the effective fluence, Φeq, is the 1 MeV neutron equivalent fluence defined as the number of 1 MeV neutrons applied to a sensor of surface area 1 cm2that cause damage equivalent to that from all particles that traversed the sensor. The volume 𝑉is the depleted volume of the silicon sensor and 𝛼is the current-related damage coefficient. The goal of this measurement is to compare 𝐼leak with predictions of Φeq, either by transforming the leakage current to a fluence or by transforming the fluence into a leakage current via 𝛼. –3– 2021 JINST 16 P08025 3 Simulations 3.1 Radiation simulations The complex radiation fields inside the ATLAS inner detector are simulated by propagating particles from inelastic proton-proton interactions, generated by Pythia 8 [15,16] using the MSTW2008LO parton distribution functions [17] and the A3 set of tuned parameters [18], through the ATLAS detector material using the particle transport code Fluka [19,20] or Geant4 [21]. The particle and energy spectra are then folded with silicon damage factors from the RD50 database [22–26] to compute the 1 MeV neutron equivalent damage. The tabulated weights cover neutrons, protons, charged pions and electrons. For charged kaons the pion weights are used, while for anti-neutrons, anti-protons, baryons and ions the proton weights are used and positrons are treated like electrons. The Fluka and Geant4 programs are composed of many subroutines based on a variety of phenomenological and first-principles models for nuclear and electromagnetic interactions of particles with the ATLAS detector material. The most important difference from the point of view of particle propagation is the detector geometry, which slightly differs between the two programs as is explained in more detail below. 3.1.1 Fluka The Fluka code [27,28] is well-established for studies of hadronic and electromagnetic cascades induced by high-energy particles and is the baseline code for radiation background simulations at CERN and the LHC experiments. Electrons, photons and muons up to 1000 TeV, and hadrons up to 20 TeV, can undergo interactions and be transported. The lower cut-offs in energy for particle transport in the Fluka simulations are: hadrons and muons 100 keV; neutrons 10−5eV (thermal); photons 30 keV; and electrons 100 keV. However, photons and electrons have higher cut-offs in some regions (collimators, forward shielding) to reduce simulation time. Anti-particles, heavy ions and residual nuclei production are also treated by Fluka. A complete description of Fluka’s physics models and capabilities can be found in ref. [29] and references therein. For example, inelastic hadron interactions are described by different physics models depending on the energy. Inelastic hadron-hadron interactions above 5GeV are treated by the Dual Parton Model [30], and below 5GeV by the resonance production and decay model [31]. For hadron-nucleus inelastic interactions above 5GeV, Glauber-Gribov multiple scattering followed by Generalized Intranuclear Cascade is employed. Below 5GeV the pre-equilibrium cascade model Peanut is used [32,33]. All the above hadron interaction models include evaporation and gamma de-excitation of the residual nucleus [34,35]. Light residual nuclei are not evaporated, but are fragmented into a maximum of six bodies according to a Fermi break-up model. A description of the full ATLAS detector geometry and material has evolved in Fluka over the past 20 years, and includes shielding, beam-line and machine components. This has been developed independently of the ATLAS Geant4 geometry described below, and in some cases has been simplified to speed up the simulations, e.g. by using cylinders to describe detector barrel layers. In parts a three-dimensional geometry with 𝜙-asymmetry is implemented when considered important for providing more accurate predictions. The magnetic fields are imported from the ATLAS offline software. –4– 2021 JINST 16 P08025 3.1.2 Geant4 Geant4 [21] is used as the standard simulation toolkit for physics analysis in ATLAS. The implementation of the detector geometry is therefore very detailed, especially for instrumented regions and regions relevant for the signal response, including upstream non-instrumented areas. The advent of new ‘physics lists’ in Geant4 with high-precision transport of neutrons and the possibility to simulate activation of nuclides and their radioactive decay from timescales of nanoseconds to billions of years makes Geant4 an attractive option for simulating the radiation background. Various physics lists are used to determine the models and precision for processes simulated by Geant4. Hadronic physics is governed by the FTFP_BERT list, which includes the Fritiof model [36–39] with a precompound model above 4 GeV and the Bertini intra-nuclear cascade model below 5 GeV. Energy thresholds are implemented via range cuts, whereby if the expected range of a secondary is less than some minimum value, the energy of that secondary particle is deposited at the end of the primary particle’s step and no separate secondary is produced. The range cuts vary from tens of microns to 1 mm depending on the subdetector material. The geometry description for the ATLAS Geant4 simulation uses GeoModel [40], a library of basic geometrical shapes, to describe and construct the detector. This model is the same one used for data analysis in ATLAS and is highly detailed for all detector components, including both active and passive material. For the actual simulation, the geometry is translated entirely from the GeoModel to the Geant4 format. Further details about the ATLAS Geant4 simulation can be found in ref. [41]. 3.2 Modelling leakage current and annealing The formula Δ𝐼leak =𝛼 𝑉 Φeq only applies to instantaneous irradiation, where 𝛼is approximately independent of the damaging particles’ energies and flavours. After some time 𝑡at a temperature 𝑇, the leakage current changes because of defect annealing, so 𝛼=𝛼(𝑡, 𝑇). Different models vary in their treatment of 𝛼. The model used to compare with all silicon layers is the Hamburg Model [13], as implemented in ref. [42], where for 𝑛time intervals, the predicated leakage current is given by 𝐼leak =(Φ/𝐿int) · 𝑛 ∑︁ 𝑖=1 𝑉𝑖·𝐿int,𝑖·"𝛼𝐼exp − 𝑛 ∑︁ 𝑗=𝑖 𝑡𝑗 𝜏(𝑇𝑗)!+𝛼∗ 0−𝛽log 𝑛 ∑︁ 𝑗=𝑖 Θ(𝑇𝑗) ·𝑡𝑗 𝑡0!#,(3.1) where 𝐿int,𝑖is the integrated luminosity, 𝑡𝑖is the duration, and 𝑇𝑖is the temperature in time interval𝑖. The firstsumisover all time periodsand the twosums inside the exponentialandlogarithm functions are over the time between the irradiation in time period 𝑖and the present time. The other symbolsineq. (3.1)are𝑡0=1min,𝑉𝑖=depleted volume (incm3), 𝛼𝐼=(1.23±0.06)×10−17 A/cm, 𝜏follows an Arrhenius equation 𝜏−1=(1.2+5.3 −1.0) ×1013 s−1×e(−1.11±0.05)eV/𝑘B𝑇, where 𝑘Bis the Boltzmann constant, 𝛼∗ 0=7.07 ×10−17 A/cm, and 𝛽=(3.29 ±0.18) ×10−18 A/cm.2Note that the 𝛼and 𝛽parameters are degenerate with the silicon damage factors, which are not well-known (see 2A small temperature dependence has been observed in the value of 𝛽[13]. For this analysis, the reported value at 21 ◦C is used as it is closest to the operational temperature range of the detector. –5– 2021 JINST 16 P08025 section 5.1). The temperature scaling function Θ(𝑇)is defined by3 Θ(𝑇)=exp −𝐸∗ 𝐼 𝑘B1 𝑇−1 𝑇ref ,(3.2) where 𝐸∗ 𝐼=(1.30 ±0.14)eV and 𝑇ref is a reference temperature. The value of the fluence rate, Φ/𝐿int, in data is estimated by performing a fit using eq. (3.1) and letting only this parameter float. The value Φ/𝐿int can be predicted from the Pythia+Fluka or Geant4 simulations. When this value is used, the resulting predictions are called ‘unscaled’. It is useful to fit a scale factor to this value instead of fitting Φ/𝐿int without any prior. The simulation normalized with a scale factor obtained from a fit to data are defined as “scaled” in the following. 4 Measurements 4.1 General inputs and corrections 4.1.1 Luminosity Luminosity data are collected approximately once per minute. The luminosity scale is determined by a set of dedicated bunch-by-bunch luminosity detectors [44] that are calibrated using the van der Meermethod [45]. The absolute luminosity usedin this studyaccountsfor luminosities accumulated during times when the ATLAS detector is operating and also when it is not operating, because all particle fluence received by the silicon sensors will impact the leakage current. Luminosities used for this study surpass the quantities reported as the official ATLAS integrated luminosity usable for physics. The uncertainty in the luminosity does not contribute to uncertainty in the leakage current data but is included in the fluence measurement. The uncertainty in the combined 2015–2018 integrated luminosity is 1.7% [46], obtained using the LUCID-2 detector [47] for the primary luminosity measurements. A series of quality criteria are applied to the leakage current data considered in subsequent sections. Leakage current data are excluded for modules during periods when their bias voltage was not applied. Data collected within one minute of high-voltage turn-on are also excluded. Analysis of the leakage current data is restricted to times when the LHC has declared the proton beams to be stable. Luminosity recorded outside of these periods is included. 4.1.2 Temperature corrections The leakage current depends on the temperature of the sensor [48]. The following equation converts the leakage current of a sensor measured at temperature 𝑇to that at a reference temperature 𝑇R: 𝐼leak(𝑇R)=𝐼leak(𝑇)𝑇R 𝑇2 exp −𝐸eff 2𝑘B1 𝑇R−1 𝑇, where 𝐸eff is the effective silicon band-gap energy after irradiation, also called the activation energy, and 𝑘Bis the Boltzmann constant. A value of 1.21 eV was used for 𝐸eff for all sensors studied in 3This is not the only way to incorporate time-dependence in the thermal history. Another proposal is to sum the inverse temperatures [43]. Such a method has been compared with eq. (3.2) and results in similar predictions for the leakage current at the present fluence levels and annealing times. –6– 2021 JINST 16 P08025 ref. [49]. This choice provides consistency when comparing results from different subdetectors. A study using this value, performed with sensors that have been subjected to different radiation conditions, is presented in the next section. 4.2 Optimal 𝐸eff study with the silicon sensors on the pixel layers and disks Dedicated temperature scans were used to measure 𝐸eff in the pixel detector. The upper panel of the left plot in figure 2shows the measured temperature as a function of time during the scan for a representative module in the IBL. The bottom panel shows the measured leakage current and corrections to a reference temperature of 0 ◦C with several values of 𝐸eff. The optimal value of 𝐸eff in the temperature correction equation is the value that results in corrected leakage current data that best fit a line of zero slope. The best fit is determined using a minimum-𝜒2figure of merit. The optimal 𝐸eff is 1.26 eV for this particular module and corresponds to an integrated luminosity of 161fb−1delivered to the IBL. This procedure is repeated for all modules in the pixel detector and the extracted values of 𝐸eff are shown in the right plot of figure 2. The 𝐸eff values for the IBL modules were extracted using temperature scan data in Feb. 2018 (May 2019) corresponding to 95 (161)fb−1. The 𝐸eff values for all other layers correspond to the temperature scan data in May 2019 and 191 fb−1accumulated in Run 1 and Run 2. The bin ranges of the outer layers are determined by the paired-module powering scheme of module power supplies. The measured 𝐸eff is found to vary by layer, with a small radial dependence. The Layer-2 values are most consistent with 1.21 eV, with slightly lower values observed for Layer-1 and the 𝐵-Layer. Higher values of 𝐸eff are observed for the IBL. The data are consistent with a small (O(1%)) increase in 𝐸eff between 2018 and 2019 for the IBL. It is possible that 𝐸eff depends on the composition of irradiating particles, the thermal history, or the sensor doping properties. Further investigations are left to future studies. The largest uncertainty in the 𝐸eff measurement is due to the offset between the measured and true sensor temperatures. This offset is not well-constrained; a ±2◦C uncertainty is used for illustration purposes, acting as a conservative estimate to show the effects of such a shift on the final measurements. In comparison, the statistical uncertainty is found to be negligible. In principle, one could simultaneously extract the temperature offset and 𝐸eff from the fit. The Δ𝜒2 landscape is presented in figure 3and shows that there is a near degeneracy between the extracted temperature offset and 𝐸eff. Therefore, additional studies of the temperature offset using simulations or laboratory tests will be required to determine 𝐸eff with more precision. 4.3 Innermost pixel layer (IBL) Each sensor is equipped with a negative temperature coefficient (NTC) thermistor for measuring the temperature. On the FEI4 readout chips [50] bonded to the sensors, there is a 10-bit analog-todigital converter associated with an 8-to-1 analogue multiplexer that can be used to select and read out the temperature, power supply voltages, voltage references, detector leakage current, and other detector control system analogue voltages. The leakage currents were measured at the nominal operational temperature and bias voltage settings. In particular, the temperature started at -2 ◦C and the high voltage for the planar (3D) sensors started at −80 V (−20 V) at the beginning of 2015 and was subsequently increased to ensure full depletion. The high voltage was increased to −150 V –7– 2021 JINST 16 P08025 Figure 7. Ratios of the 𝐵-Layer and Layer-1 leakage current data to Layer-2 leakage current for the LHC Run 2 period of ATLAS operation. The bands include uncertainties on the measurement, as described in section 4.4.2 4.5.1 Temperature measurement Each barrel module consists of four rectangular silicon-strip sensors. Two sensors on each side are daisy-chained together. Two sides of identical pairs are glued back-to-back with a 380 𝜇m thick anisotropic thermal pyrolytic graphite (TPG) substrate in between. The endcap region has four module types and five different trapezoid-shaped sensors. Modules in the Outer and Middle rings consist of two daisy-chained sensors on each side, whereas those in the Inner ring have one sensor per side. A TPG spine sandwiched between two sensor sides conducts heat away from the sensors to the cooling block contacts. The modules in the Outer and Middle are supported and cooled by their contacts with two cooling blocks; the main block is shared between the hybrid and the spine, while the far block cools only the spine. Inner modules are cooled only through the main block. SCT modules are cooled using the inner detector’s evaporative C3F8cooling system [54]. Each loop cools 48 (up to 33) barrel (endcap) modules. A total of 44 and 72 cooling loops are operated simultaneously for the barrel and endcap regions, respectively. The cooling-pipe temperatures were set to about −12 ◦C, −7◦C, −13 ◦C and −10 ◦C in B3–B5, B6, EC-A and EC-C, respectively. The cooling temperatures for B6 were set higher because of a failure in resistive pad heaters on the thermal enclosure cylinders at the SCT-TRT interface. There is a 3 ◦C difference in the cooling temperature settings of EC-A and EC-C due in part to different assembly sites. The initial cooling temperature settings were kept from 2009 to 2018 except for 6 months during 2015 to avoid condensation in the SCT volumes. The sensor temperature 𝑇sensor of each module is deduced from a hybrid-board temperature 𝑇hybrid measured by NTC thermistors mounted on the hybrid circuit board. A temperature offset between 𝑇hybrid and 𝑇sensor strongly depends on the mechanical and thermal structure. It also differs – 14 – 2021 JINST 16 P08025 module-by-module due to slightly different thermal resistances of the hybrid-circuit, sensor and cooling pipe. The temperature offset is determined by measuring the hybrid temperature and the leakage current of the sensors with and without power applied to the hybrid low voltage (LV). The hybrid temperature and the leakage current data are collected at several cooling temperature settings. The temperature differences are extracted by an interpolation of leakage currents vs 𝑇hybrid curves. The offsets are similar to predictions from the thermal finite-element method. The individual differences are 3–5 ◦C and 12–20 ◦C in barrel and endcap modules, respectively. The offsets of the B6 modules are not measured because the TRT would become too cold at the SCT-TRT interface if the LV power were not supplied to the B6 modules. During long shutdowns in winters and LS1, as well as occasional power-cut cases, 𝑇hybrid data are not available. In most cases, however, the temperature monitors of the evaporative cooling system were active. Since there was no heat generation in the SCT volume, the temperature of the closest cooling pipe could be used as an estimate of 𝑇sensor, which occasionally reached as high as 20 ◦C. The precision of the 𝑇hybrid measurement is determined by using temperature data (when all HVs and LVs are off) from the two thermistors mounted on each side of a barrel module. These readings agree well with a root-mean-square (RMS) uncertainty of 0.27 ◦C. A major uncertainty in 𝑇hybrid comes from the smallest digitization unit of 0.33 ◦C used in the conversion from thermistor resistance to temperature. However, module-by-module fluctuations in the leakage currents cause larger spreads and thus such digitization effects smear out once the mean of >30 modules belonging to the same module group is taken. 4.5.2 Time evolution of leakage current All voltages and currents of the HV power supplies, as well as 𝑇hybrid, were continuously monitored and stored in a database called the Detector Control System Data Viewer [55]. For the leakage current of each module, typically 150 data points of leakage current are recorded every hour because of the presence of small ripples of about 0.2%. During a physics run lasting several hours, as the instantaneous luminosity goes down, the HV current drops by 0.2–2% (values in late 2018, none in Run 1) due to sensor self-heating. A simple time-weighted average during each physics run is taken for the present study. Another way to get the leakage current data is to read the leakage current mean values recorded in the gain calibration runs. In-beam leakage current averages are consistent with the mean values from nearby calibration run values within 1%. It should be noted that the leakage current depends on the HV applied to the sensor. Well above the full depletion voltage, the leakage current increases by several percent per 100 V. In addition, due to the filter resistance of about 12 kΩin the HV supply lines, the true voltage on the sensor could be 20 V less in the worst case for B3 modules in late 2018, introducing up to 1% additional uncertainty in the leakage current measurement. Figure 8shows the time evolution of the leakage current during the Run 2 period including winter shutdowns for four representative module groups, two each from barrel and endcap regions. The top plots show the histories of the estimated sensor temperatures 𝑇sensor, which were around −1◦C, +5◦C and −7◦C for B3, B6 and both EC-A and EC-C, respectively, during the running periods, and were >15 ◦C during LS1 and the winter shutdowns. In the 2015 beam time, all modules – 15 – 2021 JINST 16 P08025 except those in B6 were set warmer to avoid a condensation risk in the SCT volume. The HV is adjusted to always be above the prediction for full depletion. The second set of plots from the top in figure 8display normalized leakage currents, and the third and fourth sets of plots display ratios of data to predictions from the Hamburg Model and the Sheffield Model5[56,57]. Uncertainties from the model predictions are shown with bands which are calculated by varying each parameter of the model by 1𝜎and adding the resulting changes in quadrature. Uncertainties in the temperature measurements (1 ◦C) and delivered luminosities (3.7%) are also included. The leakage current anneals out by 20–30% during each winter shutdown, as evident from the drop in the current during periods of no beam. The Sheffield Model systematically predicts 15% more leakage current than the Hamburg Model. For most of the cases, the ratios seldom change by more than 5% during a given year, which is good evidence for the leakage current being proportional to irradiation with good-enough short annealing terms. Any year-by-year dependencies in the ratios may be due to the influence of insufficiently accurate estimates of 𝑇sensor during shutdowns or model limitations or both on annealing effects. 4.5.3 Lateral distribution of leakage current Figures 9(a) and 9(b) show leakage currents for all barrel and endcap modules, respectively, as of November 2018 with the applied HV set to 150 V. In these plots, modules with the same 𝑟and 𝑧 locations but different azimuthal angles are bundled side by side. Modules of EC-A and EC-C are coloured differently, as are modules built with sensors from different manufacturers. Permanently disabled modules (42 in total out of about 2000) are not displayed in the plot. It can be seen that almost all modules in the same group have quite similar leakage currents with a spread of about 3%. Despite the difference of 2–3 ◦C between the EC-A and EC-C cooling temperatures, the leakage currents agree well once they are normalized to 0 ◦C. Additional differences between sensors across the detector did not result in appreciable variations among the leakage currents. The majority of the modules are constructed from silicon wafers with crystal lattice orientation (Miller indices) h111iwhile a small number of modules in the barrel use wafers with h100ilattice orientation. The barrel sensors and 75% of the endcap sensors were supplied by Hamamatsu Photonics (HPK)6while the remaining endcap sensors were supplied by CiS.7Sensors supplied by the two manufacturers meet the same performance specifications, but differ in design and processing details [11]. The CiS sensors for EC inner modules were oxygen-enriched. No appreciable differences in leakage current were observed among sensors from different manufacturers (HPK vs CiS), or different crystal orientations (h111ivs h100i) or standard/oxygen-enriched silicon materials. One sees a clear and systematic trend of higher leakage currents in higher rapidity regions at all radii covered by the SCT. In the barrel layers, the normalized leakage currents in near-centre modules are about 3% smaller than in edge modules at |𝑧|=68 cm. 5This is an alternative to the Hamburg Model that has been developed for the ATLAS strip detector. While the Hamburg model uses an exponential combined with a logarithmic function, the Sheffield model is based on a sum of five exponentials. The parameters of these exponentials were tuned to SCT-like modules prior to the start of the LHC. While it has not yet been compared with pixel data from any experiment, this will be important for the future (see section 5.1). 6Hamamatsu Photonics Co. Ltd.,1126-1 Ichino-cho, Hamamastu, Shizuoka 431-3196, Japan. 7CiS Institut für Mikrosensorik gGmbH, Konrad-Zuse-Strasse 14, 99099 Erfurt, Germany. – 16 – 2021 JINST 16 P08025 10−0 10 20 C] o Ts [ 0 100 200 300 400 C o ] at 0 3 A/cmµLeakage current [ Fluka transport⊗Hamburg model =1 index ηBarrel 3 ( r=29.9cm, z= 6.0cm ) 0.5 1.0 1.5 data/model Hamburg model Year 0.5 1.0 1.5 data/model Sheffield model 2014 2015 2016 2017 2018 ATLAS 10−0 10 20 C] o Ts [ 0 100 200 300 400 C o ] at 0 3 A/cmµLeakage current [ Fluka transport⊗Hamburg model =6 index ηBarrel 6 ( r=51.4cm, z=67.9cm ) 0.5 1.0 1.5 data/model Hamburg model Year 0.5 1.0 1.5 data/model Sheffield model 2014 2015 2016 2017 2018 ATLAS ATLAS 10−0 10 20 C] o Ts [ 0 100 200 300 400 C o ] at 0 3 A/cmµLeakage current [ Fluka transport⊗Hamburg model Endcap-C Outer disc-9 ( r=49.9cm, z=-272.0cm ) 0.5 1.0 1.5 data/model Hamburg model Year 0.5 1.0 1.5 data/model Sheffield model 2014 2015 2016 2017 2018 ATLAS ATLAS 10−0 10 20 C] o Ts [ 0 100 200 300 400 C o ] at 0 3 A/cmµLeakage current [ Fluka transport⊗Hamburg model Endcap-A Inner disc-5 ( r=30.6cm, z=140.0cm ) 0.5 1.0 1.5 data/model Hamburg model Year 0.5 1.0 1.5 data/model Sheffield model 2014 2015 2016 2017 2018 ATLAS ATLAS Figure 8. Typical time evolution of normalized leakage currents in the four SCT modules groups, one each from B3, B6, EC-C and EC-A regions. Top plots show histories of deduced sensor temperatures, while the second plots are leakage current data and Hamburg Model predictions. Each data point represents an average of time-weighted leakage current means over 30–56 modules in a single physics or calibration run. The two bottom plots show the ratios of data to predictions from the Hamburg and Sheffield models, using the same conversion factors as in Fluka transport simulations. Coloured bands show 1𝜎uncertainties of the model predictions. This is in contrast to the observation at the end of Run 1 when a slight excess was seen in the central B3 layer but a flat behavior was seen in other barrel layers [9]. The observed gross trends are reproduced fairly well by the Hamburg Model times the conversion factors from Fluka or Geant transport simulations. In general, leakage current predictions are systematically 10–20% higher in the Geant case. The ratios in modules near the centre of B3 are 10% higher than in those at the edges, but the difference is less in the B6 layer, indicating an additional fluence component close to the interaction point. In the endcap regions, however, the data-to-model ratios are fairly constant for all disks in the range |𝑧|=85 to 270 cm although their – 17 – 2021 JINST 16 P08025 leakage currents being different by up to 50%. 0 100 200 300 400 ] 3 A/cmµC [°Leakage current at 0 ATLAS °90 °180°270 °90 °180°270 °90 °180°270 °90 °180°270 °90 °180°270 °90 °180°270 °90 °180°270 °90 °180°270 °90 °180°270 °90 °180°270 °90 °180°270 °90 °180°270 Hamburg model : Data⊗Transport Geant4 FLUKA B3 B4 B5 B6 Barrel 3 (r=29.9cm) Barrel 4 (r=37.1cm) Barrel 5 (r=44.3cm) Barrel 6 (r=51.4cm) <111> <100> Date : 2018-11-01 , HV=150V 0.8 1.0 1.2 1.4 <data>/model Hamburg model⊗FLUKA transport B3 B4 B5 B6 0.8 1.0 1.2 1.4 <data>/model Hamburg model⊗Geant4 transport B3 B4 B5 B6 -67.9 -56.0 -43.2 -31.0 -18.7 -6.3 6.0 18.4 30.7 43.0 55.0 67.9 Z [cm] 0 100 200 300 400 ] 3 A/cmµC [°Leakage current at 0 ATLASATLAS °90 °180 °270 °90 °180 °270 °90 °180 °270 °90 °180 °270 °90 °180 °270 °90 °180 °270 °90 °180 °270 °90 °180 °270 °90 °180 °270 ATLAS Hamburg model⊗FLUKA Geant4Inner (r=27.5-33.8 cm) Middle (r=33.8-43.9 cm) Outer (r=43.9-56.0 cm) HPK, C-side HPK, A-side CiS, C-side CiS, A-side Date : 2018-11-01 , HV=150V 0.8 1.0 1.2 1.4 <data>/model Hamburg model⊗FLUKA transport C-side A-side 0.8 1.0 1.2 1.4 <data>/model Hamburg model⊗Geant4 transport C-side A-side 85.4± 93.4±109.2±130.0±140.0±177.1±211.5±250.5±272.0± Z [cm] (a) (b) Figure 9. Leakage current measured at HV set to 150 V normalized to 0 ◦C per unit volume for all (a) barrel modules and (b) endcap modules as of November 1, 2018. At the same 𝑟and 𝑧location, modules with different 𝜙indices are arranged horizontally from left (𝜙=0◦) to right (𝜙=360◦). Endcap side-A and side-C as well as sensor manufacturers (Hamamatsu (HPK) and CiS) are plotted in different colours. Horizontal solid/dot-dash bars indicate model predictions of the Hamburg Model using the conversion factors by Fluka (solid) / Geant (dot-dash) transport simulations. In the two ratio plots, mean and RMS values via Gaussian fits of modules belonging to the same group are plotted. The model uncertainties are shown by blue bands. 5 Leakage current and fluence comparisons This section incorporates measurements and simulations for all of the silicon-based inner-detector subsystems described in the previous sections. For leakage current comparisons, the measurements are presented from the end of Run 2. The simulations are combined with the thermal and luminosity history of the various sensors to transform fluence predictions to leakage current predictions for all detector regions. For fluence comparisons, the annealing models are used to fit a scale factor that normalizes the fluence in the simulations per detector region. The nominal predicted fluence combined with the scale factor gives the measured fluence rate, Φ/𝐿int (from eq. 3.1). Figure 10 presents a comparison of the measured leakage current and fluence rate for both the pixel and strip detectors. The leakage current is the value at the end of Run 2 while the fluence rate is independent of time.8The fluence rate prediction agrees well with the IBL data at |𝑧|=0 8This is exactly true for the IBL, but for the other layers, a minor model dependence is introduced by using the relative total cross sections between collision energies (lower in Run 1 than in Run 2) to be able to measure a single value. About 85% of the data come from Run 2, which further reduces the impact of this correction. – 18 – 2021 JINST 16 P08025 and with all the SCT data. In contrast, there is a much stronger |𝑧|-dependence observed in the IBL data than is predicted (numerical values are presented in table 1) and the overall fluence is significantly higher than predicted for the outer pixel layers. Near the centre of the detector, the measured fluence is 30–50% higher than predicted by the simulation. For Layer-1 and Layer-2, the difference is 30–40%. For the IBL, the measured fluence at |𝑧|=30 cm is about 50% of the value at |𝑧|=0. These data are presented as a function of 𝜂instead of 𝑧in figure 11. The trends are exactly the same as in figure 10, but now the |𝜂|<2.5acceptance of the silicon tracking detector is clear. The fluence on the inner layers is mostly determined by the primary charged-pion flux, which is relatively constant as a function of 𝜂. In contrast, a significant fraction of the fluence in the outer layers of the SCT is due to neutrons that are produced by interactions with material in the dense regions of the ATLAS calorimeters. The data are presented in a third way in figure 12, demonstrating the radial dependence of the measured leakage current and fluence. The fluence drops off approximately as the square of the inverse radius, with deviations resulting from particles produced through interactions with the detector. Beyond the pixel detector, the Fluka and Geant4 simulations bracket the measured values. The ratio of the simulated values to the leakage current data, as shown in figure 12, is presented in table 2. The values in this table provide concrete input for further studies of the radiation environment at the LHC. Table 1. IBL scale factors as a function of 𝑧, depicted in figure 10. The measurements are consistent between +𝑧and −𝑧and the predictions are symmetric by construction, so the values are presented in bins of |𝑧|. 𝑧Bin Mean SF 32 cm >|𝑧|>24 cm 0.56 ±0.06 24 cm >|𝑧|>16 cm 0.77 ±0.08 16 cm >|𝑧|>8cm 0.84 ±0.09 8cm >|𝑧|>0cm 0.97 ±0.10 Table 2. Mean, minimum, and maximum simulation-to-data ratios (scale factors, SF) for each barrel layer in the inner detector, as depicted in figure 12. The average uncertainty in the ratio is in the rightmost column. Detector Layer 𝑟[cm] Mean SF Min. SF Max. SF SF uncert. Pixel IBL 3.30 0.78 0.56 0.97 0.08 𝐵-Layer 5.10 1.28 1.11 1.47 0.15 Layer-1 8.90 1.31 1.19 1.44 0.15 Layer-2 12.30 1.39 1.32 1.46 0.16 SCT Barrel 3 29.90 1.13 1.11 1.17 0.11 Barrel 4 37.10 1.09 1.05 1.15 0.11 Barrel 5 44.30 1.06 1.01 1.13 0.10 Barrel 6 51.40 1.03 0.98 1.09 0.11 – 19 – 2021 JINST 16 P08025 5.1 Discussion The leakage current is a powerful probe of bulk damage in silicon caused by irradiation, and the resultsintheprevioussectionindicateareaswheresimulationsprovideanexcellentdescriptionofthe data as well as other areas where there are significant deviations from observations. The purpose of this section is to examine possible sources of the differences between data and simulation, including systematic effects for which there is currently no concrete uncertainty model. Differences could be due to a variety of sources, affecting both the data measurements and simulation predictions: Method biases in the measurement. Intheory, thecurrent shouldrise, plateau, andthenrise again as the high voltage is increased from zero up through breakdown. In practice, the current increases past full depletion for irradiated sensors so there is no unique high voltage at which to determine the leakage current. Changes in the current past full depletion are typically small (O(10%)) but not negligible for the current level of measurement precision. Furthermore, as remarked earlier, the current depends strongly on temperature. This presents a challenge because the temperature of the sensors is often not known precisely. Temperature measurements are also complicated by bulk heat generation. This affects both the leakage current measurements and their interpretation because the measured temperature is used as an input for leakage current normalization and leakage current predictions. A coupled complication is that the effective band-gap energy 𝐸eff may not be constant, as noted in section 4.2. Additional measurements with other observables such as the Lorentz angle, depletion voltage, charge collection efficiency, etc. may add valuable information to confirm the trends observed with the leakage current. Physics modelling for the outgoing particle spectra. The input to radiation damage model predictions is the type and energy of particles produced by the primary proton-proton collisions. The physics of soft quantum chromodynamics governs the majority of particles produced and these dynamics are not well understood. A variety of models exist, as do measurements of the total inelastic cross section [58–60] and minimum-bias / underlying-event spectra [61–63]. Systematic studies of model uncertainties and data/simulation differences may provide insight into deviations observed in the leakage current measurement. Transport models. Most of the significant data/prediction differences are common to both Fluka and Geant4. An important difference between the two transport models is that Geant4 includes a more detailed description of the ATLAS detector geometry. Improvements in the description of the inner-detector material may mitigate discrepancies, but the material is known precisely from studies of secondary interactions [64]. Damage factors. The largest single (largely unknown) uncertainty comes from the damage factors. These factors have been tabulated by the RD50 Collaboration [22–26] and are based on a combination of measurements and simulations. A key challenge is that there are not many beam facilities with monochromatic beams of hadrons in the relevant energy range. Furthermore, the damage from neutrons changes rapidly near 1 MeV so there is a significant uncertainty when converting from the damage from pions to the damage from neutrons. Improving the precision of these factors is challenging, but a first important step would be to estimate their uncertainty. – 20 – 2021 JINST 16 P08025 Annealing models. The Hamburg Model is the community standard for leakage current modelling. Aside from the overall luminosity-to-fluence conversion, it has achieved excellent precision over the full lifetime of the LHC. However, there may be early indications from the pixel data that there are systematic differences between the model and data and these may grow to become significant in the future. Alternative models are available, such as the Sheffield Model [56,57]. Comparisons with this model as well as updating/tuning the Hamburg Model (including how to model periods of non-constant temperature) may be necessary for describing the leakage currents when including Run 3 and the high-luminosity phase of the LHC. Despite these challenges, the leakage current is an important tool that will continue to improve in its descriptive and predictive power with additional studies on various fronts described above. – 21 – 2021 JINST 16 P08025 300−200−100−0 100 200 300 z [cm] 1− 10 1 10 ] 3 C [mA/cm o Leakage Current at 0 Simulation 4 + Hamburg ModelEANT8 (A3) + GYTHIAP + Hamburg ModelLUKA8 (A3) + FYTHIAP Data Pixel Detector IBL (3.3 cm) -Layer (5.1 cm)B Layer-1 (8.9 cm) Layer-2 (12.3 cm) Disks (8.88 - 14.96 cm) SCT Detector Barrel 3 (29.9 cm) Barrel 6 (51.4 cm) Disks, Inner Rings (27.50 - 33.76 cm) Disks, Outer Rings (43.88 - 56.00 cm) ATLAS = 7, 8, and 13 TeVs End of Run 2 300−200−100−0 100 200 300 z [cm] 1− 10 1 10 ] -1 /fb 2 /cm 12 rate [10 Si eqn Φ Simulation 4EANT8 (A3) + GYTHIAP LUKA8 (A3) + FYTHIAP Data (Leakage Current + Hamburg Model) Pixel Detector IBL (3.3 cm) -Layer (5.1 cm)B Layer-1 (8.9 cm) Layer-2 (12.3 cm) Disks (8.88 - 14.96 cm) SCT Detector Barrel 3 (29.9 cm) Barrel 6 (51.4 cm) Disks, Inner Rings (27.50 - 33.76 cm) Disks, Outer Rings (43.88 - 56.00 cm) ATLAS = 7, 8, and 13 TeVs Figure 10. The leakage current at the end of Run 2 (left) and the fluence rate (right) as a function of 𝑧for the silicon-based parts of the ATLAS inner detector. The predicted values are symmetric in 𝑧by construction. Distances given in parentheses after layer names correspond to the radial positions of the sensors relative to the geometric centre of ATLAS. For the IBL, the error bars are dominated by the residual dependence of the leakage current on the high voltage past full depletion; for the outer layers of the pixel detector, the uncertainty is dominated by a power supply uncertainty and uncertainties in the temperature and luminosity; for the SCT, the uncertainty is due to the sensor temperature, the luminosity, and the sensor thickness (for fluence) and the RMS spread across modules (leakage current). Uncertainties in the silicon damage factors (relevant for the simulation and the Hamburg Model) are not included. – 22 – 2021 JINST 16 P08025 3−2−1−0 1 2 3 Pseudorapidity 1− 10 1 10 ] 3 C [mA/cm o Leakage Current at 0 Simulation 4 + Hamburg ModelEANT8 (A3) + GYTHIAP + Hamburg ModelLUKA8 (A3) + FYTHIAP Data Pixel Detector IBL (3.3 cm) -Layer (5.1 cm)B Layer-1 (8.9 cm) Layer-2 (12.3 cm) Disks (8.88 - 14.96 cm) SCT Detector Barrel 3 (29.9 cm) Barrel 6 (51.4 cm) Disks, Inner Rings (27.50 - 33.76 cm) Disks, Outer Rings (43.88 - 56.00 cm) ATLAS = 7, 8, and 13 TeVs End of Run 2 3−2−1−0 1 2 3 Pseudorapidity 1− 10 1 10 ] -1 /fb 2 /cm 12 rate [10 Si eqn Φ Simulation 4EANT8 (A3) + GYTHIAP LUKA8 (A3) + FYTHIAP Data (Leakage Current + Hamburg Model) Pixel Detector IBL (3.3 cm) -Layer (5.1 cm)B Layer-1 (8.9 cm) Layer-2 (12.3 cm) Disks (8.88 - 14.96 cm) SCT Detector Barrel 3 (29.9 cm) Barrel 6 (51.4 cm) Disks, Inner Rings (27.50 - 33.76 cm) Disks, Outer Rings (43.88 - 56.00 cm) ATLAS = 7, 8, and 13 TeVs End of Run 2 Figure 11. The leakage current at the end of Run 2 (left) and the fluence rate (right) as a function of 𝜂for the silicon-based parts of the ATLAS inner detector. The predicted values are symmetric in 𝜂by construction. Distances given in parentheses after layer names correspond to the radial positions of the sensors relative to the geometric centre of ATLAS. For the IBL, the error bars are dominated by the residual dependence of the leakage current on the high voltage past full depletion; for the outer layers of the pixel detector, the uncertainty is dominated by a power supply uncertainty and uncertainties in the temperature and luminosity; for the SCT, the uncertainty is due to the sensor temperature, the luminosity, and the sensor thickness (for fluence) and the RMS spread across modules (leakage current). Uncertainties in the silicon damage factors (relevant for the simulation and the Hamburg Model) are not included. – 23 – 2021 JINST 16 P08025 The ATLAS collaboration G. Aad101,B. Abbott127,D.C. Abbott102,A. Abed Abud36,K. Abeling53,D.K. Abhayasinghe93, S.H. Abidi29,O.S. AbouZeid40, N.L. Abraham155,H. Abramowicz160,H. Abreu159,Y. Abulaiti6, A.C. Abusleme Hoffman145a,B.S. Acharya66a,66b,o,B. Achkar53,L. Adam99,C. Adam Bourdarios5, L. Adamczyk83a,L. Adamek165,J. Adelman120,A. Adiguzel12c,ad,S. Adorni54,T. Adye142, A.A. Affolder144,Y. Afik159,C. Agapopoulou64,M.N. Agaras38,A. Aggarwal118,C. Agheorghiesei27c, J.A. Aguilar-Saavedra138f,138a,ac,A. Ahmad36,F. Ahmadov79,W.S. Ahmed103,X. Ai46,G. Aielli73a,73b, S. Akatsuka85,M. Akbiyik99,T.P.A. Åkesson96,E. Akilli54,A.V. Akimov110,K. Al Khoury39, G.L. Alberghi23b,23a,J. Albert174,M.J. Alconada Verzini160,S. Alderweireldt36,M. Aleksa36, I.N. Aleksandrov79,C. Alexa27b,T. Alexopoulos10,A. Alfonsi119,F. Alfonsi23b,23a,M. Alhroob127, B. Ali140,S. Ali157,M. Aliev164,G. Alimonti68a,C. Allaire36,B.M.M. Allbrooke155,P.P. Allport21, A. Aloisio69a,69b,F. Alonso88,C. Alpigiani147, E. Alunno Camelia73a,73b,M. Alvarez Estevez98, M.G. Alviggi69a,69b,Y. Amaral Coutinho80b,A. Ambler103,L. Ambroz133, C. Amelung36,D. Amidei105, S.P. Amor Dos Santos138a,S. Amoroso46, C.S. Amrouche54,C. Anastopoulos148,N. Andari143, T. Andeen11,J.K. Anders20,S.Y. Andrean45a,45b,A. Andreazza68a,68b, V. Andrei61a, C.R. Anelli174, S. Angelidakis9,A. Angerami39,A.V. Anisenkov121b,121a,A. Annovi71a,C. Antel54,M.T. Anthony148, E. Antipov128,M. Antonelli51,D.J.A. Antrim18,F. Anulli72a,M. Aoki81,J.A. Aparisi Pozo172, M.A. Aparo155,L. Aperio Bella46,N. Aranzabal36,V. Araujo Ferraz80a,C. Arcangeletti51,A.T.H. Arce49, J-F. Arguin109,S. Argyropoulos52,J.-H. Arling46,A.J. Armbruster36,A. Armstrong169,O. Arnaez165, H. Arnold36, Z.P. Arrubarrena Tame113,G. Artoni133,H. Asada116,K. Asai125,S. Asai162,N.A. Asbah59, E.M. Asimakopoulou170,L. Asquith155,J. Assahsah35e, K. Assamagan29,R. Astalos28a,R.J. Atkin33a, M. Atkinson171,N.B. Atlay19, H. Atmani64, P.A. Atmasiddha105,K. Augsten140,V.A. Austrup180, G. Avolio36,M.K. Ayoub15c,G. Azuelos109,ak,D. Babal28a,H. Bachacou143,K. Bachas161, F. Backman45a,45b,P. Bagnaia72a,72b, H. Bahrasemani151,A.J. Bailey172,V.R. Bailey171,J.T. Baines142, C. Bakalis10,O.K. Baker181,P.J. Bakker119,E. Bakos16,D. Bakshi Gupta8,S. Balaji156, R. Balasubramanian119,E.M. Baldin121b,121a,P. Balek178,F. Balli143,W.K. Balunas133,J. Balz99, E. Banas84,M. Bandieramonte137,A. Bandyopadhyay19,L. Barak160,W.M. Barbe38,E.L. Barberio104, D. Barberis55b,55a,M. Barbero101, G. Barbour94,K.N. Barends33a,T. Barillari114,M-S. Barisits36, J. Barkeloo130,T. Barklow152,B.M. Barnett142,R.M. Barnett18,Z. Barnovska-Blenessy60a, A. Baroncelli60a,G. Barone29,A.J. Barr133,L. Barranco Navarro45a,45b,F. Barreiro98, J. Barreiro Guimarães da Costa15a,U. Barron160,S. Barsov136,F. Bartels61a,R. Bartoldus152, G. Bartolini101,A.E. Barton89,P. Bartos28a,A. Basalaev46,A. Basan99,A. Bassalat64,ah,M.J. Basso165, C.R. Basson100,R.L. Bates57, S. Batlamous35f,J.R. Batley32,B. Batool150, M. Battaglia144, M. Bauce72a,72b,F. Bauer143,*,P. Bauer24, H.S. Bawa31,A. Bayirli12c,J.B. Beacham49,T. Beau134, P.H. Beauchemin168,F. Becherer52,P. Bechtle24,H.P. Beck20,q,K. Becker176,C. Becot46,A.J. Beddall12a, V.A. Bednyakov79,C.P. Bee154,T.A. Beermann180,M. Begalli80b,M. Begel29,A. Behera154,J.K. Behr46, J.F. Beirer53,36,F. Beisiegel24,M. Belfkir5,G. Bella160,L. Bellagamba23b,A. Bellerive34,P. Bellos21, K. Beloborodov121b,121a,K. Belotskiy111,N.L. Belyaev111,D. Benchekroun35a,N. Benekos10, Y. Benhammou160,D.P. Benjamin6,M. Benoit29,J.R. Bensinger26,S. Bentvelsen119,L. Beresford133, M. Beretta51,D. Berge19,E. Bergeaas Kuutmann170,N. Berger5,B. Bergmann140,L.J. Bergsten26, J. Beringer18,S. Berlendis7,G. Bernardi134,C. Bernius152,F.U. Bernlochner24,T. Berry93,P. Berta46, A. Berthold48,I.A. Bertram89,O. Bessidskaia Bylund180,S. Bethke114,A. Betti42,A.J. Bevan92, S. Bhatta154,D.S. Bhattacharya175, P. Bhattarai26,V.S. Bhopatkar6, R. Bi137,R.M. Bianchi137, O. Biebel113,R. Bielski36,K. Bierwagen99,N.V. Biesuz71a,71b,M. Biglietti74a,T.R.V. Billoud140, M. Bindi53,A. Bingul12d,C. Bini72a,72b,S. Biondi23b,23a,C.J. Birch-sykes100,G.A. Bird21,142, M. Birman178, T. Bisanz36,J.P. Biswal3,D. Biswas179,j,A. Bitadze100,C. Bittrich48,K. Bjørke132, T. Blazek28a,I. Bloch46,C. Blocker26,A. Blue57,U. Blumenschein92,G.J. Bobbink119, – 30 – 2021 JINST 16 P08025 V.S. Bobrovnikov121b,121a,D. Bogavac14,A.G. Bogdanchikov121b,121a, C. Bohm45a,V. Boisvert93, P. Bokan170,53,T. Bold83a,M. Bomben134,M. Bona92,J.S. Bonilla130,M. Boonekamp143,C.D. Booth93, A.G. Borbély57,H.M. Borecka-Bielska109,L.S. Borgna94,G. Borissov89,D. Bortoletto133, D. Boscherini23b,M. Bosman14,J.D. Bossio Sola103,K. Bouaouda35a,J. Boudreau137, E.V. Bouhova-Thacker89,D. Boumediene38,R. Bouquet134,A. Boveia126,J. Boyd36,D. Boye29, I.R. Boyko79,A.J. Bozson93,J. Bracinik21,N. Brahimi60d,60c,G. Brandt180,O. Brandt32,F. Braren46, B. Brau102,J.E. Brau130, W.D. Breaden Madden57,K. Brendlinger46,R. Brener159,L. Brenner36, R. Brenner170,S. Bressler178,B. Brickwedde99,D.L. Briglin21,D. Britton57,D. Britzger114,I. Brock24, R. Brock106,G. Brooijmans39,W.K. Brooks145d,E. Brost29,P.A. Bruckman de Renstrom84,B. Brüers46, D. Bruncko28b,A. Bruni23b,G. Bruni23b,M. Bruschi23b,N. Bruscino72a,72b,L. Bryngemark152, T. Buanes17,Q. Buat154,P. Buchholz150,A.G. Buckley57,I.A. Budagov79,M.K. Bugge132,O. Bulekov111, B.A. Bullard59,T.J. Burch120,S. Burdin90,C.D. Burgard46,A.M. Burger128,B. Burghgrave8,J.T.P. Burr46, C.D. Burton11,J.C. Burzynski102,V. Büscher99, E. Buschmann53,P.J. Bussey57,J.M. Butler25, C.M. Buttar57,J.M. Butterworth94,W. Buttinger142, C.J. Buxo Vazquez106,A.R. Buzykaev121b,121a, G. Cabras23b,23a,S. Cabrera Urbán172,D. Caforio56,H. Cai137,V.M.M. Cairo152,O. Cakir4a,N. Calace36, P. Calafiura18,G. Calderini134,P. Calfayan65,G. Callea57, L.P. Caloba80b, A. Caltabiano73a,73b, S. Calvente Lopez98,D. Calvet38,S. Calvet38,T.P. Calvet101,M. Calvetti71a,71b,R. Camacho Toro134, S. Camarda36,D. Camarero Munoz98,P. Camarri73a,73b,M.T. Camerlingo74a,74b,D. Cameron132, C. Camincher36,M. Campanelli94,A. Camplani40,V. Canale69a,69b,A. Canesse103,M. Cano Bret77, J. Cantero128,Y. Cao171,M. Capua41b,41a,R. Cardarelli73a,F. Cardillo172,G. Carducci41b,41a,T. Carli36, G. Carlino69a,B.T. Carlson137,E.M. Carlson174,166a,L. Carminati68a,68b,M. Carnesale72a,72b, R.M.D. Carney152,S. Caron118,E. Carquin145d,S. Carrá46,G. Carratta23b,23a,J.W.S. Carter165, T.M. Carter50,M.P. Casado14,g, A.F. Casha165,E.G. Castiglia181,F.L. Castillo172,L. Castillo Garcia14, V. Castillo Gimenez172,N.F. Castro138a,138e,A. Catinaccio36,J.R. Catmore132, A. Cattai36,V. Cavaliere29, V. Cavasinni71a,71b,E. Celebi12b,F. Celli133,K. Cerny129,A.S. Cerqueira80a,A. Cerri155,L. Cerrito73a,73b, F. Cerutti18,A. Cervelli23b,23a,S.A. Cetin12b, Z. Chadi35a,D. Chakraborty120,M. Chala138f,J. Chan179, W.S. Chan119,W.Y. Chan90,J.D. Chapman32,B. Chargeishvili158b,D.G. Charlton21,T.P. Charman92, M. Chatterjee20,C.C. Chau34,S. Chekanov6,S.V. Chekulaev166a,G.A. Chelkov79,af,B. Chen78, C. Chen60a,C.H. Chen78,H. Chen15c,H. Chen29,J. Chen60a,J. Chen39,J. Chen26,S. Chen135, S.J. Chen15c,X. Chen15b,Y. Chen60a,Y-H. Chen46,C.L. Cheng179,H.C. Cheng62a,H.J. Cheng15a, A. Cheplakov79,E. Cheremushkina122,R. Cherkaoui El Moursli35f,E. Cheu7,K. Cheung63, L. Chevalier143,V. Chiarella51,G. Chiarelli71a,G. Chiodini67a,A.S. Chisholm21,A. Chitan27b,I. Chiu162, Y.H. Chiu174,M.V. Chizhov79,s,K. Choi11,A.R. Chomont72a,72b,Y. Chou102, Y.S. Chow119, L.D. Christopher33f,M.C. Chu62a,X. Chu15a,15d,J. Chudoba139,J.J. Chwastowski84,D. Cieri114, K.M. Ciesla84,V. Cindro91,I.A. Cioară27b,A. Ciocio18,F. Cirotto69a,69b,Z.H. Citron178,k,M. Citterio68a, D.A. Ciubotaru27b,B.M. Ciungu165,A. Clark54,P.J. Clark50,S.E. Clawson100,C. Clement45a,45b, L. Clissa23b,23a,Y. Coadou101,M. Cobal66a,66c,A. Coccaro55b, J. Cochran78,R. Coelho Lopes De Sa102, S. Coelli68a, H. Cohen160,A.E.C. Coimbra36,B. Cole39,J. Collot58,P. Conde Muiño138a,138h, S.H. Connell33c,I.A. Connelly57,F. Conventi69a,al,A.M. Cooper-Sarkar133,F. Cormier173,L.D. Corpe94, M. Corradi72a,72b,E.E. Corrigan96,F. Corriveau103,aa,M.J. Costa172,F. Costanza5,D. Costanzo148, G. Cowan93,J.W. Cowley32,J. Crane100,K. Cranmer124,R.A. Creager135,S. Crépé-Renaudin58, F. Crescioli134,M. Cristinziani150,M. Cristoforetti75a,75b,V. Croft168,G. Crosetti41b,41a,A. Cueto5, T. Cuhadar Donszelmann169, H. Cui15a,15d,A.R. Cukierman152,W.R. Cunningham57,S. Czekierda84, P. Czodrowski36,M.M. Czurylo61b,M.J. Da Cunha Sargedas De Sousa60b,J.V. Da Fonseca Pinto80b, C. Da Via100,W. Dabrowski83a,T. Dado47,S. Dahbi33f,T. Dai105,C. Dallapiccola102,M. Dam40, G. D’amen29,V. D’Amico74a,74b,J. Damp99,J.R. Dandoy135,M.F. Daneri30,M. Danninger151,V. Dao36, G. Darbo55b,A. Dattagupta130,S. D’Auria68a,68b,C. David166b,T. Davidek141,D.R. Davis49, I. Dawson148,K. De8,R. De Asmundis69a,M. De Beurs119,S. De Castro23b,23a,N. De Groot118, – 31 – 2021 JINST 16 P08025 P. de Jong119,H. De la Torre106,A. De Maria15c,D. De Pedis72a,A. De Salvo72a,U. De Sanctis73a,73b, M. De Santis73a,73b,A. De Santo155,J.B. De Vivie De Regie58, D.V. Dedovich79,J. Degens119, A.M. Deiana42,J. Del Peso98,Y. Delabat Diaz46,F. Deliot143,C.M. Delitzsch7,M. Della Pietra69a,69b, D. Della Volpe54,A. Dell’Acqua36,L. Dell’Asta68a,68b,M. Delmastro5, C. Delporte64,P.A. Delsart58, S. Demers181,M. Demichev79, G. Demontigny109,S.P. Denisov122,L. D’Eramo120,D. Derendarz84, J.E. Derkaoui35e,F. Derue134,P. Dervan90,K. Desch24,K. Dette165,C. Deutsch24,P.O. Deviveiros36, F.A. Di Bello72a,72b,A. Di Ciaccio73a,73b,L. Di Ciaccio5,C. Di Donato69a,69b,A. Di Girolamo36, G. Di Gregorio71a,71b,A. Di Luca75a,75b,B. Di Micco74a,74b,R. Di Nardo74a,74b,C. Diaconu101, F.A. Dias119,T. Dias Do Vale138a,M.A. Diaz145a,F.G. Diaz Capriles24,J. Dickinson18,M. Didenko164, E.B. Diehl105,J. Dietrich19,S. Díez Cornell46,C. Diez Pardos150,A. Dimitrievska18,W. Ding15b, J. Dingfelder24,S.J. Dittmeier61b,F. Dittus36,F. Djama101,T. Djobava158b,J.I. Djuvsland17, M.A.B. Do Vale146,M. Dobre27b,D. Dodsworth26,C. Doglioni96,J. Dolejsi141,Z. Dolezal141, M. Donadelli80c,B. Dong60c,J. Donini38,A. D’onofrio15c,M. D’Onofrio90,J. Dopke142,A. Doria69a, M.T. Dova88,A.T. Doyle57,E. Drechsler151,E. Dreyer151,T. Dreyer53,A.S. Drobac168,D. Du60b, T.A. du Pree119,Y. Duan60d,F. Dubinin110,M. Dubovsky28a,A. Dubreuil54,E. Duchovni178, G. Duckeck113,O.A. Ducu36,27b,D. Duda114,A. Dudarev36,A.C. Dudder99,M. D’uffizi100,L. Duflot64, M. Dührssen36,C. Dülsen180,M. Dumancic178,A.E. Dumitriu27b,M. Dunford61a,S. Dungs47, A. Duperrin101,H. Duran Yildiz4a,M. Düren56,A. Durglishvili158b,B. Dutta46,D. Duvnjak1, G.I. Dyckes135,M. Dyndal36,S. Dysch100,B.S. Dziedzic84,B. Eckerova28a, M.G. Eggleston49, E. Egidio Purcino De Souza80b,L.F. Ehrke54,T. Eifert8,G. Eigen17,K. Einsweiler18,T. Ekelof170, H. El Jarrari35f,A. El Moussaouy35a,V. Ellajosyula170,M. Ellert170,F. Ellinghaus180,A.A. Elliot92, N. Ellis36,J. Elmsheuser29,M. Elsing36,D. Emeliyanov142,A. Emerman39,Y. Enari162,J. Erdmann47, A. Ereditato20,P.A. Erland84,M. Errenst180,M. Escalier64,C. Escobar172,O. Estrada Pastor172, E. Etzion160,G. Evans138a,H. Evans65,M.O. Evans155,A. Ezhilov136,F. Fabbri57,L. Fabbri23b,23a, V. Fabiani118,G. Facini176, R.M. Fakhrutdinov122,S. Falciano72a,P.J. Falke24,S. Falke36,J. Faltova141, Y. Fan15a,Y. Fang15a,Y. Fang15a,G. Fanourakis44,M. Fanti68a,68b,M. Faraj60c,A. Farbin8,A. Farilla74a, E.M. Farina70a,70b,T. Farooque106,S.M. Farrington50,P. Farthouat36,F. Fassi35f,D. Fassouliotis9, M. Faucci Giannelli73a,73b,W.J. Fawcett32,L. Fayard64,O.L. Fedin136,p,A. Fehr20,M. Feickert171, L. Feligioni101,A. Fell148,C. Feng60b,M. Feng49,M.J. Fenton169, A.B. Fenyuk122,S.W. Ferguson43, J. Ferrando46,A. Ferrari170,P. Ferrari119,R. Ferrari70a,D. Ferrere54,C. Ferretti105,F. Fiedler99, A. Filipčič91,F. Filthaut118,K.D. Finelli25,M.C.N. Fiolhais138a,138c,a,L. Fiorini172,F. Fischer113, J. Fischer99,W.C. Fisher106,T. Fitschen21,I. Fleck150,P. Fleischmann105,T. Flick180,B.M. Flierl113, L. Flores135,L.R. Flores Castillo62a,F.M. Follega75a,75b,N. Fomin17,J.H. Foo165,G.T. Forcolin75a,75b, B.C. Forland65,A. Formica143,F.A. Förster14,A.C. Forti100, E. Fortin101,M.G. Foti133,D. Fournier64, H. Fox89,P. Francavilla71a,71b,S. Francescato72a,72b,M. Franchini23b,23a,S. Franchino61a, D. Francis36, L. Franco5,L. Franconi20,M. Franklin59,G. Frattari72a,72b, P.M. Freeman21,B. Freund109, W.S. Freund80b,E.M. Freundlich47,D.C. Frizzell127,D. Froidevaux36,J.A. Frost133,Y. Fu60a, M. Fujimoto125,E. Fullana Torregrosa172, T. Fusayasu115,J. Fuster172,A. Gabrielli23b,23a,A. Gabrielli36, P. Gadow114,G. Gagliardi55b,55a,L.G. Gagnon109,G.E. Gallardo133,E.J. Gallas133,B.J. Gallop142, R. Gamboa Goni92,K.K. Gan126,S. Ganguly178,J. Gao60a,Y. Gao50,Y.S. Gao31,m,F.M. Garay Walls145a, C. García172,J.E. García Navarro172,J.A. García Pascual15a,M. Garcia-Sciveres18,R.W. Gardner37, S. Gargiulo52, C.A. Garner165,V. Garonne132,S.J. Gasiorowski147,P. Gaspar80b,G. Gaudio70a, P. Gauzzi72a,72b,I.L. Gavrilenko110,A. Gavrilyuk123,C. Gay173,G. Gaycken46,E.N. Gazis10, A.A. Geanta27b,C.M. Gee144,C.N.P. Gee142,J. Geisen96,M. Geisen99,C. Gemme55b,M.H. Genest58, C. Geng105,S. Gentile72a,72b,S. George93,T. Geralis44, L.O. Gerlach53,P. Gessinger-Befurt99, G. Gessner47,M. Ghasemi Bostanabad174,M. Ghneimat150,A. Ghosh169,A. Ghosh77,B. Giacobbe23b, S. Giagu72a,72b,N. Giangiacomi165,P. Giannetti71a,A. Giannini69a,69b,S.M. Gibson93,M. Gignac144, D.T. Gil83b,B.J. Gilbert39,D. Gillberg34,G. Gilles180,N.E.K. Gillwald46,D.M. Gingrich3,ak, – 32 – 2021 JINST 16 P08025 M.P. Giordani66a,66c,P.F. Giraud143,G. Giugliarelli66a,66c,D. Giugni68a,F. Giuli73a,73b,S. Gkaitatzis161, I. Gkialas9,h,E.L. Gkougkousis14,P. Gkountoumis10,L.K. Gladilin112,C. Glasman98,G.R. Gledhill130, I. Gnesi41b,c,M. Goblirsch-Kolb26, D. Godin109,S. Goldfarb104,T. Golling54,D. Golubkov122, A. Gomes138a,138b,R. Goncalves Gama53,R. Gonçalo138a,138c,G. Gonella130,L. Gonella21, A. Gongadze79,F. Gonnella21,J.L. Gonski39,S. González de la Hoz172,S. Gonzalez Fernandez14, R. Gonzalez Lopez90,C. Gonzalez Renteria18,R. Gonzalez Suarez170,S. Gonzalez-Sevilla54, G.R. Gonzalvo Rodriguez172,L. Goossens36,N.A. Gorasia21,P.A. Gorbounov123,H.A. Gordon29, B. Gorini36,E. Gorini67a,67b,A. Gorišek91,A.T. Goshaw49,M.I. Gostkin79,C.A. Gottardo118, M. Gouighri35b,A.G. Goussiou147,N. Govender33c,C. Goy5,I. Grabowska-Bold83a,E. Gramstad132, S. Grancagnolo19,M. Grandi155, V. Gratchev136,P.M. Gravila27f,F.G. Gravili67a,67b,C. Gray57, H.M. Gray18,C. Grefe24,I.M. Gregor46,P. Grenier152,K. Grevtsov46,C. Grieco14, N.A. Grieser127, A.A. Grillo144,K. Grimm31,l,S. Grinstein14,w,J.-F. Grivaz64,S. Groh99,E. Gross178,J. Grosse-Knetter53, Z.J. Grout94, C. Grud105,A. Grummer117,J.C. Grundy133,L. Guan105,W. Guan179,C. Gubbels173, J. Guenther36,J.G.R. Guerrero Rojas172,F. Guescini114,D. Guest19,R. Gugel99,A. Guida46, T. Guillemin5,S. Guindon36,J. Guo60c,L. Guo64,Y. Guo105,Z. Guo101,R. Gupta46,S. Gurbuz24, G. Gustavino127,M. Guth52,P. Gutierrez127,L.F. Gutierrez Zagazeta135,C. Gutschow94,C. Guyot143, C. Gwenlan133,C.B. Gwilliam90,E.S. Haaland132,A. Haas124,M.H. Habedank19,C. Haber18, H.K. Hadavand8,A. Hadef99,M. Haleem175,J. Haley128,J.J. Hall148,G. Halladjian106,G.D. Hallewell101, K. Hamano174,H. Hamdaoui35f,M. Hamer24,G.N. Hamity50,K. Han60a,L. Han15c,L. Han60a,S. Han18, Y.F. Han165,K. Hanagaki81,u,M. Hance144,M.D. Hank37,R. Hankache100,E. Hansen96,J.B. Hansen40, J.D. Hansen40,M.C. Hansen24,P.H. Hansen40,E.C. Hanson100,K. Hara167,T. Harenberg180, S. Harkusha107, P.F. Harrison176,N.M. Hartman152,N.M. Hartmann113,Y. Hasegawa149,A. Hasib50, S. Hassani143,S. Haug20,R. Hauser106,M. Havranek140,C.M. Hawkes21,R.J. Hawkings36, S. Hayashida116,D. Hayden106,C. Hayes105,R.L. Hayes173,C.P. Hays133,J.M. Hays92,H.S. Hayward90, S.J. Haywood142,F. He60a,Y. He163,Y. He134,M.P. Heath50,V. Hedberg96,A.L. Heggelund132, N.D. Hehir92,C. Heidegger52,K.K. Heidegger52,W.D. Heidorn78,J. Heilman34,S. Heim46,T. Heim18, B. Heinemann46,ai,J.G. Heinlein135,J.J. Heinrich130,L. Heinrich36,J. Hejbal139,L. Helary46,A. Held124, S. Hellesund132,C.M. Helling144,S. Hellman45a,45b,C. Helsens36, R.C.W. Henderson89, L. Henkelmann32, A.M. Henriques Correia36,H. Herde152,Y. Hernández Jiménez33f, H. Herr99, M.G. Herrmann113,T. Herrmann48,G. Herten52,R. Hertenberger113,L. Hervas36,N.P. Hessey166a, H. Hibi82,S. Higashino81,E. Higón-Rodriguez172, K. Hildebrand37,K.K. Hill29, K.H. Hiller46, S.J. Hillier21,M. Hils48,I. Hinchliffe18,F. Hinterkeuser24,M. Hirose131,S. Hirose167,D. Hirschbuehl180, B. Hiti91, O. Hladik139,J. Hobbs154,R. Hobincu27e,N. Hod178,M.C. Hodgkinson148,A. Hoecker36, M.R. Hoeferkamp117,D. Hohn52,T. Holm24,T.R. Holmes37,M. Holzbock114,L.B.A.H. Hommels32, B.P. Honan100,T.M. Hong137,J.C. Honig52,A. Hönle114,B.H. Hooberman171,W.H. Hopkins6, Y. Horii116,P. Horn48,L.A. Horyn37,S. Hou157,J. Howarth57,J. Hoya88,M. Hrabovsky129, A. Hrynevich108,T. Hryn’ova5,P.J. Hsu63,S.-C. Hsu147,Q. Hu39,S. Hu60c,Y.F. Hu15a,15d,am, D.P. Huang94,X. Huang15c,Y. Huang60a,Y. Huang15a,Z. Hubacek140,F. Hubaut101,M. Huebner24, F. Huegging24,T.B. Huffman133,M. Huhtinen36,R. Hulsken58,R.F.H. Hunter34,N. Huseynov79,ab, J. Huston106,J. Huth59,R. Hyneman152,S. Hyrych28a,G. Iacobucci54,G. Iakovidis29,I. Ibragimov150, L. Iconomidou-Fayard64,P. Iengo36, R. Ignazzi40,R. Iguchi162,T. Iizawa54,Y. Ikegami81, N. Ilic165,165, H. Imam35a,G. Introzzi70a,70b,M. Iodice74a,K. Iordanidou166a,V. Ippolito72a,72b,M. Ishino162, W. Islam128,C. Issever19,46,S. Istin12c,J.M. Iturbe Ponce62a,R. Iuppa75a,75b,A. Ivina178,J.M. Izen43, V. Izzo69a,P. Jacka139,P. Jackson1,R.M. Jacobs46,B.P. Jaeger151,C.S. Jagfeld113,G. Jäkel180, K.B. Jakobi99,K. Jakobs52,T. Jakoubek178,J. Jamieson57,K.W. Janas83a,P.A. Janus83a,G. Jarlskog96, A.E. Jaspan90, N. Javadov79,ab,T. Javůrek36,M. Javurkova102,F. Jeanneau143,L. Jeanty130,J. Jejelava158a, P. Jenni52,d,S. Jézéquel5,J. Jia154,Z. Jia15c, Y. Jiang60a,S. Jiggins52, F.A. Jimenez Morales38, J. Jimenez Pena114,S. Jin15c,A. Jinaru27b,O. Jinnouchi163,H. Jivan33f,P. Johansson148,K.A. Johns7, – 33 – 2021 JINST 16 P08025 C.A. Johnson65,E. Jones176,R.W.L. Jones89,T.J. Jones90,J. Jovicevic36,X. Ju18,J.J. Junggeburth114, A. Juste Rozas14,w,A. Kaczmarska84, M. Kado72a,72b,H. Kagan126,M. Kagan152, A. Kahn39,C. Kahra99, T. Kaji177,E. Kajomovitz159,C.W. Kalderon29, A. Kaluza99,A. Kamenshchikov122,M. Kaneda162, N.J. Kang144,S. Kang78,Y. Kano116, J. Kanzaki81,D. Kar33f,K. Karava133,M.J. Kareem166b, I. Karkanias161,S.N. Karpov79,Z.M. Karpova79,V. Kartvelishvili89,A.N. Karyukhin122,E. Kasimi161, C. Kato60d,J. Katzy46,K. Kawade149,K. Kawagoe87,T. Kawaguchi116,T. Kawamoto143, G. Kawamura53, E.F. Kay174,F.I. Kaya168,S. Kazakos14,V.F. Kazanin121b,121a,Y. Ke154,J.M. Keaveney33a,R. Keeler174, J.S. Keller34,D. Kelsey155,J.J. Kempster21,J. Kendrick21,K.E. Kennedy39,O. Kepka139,S. Kersten180, B.P. Kerševan91,S. Ketabchi Haghighat165, F. Khalil-Zada13,M. Khandoga143,A. Khanov128, A.G. Kharlamov121b,121a,T. Kharlamova121b,121a,E.E. Khoda173,T.J. Khoo19,G. Khoriauli175, E. Khramov79,J. Khubua158b,S. Kido82,M. Kiehn36,A. Kilgallon130,E. Kim163,Y.K. Kim37, N. Kimura94,A. Kirchhoff53,D. Kirchmeier48,J. Kirk142,A.E. Kiryunin114,T. Kishimoto162, D.P. Kisliuk165,V. Kitali46,C. Kitsaki10,O. Kivernyk24,T. Klapdor-Kleingrothaus52,M. Klassen61a, C. Klein34,L. Klein175,M.H. Klein105,M. Klein90,U. Klein90,P. Klimek36,A. Klimentov29, F. Klimpel36,T. Klingl24,T. Klioutchnikova36,F.F. Klitzner113,P. Kluit119,S. Kluth114,E. Kneringer76, A. Knue52, D. Kobayashi87,M. Kobel48,M. Kocian152, T. Kodama162,P. Kodys141,D.M. Koeck155, P.T. Koenig24,T. Koffas34,N.M. Köhler36,M. Kolb143,I. Koletsou5,T. Komarek129,K. Köneke52, A.X.Y. Kong1,T. Kono125, V. Konstantinides94,N. Konstantinidis94,B. Konya96,R. Kopeliansky65, S. Koperny83a,K. Korcyl84,K. Kordas161, G. Koren160,A. Korn94,S. Korn53,I. Korolkov14, E.V. Korolkova148,N. Korotkova112,O. Kortner114,S. Kortner114,V.V. Kostyukhin148,164, A. Kotsokechagia64,A. Kotwal49,A. Koulouris10,A. Kourkoumeli-Charalampidi70a,70b, C. Kourkoumelis9,E. Kourlitis6,R. Kowalewski174,W. Kozanecki143,A.S. Kozhin122, V.A. Kramarenko112,G. Kramberger91,D. Krasnopevtsev60a,M.W. Krasny134,A. Krasznahorkay36, J.A. Kremer99,J. Kretzschmar90,K. Kreul19,P. Krieger165,F. Krieter113,S. Krishnamurthy102, A. Krishnan61b,M. Krivos141,K. Krizka18,K. Kroeninger47,H. Kroha114,J. Kroll139,J. Kroll135, K.S. Krowpman106,U. Kruchonak79,H. Krüger24, N. Krumnack78,M.C. Kruse49,J.A. Krzysiak84, A. Kubota163,O. Kuchinskaia164,S. Kuday4b,D. Kuechler46,J.T. Kuechler46,S. Kuehn36,T. Kuhl46, V. Kukhtin79,Y. Kulchitsky107,ae,S. Kuleshov145b,M. Kumar33f,M. Kuna58,A. Kupco139, T. Kupfer47, O. Kuprash52,H. Kurashige82,L.L. Kurchaninov166a,Y.A. Kurochkin107,A. Kurova111, M.G. Kurth15a,15d,E.S. Kuwertz36,M. Kuze163,A.K. Kvam147,J. Kvita129,T. Kwan103,C. Lacasta172, F. Lacava72a,72b,D.P.J. Lack100,H. Lacker19,D. Lacour134,E. Ladygin79,R. Lafaye5,B. Laforge134, T. Lagouri145c,S. Lai53,I.K. Lakomiec83a,J.E. Lambert127, S. Lammers65,W. Lampl7,C. Lampoudis161, E. Lançon29,U. Landgraf52,M.P.J. Landon92,V.S. Lang52,J.C. Lange53,R.J. Langenberg102, A.J. Lankford169,F. Lanni29,K. Lantzsch24,A. Lanza70a,A. Lapertosa55b,55a,J.F. Laporte143,T. Lari68a, F. Lasagni Manghi23b,23a,M. Lassnig36,V. Latonova139,T.S. Lau62a,A. Laudrain99,A. Laurier34, M. Lavorgna69a,69b,S.D. Lawlor93,M. Lazzaroni68a,68b, B. Le100,A. Lebedev78,M. LeBlanc7, T. LeCompte6,F. Ledroit-Guillon58, A.C.A. Lee94,C.A. Lee29,G.R. Lee17,L. Lee59,S.C. Lee157, S. Lee78,L.L. Leeuw33c,B. Lefebvre166a,H.P. Lefebvre93,M. Lefebvre174,C. Leggett18,K. Lehmann151, N. Lehmann20,G. Lehmann Miotto36,W.A. Leight46,A. Leisos161,v,M.A.L. Leite80c,C.E. Leitgeb113, R. Leitner141,K.J.C. Leney42,T. Lenz24,S. Leone71a,C. Leonidopoulos50,A. Leopold134,C. Leroy109, R. Les106,C.G. Lester32,M. Levchenko136,J. Levêque5,D. Levin105,L.J. Levinson178,D.J. Lewis21, B. Li15b,B. Li105,C-Q. Li60c,60d, F. Li60c,H. Li60a,H. Li60b,J. Li60c,K. Li147,L. Li60c,M. Li15a,15d, Q.Y. Li60a,S. Li60d,60c,b,X. Li46,Y. Li46,Z. Li60b,Z. Li133,Z. Li103, Z. Li90,Z. Liang15a, M. Liberatore46,B. Liberti73a,K. Lie62c,C.Y. Lin32,K. Lin106,R.A. Linck65, R.E. Lindley7, J.H. Lindon21,A. Linss46,A.L. Lionti54,E. Lipeles135,A. Lipniacka17,T.M. Liss171,aj,A. Lister173, J.D. Little8,B. Liu15a,B.X. Liu151,J.B. Liu60a,J.K.K. Liu37,K. Liu60d,60c,M. Liu60a,M.Y. Liu60a, P. Liu15a,X. Liu60a,Y. Liu46,Y. Liu15a,15d,Y.L. Liu105,Y.W. Liu60a,M. Livan70a,70b,A. Lleres58, J. Llorente Merino151,S.L. Lloyd92,E.M. Lobodzinska46,P. Loch7,S. Loffredo73a,73b,T. Lohse19, – 34 – 2021 JINST 16 P08025 K. Lohwasser148,M. Lokajicek139,J.D. Long171,R.E. Long89,I. Longarini72a,72b,L. Longo36, R. Longo171, I. Lopez Paz14,A. Lopez Solis46,J. Lorenz113,N. Lorenzo Martinez5,A.M. Lory113, A. Lösle52,X. Lou45a,45b,X. Lou15a,A. Lounis64,J. Love6,P.A. Love89,J.J. Lozano Bahilo172,G. Lu15a, M. Lu60a,S. Lu135,Y.J. Lu63,H.J. Lubatti147,C. Luci72a,72b,F.L. Lucio Alves15c,A. Lucotte58, F. Luehring65,I. Luise154, L. Luminari72a,B. Lund-Jensen153,N.A. Luongo130,M.S. Lutz160,D. Lynn29, H. Lyons90,R. Lysak139,E. Lytken96,F. Lyu15a,V. Lyubushkin79,T. Lyubushkina79,H. Ma29,L.L. Ma60b, Y. Ma94,D.M. Mac Donell174,G. Maccarrone51,C.M. Macdonald148,J.C. MacDonald148, J. Machado Miguens135,R. Madar38,W.F. Mader48,M. Madugoda Ralalage Don128,N. Madysa48, J. Maeda82,T. Maeno29,M. Maerker48,V. Magerl52,J. Magro66a,66c,D.J. Mahon39,C. Maidantchik80b, A. Maio138a,138b,138d,K. Maj83a,O. Majersky28a,S. Majewski130,N. Makovec64,B. Malaescu134, Pa. Malecki84,V.P. Maleev136,F. Malek58,D. Malito41b,41a,U. Mallik77,C. Malone32, S. Maltezos10, S. Malyukov79,J. Mamuzic172,G. Mancini51,J.P. Mandalia92,I. Mandić91, L. Manhaes de Andrade Filho80a,I.M. Maniatis161,M. Manisha143,J. Manjarres Ramos48, K.H. Mankinen96,A. Mann113,A. Manousos76,B. Mansoulie143,I. Manthos161,S. Manzoni119, A. Marantis161,v,L. Marchese133,G. Marchiori134,M. Marcisovsky139,L. Marcoccia73a,73b,C. Marcon96, M. Marjanovic127,Z. Marshall18,M.U.F. Martensson170,S. Marti-Garcia172,T.A. Martin176, V.J. Martin50,B. Martin dit Latour17,L. Martinelli74a,74b,M. Martinez14,w,P. Martinez Agullo172, V.I. Martinez Outschoorn102,S. Martin-Haugh142,V.S. Martoiu27b,A.C. Martyniuk94,A. Marzin36, S.R. Maschek114,L. Masetti99,T. Mashimo162,R. Mashinistov110,J. Masik100,A.L. Maslennikov121b,121a, L. Massa23b,23a,P. Massarotti69a,69b,P. Mastrandrea71a,71b,A. Mastroberardino41b,41a,T. Masubuchi162, D. Matakias29,T. Mathisen170,A. Matic113, N. Matsuzawa162,J. Maurer27b,B. Maček91, D.A. Maximov121b,121a,R. Mazini157,I. Maznas161,S.M. Mazza144,C. Mc Ginn29,J.P. Mc Gowan103, S.P. Mc Kee105,T.G. McCarthy114,W.P. McCormack18,E.F. McDonald104,A.E. McDougall119, J.A. Mcfayden155,G. Mchedlidze158b, M.A. McKay42,K.D. McLean174,S.J. McMahon142, P.C. McNamara104,R.A. McPherson174,aa,J.E. Mdhluli33f,Z.A. Meadows102,S. Meehan36,T. Megy38, S. Mehlhase113,A. Mehta90,B. Meirose43,D. Melini159,B.R. Mellado Garcia33f,F. Meloni46, A. Melzer24,E.D. Mendes Gouveia138a,138e,A.M. Mendes Jacques Da Costa21, H.Y. Meng165,L. Meng36, S. Menke114,E. Meoni41b,41a, S.A.M. Merkt137,C. Merlassino133,P. Mermod54,*,L. Merola69a,69b, C. Meroni68a, G. Merz105,O. Meshkov112,110,J.K.R. Meshreki150,J. Metcalfe6,A.S. Mete6,C. Meyer65, J-P. Meyer143,M. Michetti19,R.P. Middleton142,L. Mijović50,G. Mikenberg178,M. Mikestikova139, M. Mikuž91,H. Mildner148,A. Milic165,C.D. Milke42,D.W. Miller37,L.S. Miller34,A. Milov178, D.A. Milstead45a,45b,A.A. Minaenko122,I.A. Minashvili158b,L. Mince57,A.I. Mincer124,B. Mindur83a, M. Mineev79, Y. Minegishi162,Y. Mino85,L.M. Mir14,M. Miralles Lopez172, M. Mironova133, T. Mitani177,V.A. Mitsou172, M. Mittal60c,O. Miu165,A. Miucci20,P.S. Miyagawa92,A. Mizukami81, J.U. Mjörnmark96,T. Mkrtchyan61a,M. Mlynarikova120,T. Moa45a,45b,S. Mobius53,K. Mochizuki109, P. Moder46,P. Mogg113,S. Mohapatra39,G. Mokgatitswane33f,B. Mondal150,S. Mondal140,K. Mönig46, E. Monnier101,A. Montalbano151,J. Montejo Berlingen36,M. Montella94,F. Monticelli88,N. Morange64, A.L. Moreira De Carvalho138a,M. Moreno Llácer172,C. Moreno Martinez14,P. Morettini55b, M. Morgenstern159,S. Morgenstern176,D. Mori151,M. Morii59,M. Morinaga177,V. Morisbak132, A.K. Morley36,A.P. Morris94,L. Morvaj36,P. Moschovakos36,B. Moser119, M. Mosidze158b, T. Moskalets143,P. Moskvitina118,J. Moss31,n,E.J.W. Moyse102,S. Muanza101,J. Mueller137, D. Muenstermann89,G.A. Mullier96, J.J. Mullin135,D.P. Mungo68a,68b,J.L. Munoz Martinez14, F.J. Munoz Sanchez100,P. Murin28b,W.J. Murray176,142,A. Murrone68a,68b,J.M. Muse127, M. Muškinja18,C. Mwewa29,A.G. Myagkov122,af, A.A. Myers137,G. Myers65,J. Myers130,M. Myska140, B.P. Nachman18,O. Nackenhorst47,A.Nag Nag48,K. Nagai133,K. Nagano81,J.L. Nagle29,E. Nagy101, A.M. Nairz36,Y. Nakahama116,K. Nakamura81,H. Nanjo131,F. Napolitano61a,R.F. Naranjo Garcia46, R. Narayan42,I. Naryshkin136,M. Naseri34,T. Naumann46,G. Navarro22a,J. Navarro-Gonzalez172, P.Y. Nechaeva110,F. Nechansky46,T.J. Neep21,A. Negri70a,70b,M. Negrini23b,C. Nellist118,C. Nelson103, – 35 – 2021 JINST 16 P08025 K. Nelson105,M.E. Nelson45a,45b,S. Nemecek139,M. Nessi36,f,M.S. Neubauer171,F. Neuhaus99, M. Neumann180,R. Newhouse173,P.R. Newman21,C.W. Ng137, Y.S. Ng19,Y.W.Y. Ng169,B. Ngair35f, H.D.N. Nguyen101,T. Nguyen Manh109,E. Nibigira38,R.B. Nickerson133,R. Nicolaidou143, D.S. Nielsen40,J. Nielsen144,M. Niemeyer53,N. Nikiforou11,V. Nikolaenko122,af,I. Nikolic-Audit134, K. Nikolopoulos21,P. Nilsson29,H.R. Nindhito54,A. Nisati72a,N. Nishu60c,R. Nisius114,T. Nitta177, T. Nobe162,D.L. Noel32,Y. Noguchi85,I. Nomidis134, M.A. Nomura29,R.R.B. Norisam94,J. Novak91, T. Novak46,O. Novgorodova48,R. Novotny117, L. Nozka129,K. Ntekas169, E. Nurse94,F.G. Oakham34,ak, J. Ocariz134,A. Ochi82,I. Ochoa138a,J.P. Ochoa-Ricoux145a,K. O’Connor26,S. Oda87,S. Odaka81, S. Oerdek53,A. Ogrodnik83a,A. Oh100,C.C. Ohm153,H. Oide163,R. Oishi162,M.L. Ojeda165, Y. Okazaki85, M.W. O’Keefe90,Y. Okumura162, A. Olariu27b,L.F. Oleiro Seabra138a, S.A. Olivares Pino145c,D. Oliveira Damazio29,D. Oliveira Goncalves80a,J.L. Oliver1,M.J.R. Olsson169, A. Olszewski84,J. Olszowska84,Ö.O. Öncel24,D.C. O’Neil151,A.P. O’neill133,A. Onofre138a,138e, P.U.E. Onyisi11, H. Oppen132, R.G. Oreamuno Madriz120,M.J. Oreglia37,G.E. Orellana88, D. Orestano74a,74b,N. Orlando14,R.S. Orr165,V. O’Shea57,R. Ospanov60a,G. Otero y Garzon30, H. Otono87,P.S. Ott61a,G.J. Ottino18,M. Ouchrif35e,J. Ouellette29,F. Ould-Saada132,A. Ouraou143,*, Q. Ouyang15a,M. Owen57,R.E. Owen142,V.E. Ozcan12c,N. Ozturk8,J. Pacalt129,H.A. Pacey32, K. Pachal49,A. Pacheco Pages14,C. Padilla Aranda14,S. Pagan Griso18, G. Palacino65,S. Palazzo50, S. Palestini36,M. Palka83b,P. Palni83a,D.K. Panchal11,C.E. Pandini54,J.G. Panduro Vazquez93,P. Pani46, G. Panizzo66a,66c,L. Paolozzi54,C. Papadatos109,S. Parajuli42,A. Paramonov6,C. Paraskevopoulos10, D. Paredes Hernandez62b,S.R. Paredes Saenz133,B. Parida178,T.H. Park165,A.J. Parker31,M.A. Parker32, F. Parodi55b,55a,E.W. Parrish120,J.A. Parsons39,U. Parzefall52,L. Pascual Dominguez134,V.R. Pascuzzi18, J.M.P. Pasner144,F. Pasquali119,E. Pasqualucci72a,S. Passaggio55b,F. Pastore93,P. Pasuwan45a,45b, J.R. Pater100,A. Pathak179,j, J. Patton90,T. Pauly36,J. Pearkes152,M. Pedersen132,L. Pedraza Diaz118, R. Pedro138a,T. Peiffer53,S.V. Peleganchuk121b,121a,O. Penc139,C. Peng62b,H. Peng60a,M. Penzin164, B.S. Peralva80a,M.M. Perego64,A.P. Pereira Peixoto138a,L. Pereira Sanchez45a,45b,D.V. Perepelitsa29, E. Perez Codina166a,M. Perganti10,L. Perini68a,68b,H. Pernegger36,S. Perrella36,A. Perrevoort119, K. Peters46,R.F.Y. Peters100,B.A. Petersen36,T.C. Petersen40,E. Petit101,V. Petousis140,C. Petridou161, P. Petroff64,F. Petrucci74a,74b,M. Pettee181,N.E. Pettersson102,K. Petukhova141,A. Peyaud143, R. Pezoa145d,L. Pezzotti70a,70b,G. Pezzullo181,T. Pham104,P.W. Phillips142,M.W. Phipps171, G. Piacquadio154,E. Pianori18,A. Picazio102,R. Piegaia30,D. Pietreanu27b,J.E. Pilcher37, A.D. Pilkington100,M. Pinamonti66a,66c,J.L. Pinfold3, C. Pitman Donaldson94,D.A. Pizzi34, L. Pizzimento73a,73b,A. Pizzini119,M.-A. Pleier29, V. Plesanovs52,V. Pleskot141, E. Plotnikova79, P. Podberezko121b,121a,R. Poettgen96,R. Poggi54,L. Poggioli134,I. Pogrebnyak106,D. Pohl24, I. Pokharel53,G. Polesello70a,A. Poley151,166a,A. Policicchio72a,72b,R. Polifka141,A. Polini23b, C.S. Pollard46,V. Polychronakos29,D. Ponomarenko111,L. Pontecorvo36,S. Popa27a,G.A. Popeneciu27d, L. Portales5,D.M. Portillo Quintero58,S. Pospisil140,P. Postolache27c,K. Potamianos133,I.N. Potrap79, C.J. Potter32,H. Potti11,T. Poulsen46,J. Poveda172,T.D. Powell148,G. Pownall46, M.E. Pozo Astigarraga36,A. Prades Ibanez172,P. Pralavorio101,M.M. Prapa44,S. Prell78,D. Price100, M. Primavera67a,M.L. Proffitt147,N. Proklova111,K. Prokofiev62c,F. Prokoshin79,S. Protopopescu29, J. Proudfoot6,M. Przybycien83a,D. Pudzha136, P. Puzo64,D. Pyatiizbyantseva111,J. Qian105,Y. Qin100, A. Quadt53,M. Queitsch-Maitland36,G. Rabanal Bolanos59,F. Ragusa68a,68b,G. Rahal97,J.A. Raine54, S. Rajagopalan29,K. Ran15a,15d,D.F. Rassloff61a,D.M. Rauch46,S. Rave99,B. Ravina57,I. Ravinovich178, M. Raymond36,A.L. Read132,N.P. Readioff148,M. Reale67a,67b,D.M. Rebuzzi70a,70b,G. Redlinger29, K. Reeves43,D. Reikher160, A. Reiss99,A. Rej150,C. Rembser36,A. Renardi46,M. Renda27b, M.B. Rendel114,A.G. Rennie57,S. Resconi68a,E.D. Resseguie18,S. Rettie94, B. Reynolds126, E. Reynolds21,M. Rezaei Estabragh180,O.L. Rezanova121b,121a,P. Reznicek141,E. Ricci75a,75b, R. Richter114,S. Richter46,E. Richter-Was83b,M. Ridel134,P. Rieck114,O. Rifki46, M. Rijssenbeek154, A. Rimoldi70a,70b,M. Rimoldi46,L. Rinaldi23b,T.T. Rinn171,M.P. Rinnagel113,G. Ripellino153,I. Riu14, – 36 – 2021 JINST 16 P08025 P. Rivadeneira46,J.C. Rivera Vergara174,F. Rizatdinova128,E. Rizvi92,C. Rizzi54,S.H. Robertson103,aa, M. Robin46,D. Robinson32, C.M. Robles Gajardo145d,M. Robles Manzano99,A. Robson57, A. Rocchi73a,73b,C. Roda71a,71b,S. Rodriguez Bosca172,A. Rodriguez Rodriguez52, A.M. Rodríguez Vera166b, S. Roe36,J. Roggel180,O. Røhne132,R.A. Rojas145d,B. Roland52, C.P.A. Roland65,J. Roloff29,A. Romaniouk111,M. Romano23b,23a,N. Rompotis90,M. Ronzani124, L. Roos134,S. Rosati72a, G. Rosin102,B.J. Rosser135,E. Rossi165,E. Rossi5,E. Rossi69a,69b,L.P. Rossi55b, L. Rossini46,R. Rosten126,M. Rotaru27b,B. Rottler52,D. Rousseau64,D. Rousso32,G. Rovelli70a,70b, A. Roy11,A. Rozanov101,Y. Rozen159,X. Ruan33f,A.J. Ruby90,T.A. Ruggeri1,F. Rühr52, A. Ruiz-Martinez172,A. Rummler36,Z. Rurikova52,N.A. Rusakovich79,H.L. Russell36,L. Rustige38, J.P. Rutherfoord7,E.M. Rüttinger148,M. Rybar141,E.B. Rye132,A. Ryzhov122,J.A. Sabater Iglesias46, P. Sabatini172,L. Sabetta72a,72b,H.F-W. Sadrozinski144,R. Sadykov79,F. Safai Tehrani72a, B. Safarzadeh Samani155,M. Safdari152,P. Saha120,S. Saha103,M. Sahinsoy114,A. Sahu180, M. Saimpert36,M. Saito162,T. Saito162, D. Salamani54,G. Salamanna74a,74b,A. Salnikov152,J. Salt172, A. Salvador Salas14,D. Salvatore41b,41a,F. Salvatore155,A. Salzburger36,D. Sammel52, D. Sampsonidis161,D. Sampsonidou60d,60c,J. Sánchez172,A. Sanchez Pineda66a,36,66c,H. Sandaker132, C.O. Sander46,I.G. Sanderswood89,M. Sandhoff180,C. Sandoval22b,D.P.C. Sankey142,M. Sannino55b,55a, Y. Sano116,A. Sansoni51,C. Santoni38,H. Santos138a,138b,S.N. Santpur18,A. Santra178,K.A. Saoucha148, A. Sapronov79,J.G. Saraiva138a,138d,O. Sasaki81,K. Sato167,F. Sauerburger52,E. Sauvan5, P. Savard165,ak,R. Sawada162,C. Sawyer142,L. Sawyer95, I. Sayago Galvan172,C. Sbarra23b, A. Sbrizzi66a,66c,T. Scanlon94,J. Schaarschmidt147,P. Schacht114,D. Schaefer37,L. Schaefer135, U. Schäfer99,A.C. Schaffer64,D. Schaile113,R.D. Schamberger154,E. Schanet113,C. Scharf19, N. Scharmberg100,V.A. Schegelsky136,D. Scheirich141,F. Schenck19,M. Schernau169,C. Schiavi55b,55a, L.K. Schildgen24,Z.M. Schillaci26,E.J. Schioppa67a,67b,M. Schioppa41b,41a,K.E. Schleicher52, S. Schlenker36,K. Schmieden99,C. Schmitt99,S. Schmitt46,L. Schoeffel143,A. Schoening61b, P.G. 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Zhang171,R. Zhang179, S. Zhang105,X. Zhang60c,X. Zhang60b,Z. Zhang64, P. Zhao49,Y. Zhao144,Z. Zhao60a,A. Zhemchugov79,Z. Zheng105,D. Zhong171, B. Zhou105,C. Zhou179, H. Zhou7,M. Zhou154,N. Zhou60c, Y. Zhou7,C.G. Zhu60b,C. Zhu15a,15d,H.L. Zhu60a,H. Zhu15a, J. Zhu105,Y. Zhu60a,X. Zhuang15a,K. Zhukov110,V. Zhulanov121b,121a,D. Zieminska65,N.I. Zimine79, S. Zimmermann52,*, Z. Zinonos114, M. Ziolkowski150,L. Živković16,A. Zoccoli23b,23a,K. Zoch53, T.G. Zorbas148,R. Zou37,W. Zou39,L. Zwalinski36. 1Department of Physics, University of Adelaide, Adelaide; Australia 2Physics Department, SUNY Albany, Albany NY; United States of America 3Department of Physics, University of Alberta, Edmonton AB; Canada 4(𝑎)Department of Physics, Ankara University, Ankara;(𝑏)Istanbul Aydin University, Application and Research Center for Advanced Studies, Istanbul;(𝑐)Division of Physics, TOBB University of Economics and Technology, Ankara; Turkey 5LAPP, Univ. Savoie Mont Blanc, CNRS/IN2P3, Annecy; France 6High Energy Physics Division, Argonne National Laboratory, Argonne IL; United States of America 7Department of Physics, University of Arizona, Tucson AZ; United States of America 8Department of Physics, University of Texas at Arlington, Arlington TX; United States of America 9Physics Department, National and Kapodistrian University of Athens, Athens; Greece 10 Physics Department, National Technical University of Athens, Zografou; Greece 11 Department of Physics, University of Texas at Austin, Austin TX; United States of America 12 (𝑎)Bahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul;(𝑏)Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul;(𝑐)Department of Physics, Bogazici University, Istanbul;(𝑑)Department of Physics Engineering, Gaziantep University, Gaziantep; Turkey 13 Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan 14 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona; Spain 15 (𝑎)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing;(𝑏)Physics Department, Tsinghua University, Beijing;(𝑐)Department of Physics, Nanjing University, Nanjing;(𝑑)University of Chinese Academy of Science (UCAS), Beijing; China – 39 –