Search for new physics in final states with two opposite-sign, same-flavor leptons, jets, and missing transverse momentum in pp collisions at √s = 13 TeV

JHEP12(2016)013 Published for SISSA by Springer Received:July 4, 2016 Revised:October 17, 2016 Accepted:November 20, 2016 Published:December 5, 2016 Search for new physics in final states with two opposite-sign, same-flavor leptons, jets, and missing transverse momentum in pp collisions at √s= 13 TeV The CMS collaboration E-mail: [email protected] Abstract: A search is presented for physics beyond the standard model in final states with two opposite-sign, same-flavor leptons, jets, and missing transverse momentum. The data sample corresponds to an integrated luminosity of 2.3 fb−1of proton-proton collisions at √s= 13 TeV collected with the CMS detector at the LHC in 2015. The analysis uses the invariant mass of the lepton pair, searching for a kinematic edge or a resonant-like excess compatible with the Z boson mass. Both search modes use several event categories in order to increase the sensitivity to new physics. These categories are based on the rapidity of the leptons, the multiplicity of jets and b jets, the scalar sum of jet transverse momenta, and missing transverse momentum. The observations in all signal regions are consistent with the expectations from the standard model, and the results are interpreted in the context of simplified models of supersymmetry. Keywords: Beyond Standard Model, Hadron-Hadron scattering (experiments), Supersymmetry ArXiv ePrint: 1607.00915 Open Access, Copyright CERN, for the benefit of the CMS Collaboration. Article funded by SCOAP3. doi:10.1007/JHEP12(2016)013 JHEP12(2016)013 Contents 1 Introduction 1 2 The CMS detector 2 3 Datasets, triggers, and object selection 2 4 Signal models 4 5 Signal regions 5 5.1 On-Z signal regions 5 5.2 Edge search signal regions 5 6 Standard model background predictions 6 6.1 Flavor-symmetric backgrounds 6 6.2 Drell-Yan-like backgrounds 7 6.2.1 Other standard model processes with a Z boson 9 6.2.2 Drell-Yan background in the edge search 9 7 Results 10 8 Interpretation 14 8.1 Systematic uncertainty in the signal yield 14 8.2 Interpretation using simplified models 14 9 Summary 16 The CMS collaboration 21 1 Introduction Supersymmetry (SUSY) [1–8] is one of the most appealing extensions of the standard model (SM), assuming a new fundamental symmetry that assigns a new fermion (boson) to every SM boson (fermion). SUSY resolves the hierarchy problem of the SM by stabilizing the Higgs boson mass via additional quantum loop corrections from the top super-partner (top squark), which compensate the correction due to the top quark. If R-parity [9] is conserved the lightest state predicted by the theory is stable and potentially massive, providing a candidate for Dark Matter. Many SUSY models also lead to the unification of the electroweak and strong forces at high energies. This paper presents a search for signatures of SUSY in events with two oppositesign, same-flavor leptons (electrons or muons), jets, and missing transverse momentum. A – 1 – JHEP12(2016)013 dataset of pp collisions collected with the CMS detector at the CERN LHC at a center-ofmass energy √s= 13 TeV in 2015 was used, corresponding to an integrated luminosity of 2.3 fb−1. The dilepton topology is expected to occur in SUSY models where a neutralino decays to either an on-shell Z boson or a virtual Z/γ boson which in turn decays to leptons and the lightest SUSY particle (LSP), or into a lepton and its supersymmetric partner (slepton), the latter decaying into another lepton and the LSP. Decays involving an onshell Z boson are expected to produce an excess of events compatible with the Z boson mass, while decays involving off-shell Z bosons or sleptons are expected to produce a characteristic edge shape in the invariant mass distribution of the dilepton system [10]. The CMS Collaboration published a version of this analysis using a √s= 8 TeV dataset, observing a 2.6σlocal significance excess compatible with an edge shape located at a dilepton invariant mass of 78.7±1.4 GeV [11]. The ATLAS collaboration reported the absence of any excess in a similar signal region, but observed a 3.0σexcess in dilepton events compatible with the Z boson mass [12]. Both of these excesses warrant scrutiny using the 13 TeV dataset and are analyzed here with minor changes with respect to the 8 TeV searches. 2 The CMS detector The central feature of the CMS apparatus is a superconducting solenoid, 13 m in length and 6 m in diameter, that provides an axial magnetic field of 3.8 T. The bore of the solenoid is outfitted with various particle detection systems. Charged-particle trajectories are measured by silicon pixel and strip trackers, covering 0 <φ<2πin azimuth and |η|<2.5, where the pseudorapidity ηis defined as η=−log[tan(θ/2)], with θbeing the polar angle of the trajectory of the particle with respect to the beam direction. A crystal electromagnetic calorimeter (ECAL), and a brass and scintillator hadron calorimeter surround the tracking volume. The calorimetry provides high resolution energy and direction measurements of electrons and hadronic jets. A preshower detector consisting of two planes of silicon sensors interleaved with lead is located in front of the ECAL at |η|>1.479. Muons are measured in gas-ionization detectors embedded in the steel flux-return yoke outside the solenoid. The detector is nearly hermetic, allowing for energy balance measurements in the plane transverse to the beam direction. A two-tier trigger system selects the most interesting pp collision events for use in physics analysis. A more detailed description of the CMS detector, its coordinate system, and the main kinematic variables used in the analysis can be found elsewhere [13]. 3 Datasets, triggers, and object selection Events are collected with a set of isolated dilepton triggers that require a transverse momentum pT>17 GeV for the leading lepton and pT>12 (8) GeV for the subleading electron (muon), and |η|<2.5 (2.4) for electrons (muons). In order to retain high signal efficiency, in particular for Lorentz-boosted dilepton systems, non-isolated dilepton triggers with pT>33 (27) GeV for the first electron (muon) and pT>33 (8) GeV for the second – 2 – JHEP12(2016)013 electron (muon) are also used. The trigger efficiencies are measured in data using events selected by a suite of jet triggers. Events are selected by requiring two opposite-charge, same flavor leptons (e±e∓or µ±µ∓) with pT>20 GeV and pseudorapidity |η|<2.4. The distance between the leptons is requested to be at least √∆φ2+ ∆η2= ∆R > 0.3 to avoid reconstruction efficiency differences between electrons and muons in events with very collinear leptons. This requirement is relaxed to ∆R > 0.1 when the mass of the dilepton system is consistent with a Z boson to preserve acceptance for Z bosons with large transverse momentum. To ensure symmetry in acceptance between electrons and muons, all events with one of these two leptons in the barrel-endcap transition region of the ECAL, 1.4<|η|<1.6, are rejected. A control sample of different flavor leptons (eµor µe) is defined using the same lepton selection criteria. All the parameters above have been chosen in order to maximize the lepton selection efficiency while keeping the electron and muon efficiencies similar. Electrons, reconstructed by associating tracks with ECAL clusters, are identified using a multivariate approach based on information on the cluster shape in the ECAL, track quality, and the matching between the track and the ECAL cluster [14]. Additionally, electrons from photon conversions are rejected. Muons are reconstructed from tracks found in the muon system associated with tracks in the tracker. They are identified based on the quality of the track fit and the number of associated hits in the tracking detectors. For both lepton flavors, the impact parameter with respect to the reconstructed vertex with the largest p2 Tsum of associated tracks (primary vertex) is required to be within 0.5 mm in the transverse plane and below 1 mm along the beam direction. The lepton isolation, defined as the scalar pTsum of all particle candidates, excluding the lepton itself, in a cone around the lepton, divided by the lepton pT, is required to be smaller than 0.1 (0.2) for electrons (muons). A cone-size, varying with lepton pT, is chosen to be ∆R= 0.2 for pT<50 GeV, ∆R= 10 GeV/pTfor 50 < pT<200 GeV, and ∆R= 0.05 for pT>200 GeV. A particle flow (PF) technique [15,16] is used to reconstruct particle candidates in the event. Jets are clustered from these candidates, excluding charged hadrons not associated to the primary vertex, using the anti-kTclustering algorithm [17] implemented in the FastJet package [18,19] with a distance parameter of 0.4. Each jet is required to have pT>35 GeV where the pTis corrected for non-uniform detector response and multiple collision (pileup) effects [20,21], and |η|<2.4. A jet is removed from the event if it lies within ∆R < 0.4 of any of the selected leptons. The scalar sum of all jet transverse momenta is referred to as HT. The magnitude of the negative vector pTsum of all the PF candidates is referred to as Emiss T. Corrections to the jet energy are propagated to the Emiss Tusing the procedure developed for 7 TeV data [20]. Identification of jets originating from b-quarks is performed with the combined secondary vertex algorithm, using a working point in which the typical efficiency for b quarks is around 65% and the mistagging rate for light-flavor jets is around 1.5% [22]. While the main SM backgrounds are estimated using data control samples, simulated events are used to estimate uncertainties and minor SM background components. Nextto-leading order (NLO) and next-to-NLO cross sections [23–28] are used to normalize the simulated background samples, while NLO plus next-to-leading-logarithm (NLL) calcula- – 3 – JHEP12(2016)013 tions [29] are used for the signal samples. Simulated samples of Drell-Yan (DY) production associated with jets (DY + jets), γ+ jets, V + V, and ttV (V = W,Z) events are generated with the MadGraph mc@nlo v2.2.2 event generator [23], while powheg v1 [30] is used for tt and single top quark production. The matrix element calculations performed with these generators are interfaced with pythia 8 [31] for the simulation of parton showering and hadronization. The NNPDF3.0 parton distribution functions (PDF) [32] are used for all samples. The detector response is simulated with a Geant4 model [33] of the CMS detector. The simulation of new physics signals is performed using the MadGraph5 aMC@NLO program at LO precision with up to 2 additional partons in the matrix elements calculations. Events are then interfaced with pythia 8 for fragmentation and hadronization, and simulated using the CMS fast simulation package [34]. Multiple pp interactions are superimposed on the hard collision and the simulated samples are reweighted to reflect the beam conditions. Normalization scale factors are applied to the simulated samples to account for differences between simulation and data in the trigger and reconstruction efficiencies. 4 Signal models This search targets different modes of neutralino decays into final states with two oppositesign, same-flavor leptons, jets, and Emiss Toriginating from the LSPs. In order to study these processes, two simplified models have been considered for the two search modes: one producing a resonant lepton signature through an on-shell Z boson for the “on-Z” search, and another producing an edge-like distribution in the invariant mass of the leptons, for the “edge” search. The first of these simplified models represents gauge mediated supersymmetry breaking SUSY models [35] and is referred to as the GMSB scenario. The model assumes the production of a pair of gluinos (eg) that decay into a pair of quarks (u, d, s, c, or b) and the lightest neutralino eχ0 1. This neutralino decays into an on-shell Z boson and a massless gravitino ( e G) as seen in figure 1(left). At least one of the Z bosons decays into a pair of leptons producing the signature targeted by the on-Z search. The signal model for the edge search, referred to as slepton-edge, assumes the production of a pair of bottom squarks, which decay to the next-to-lightest neutralino eχ0 2and a b-quark. Two decay modes of the eχ0 2are considered each with 50% probability. In the first one, the eχ0 2decays to a Z boson and the lightest neutralino eχ0 1, which is stable. The Z boson can be on or off-shell, depending on the mass difference between the neutralinos, and decays according to its SM branching fractions. The second one features subsequent two-body decays with an intermediate slepton e `:eχ0 2→ e `` →``eχ0 1. The masses of the sleptons (ee,eµ) are assumed degenerate and equal to the average of the eχ0 2and eχ0 1. The masses of the e b and eχ0 2are free parameters, while meχ0 1is fixed at 100 GeV. This scheme allows the position of the signal edge to vary along the invariant mass distribution according to the mass difference between the eχ0 2and eχ0 1. The mass of the eχ0 1has been chosen in such a way that the difference to the eχ0 2mass is above 50 GeV, setting the minimum possible edge position at 50 GeV. An example for one of the possible decays is shown in figure 1(right). – 4 – JHEP12(2016)013 P1 P2 eg eg eχ0 1 eχ0 1 q q e G Z Z e G q q P1 P2 e b ¯ e b eχ0 2 eχ0 2 Z(∗) e l b f f eχ0 1 eχ0 1 ℓ− ℓ+ b Figure 1. Diagrams for gluino and e b pair production and decays realized in the simplified models. The GMSB model targeted by the on-Z search is shown on the left. On the right, the sleptonedge model features characteristic edges in the m`` spectrum given by the mass difference of the eχ0 2and eχ0 1. 5 Signal regions Signal regions for the on-Z and edge searches follow two principles: first, they are designed to provide sensitivity to a range of new physics models, including the simplified models defined above, and second, they are designed to investigate excesses in the 8 TeV datasets reported by the ATLAS and CMS Collaborations [11,12]. The selections described below are applied in addition to the dilepton selection described in section 3. 5.1 On-Z signal regions The on-Z search is divided into a total of three signal region (SR) categories with dilepton invariant mass (m``) in the range 81 < m`` <101 GeV. The first two, referred to as “SRA” (2–3 jets and HT>400 GeV) and “SRB” (≥4 jets), focus on events with low and high jet multiplicity. These categories are further divided according to the number of b-tagged jets and Emiss T. One additional signal region, namely “ATLAS SR”, is defined corresponding to the region showing a 3.0σexcess in the 8 TeV dataset of the ATLAS Collaboration [12]. The selection details are specified in section 7. 5.2 Edge search signal regions The signal regions in the edge search remain largely unchanged with respect to the search performed with the 8 TeV dataset [11]. The requirements on the jet multiplicity and Emiss T are similar to the previous analysis, namely Emiss T>100 (150) GeV if at least three (two) jets are present. The relative centrality expected in the decays of heavy particles, combined with the performance of the detector in the barrel region compared to the endcaps, motivates a division of the event sample depending on the |η|of the leptons. The signal region is defined as central if both leptons lie within |η|<1.4 and as forward if at least one of the leptons is located outside of this |η|range. Furthermore, two exclusive bins are defined in the number of b-tagged jets, one without and one with at least one such jet. The improvements in the CMS reconstruction algorithms for the 13 TeV data taking lead to a few differences between the 8 and 13 TeV signal regions. The lepton identification – 5 – JHEP12(2016)013 algorithms have been updated for the 13 TeV data taking, with the most relevant improvement being the use of a new electron identification algorithm based on a multivariate discriminator [14]. The jet momentum threshold has been lowered from 40 GeV to 35 GeV given the improved pile-up rejection achieved at √s= 13 TeV, and the maximum |η|has been reduced from to 3.0 to 2.4, to match the tracker acceptance. The isolation definition has also been modified to include a variable cone size. The rejection of non-prompt leptons has been improved as a consequence of all these changes. Finally, additional non-isolated double-lepton triggers have been added to recover efficiency for very boosted dilepton systems, although the increase in efficiency for the edge signal regions has been found to be small (<4%). A counting experiment is performed in five distinct regions of the m`` spectrum with events split among the four exclusive (0 or >=1 b-tagged jet, central or forward) and two inclusive (central or forward) categories. The five mass regions include the three that were present in the 8 TeV analysis (the low-mass region: 20 < m`` <70 GeV, the on-Z region: 81 < m`` <101 GeV, and the high-mass region: m`` >120 GeV), as well as the two regions immediately adjacent to the Z peak (70 < m`` <81 GeV and 101 < m`` <120 GeV). The mass spectrum in the current analysis thus covers all m`` values above 20 GeV. In order to directly compare the result obtained at 13 TeV with those obtained at 8 TeV, results for the signal regions are also given inclusively in the number of b-tagged jets, Nb-jets ≥0. A summary of all signal regions is given along with the experimental results in section 7. 6 Standard model background predictions The backgrounds from SM processes are divided into two types. Those that produce opposite-flavor (OF) pairs (e±µ∓) as often as same-flavor (SF) pairs (µ±µ∓, e±e∓) are referred to as flavor-symmetric (FS) backgrounds. Among them, the dominant contribution arises from top quark-antitop quark production; sub-leading contributions from WW, Z/γ∗(→ττ), tW single-top quark production, and leptons from hadron decays are also present. The other category of backgrounds includes flavor-correlated lepton production and only contributes with SF leptons. The dominant contributions arise from DY production in association with jets, where the Emiss Tarises from mismeasurement of the jet energies. Smaller contributions come from WZ and ZZ production, as well as rare processes such as ttZ. These backgrounds are referred to as “Other SM” in this paper. 6.1 Flavor-symmetric backgrounds The contribution of flavor-symmetric processes in the SF channels is estimated from the OF control sample. While there is a production symmetry between the two channels at particle level, it can be distorted by the different trigger, reconstruction, and identification efficiencies for electrons and muons. The background estimate is therefore obtained from the observed OF yield by applying a multiplicative correction factor, RSF/OF. This factor is determined by two independent methods, a direct measurement in a control region enriched – 6 – JHEP12(2016)013 Central Forward Data MC Data MC (1/2)(rµ/e+r−1 µ/e) 1.01 ±0.01 1.01 ±0.01 1.02 ±0.04 1.03 ±0.05 RT1.00 ±0.07 1.02 ±0.06 1.04 ±0.09 1.04 ±0.06 RSF/OF From factorization 1.01 ±0.07 1.03 ±0.06 1.06 ±0.10 1.05 ±0.08 Direct measurement 1.05 ±0.06 1.05 ±0.03 1.10 ±0.09 1.08 ±0.04 Weighted average 1.03 ±0.05 1.04 ±0.03 1.08 ±0.07 1.07 ±0.04 Table 1. Summary of RSF/OF values obtained in data and simulation using the direct and factorized methods, and the final combination. in FS backgrounds, and from the measurement of lepton efficiencies, factorized into the effects of reconstruction, identification, and trigger. The direct measurement is performed in the region with Njets = 2 and 100 < Emiss T< 150 GeV, excluding the mass range 70 < m`` <110 GeV to reduce background contributions from resonant Z-boson production. Here, RSF/OF is evaluated using the observed yield of SF and OF events, 4RSF/OF =NSF/NOF. The applicability of this value in the signal region is confirmed by comparing it with the RSF/OF value obtained in the signal region for tt simulated events. The difference between both values is found to be smaller than its statistical uncertainty (3%). The latter value is assigned as the systematic uncertainty in the measurement. For the factorized approach, the ratio of muon to electron reconstruction and identification efficiencies, rµ/e, is measured in a DY-enriched region with Njets ≥2 and Emiss T< 50 GeV and requiring 60 < m`` <120 GeV, resulting in a large sample of e±e∓and µ±µ∓ events with similar kinematics to the signal region in terms of jet multiplicity. Assuming the factorization of lepton efficiencies in an event, the efficiency ratio is measured as rµ/e=√Nµ+µ−/Ne+e−. A systematic uncertainty of 10% (20%) is assigned to rµ/ein the central (forward) lepton rapidity selection based on studies of its dependency on the lepton kinematics, the amount of Emiss T, and the jet multiplicity. The trigger efficiencies for the three different flavor combinations are used to define the factor RT=√T µ±µ∓T e±e∓/T e±µ∓, which takes into account the difference between SF and OF channels at the trigger level. The final correction is RSF/OF = (1/2)(rµ/e+r−1 µ/e)RT. Here, rµ/eis summed with its inverse, leading to a large reduction of the associated uncertainty. The results of the direct measurement and the factorization method are shown in table 1. Since the results are in agreement and are obtained on independent data samples, they are combined using the weighted average. The resulting correction is RSF/OF = 1.03 ±0.05 (1.08 ±0.07) for the central (forward) lepton rapidity selection. 6.2 Drell-Yan-like backgrounds The Emiss Tfrom the DY background is estimated from Emiss Ttemplates obtained from a data control region. The main premise of this estimate based on data is that Emiss Tin – 7 – JHEP12(2016)013 Z + jets events originates from the limited detector resolution when measuring the objects making up the hadronic system that recoils against the Z boson. We estimate the shape of the Emiss Tdistribution from a control sample of γ+ jets events where the jet system recoils against a photon instead of a Z boson. Signal regions requiring at least one b-tagged jet can lead to a small amount of additional Emiss Tdue to the neutrinos in semileptonic b quark decays. To account for this effect, the Emiss Ttemplates are extracted from a control sample of γ+ jets events with at least one b-tagged jet. The γ+ jets events in data are selected with a suite of single-photon triggers with pT thresholds varying from 22 to 165 GeV. The triggers with thresholds below 165 GeV are prescaled such that only a fraction of accepted events are recorded, and the events are weighted by the trigger prescales to match the integrated luminosity collected with the signal dilepton triggers. In order to account for kinematic differences between the hadronic systems in the γ+jets and the Z+jets sample, the γ+jets sample is reweighted such that the boson pTdistribution matches that of the Z + jets sample. This reweighting is performed for each signal region, where the same requirements are applied to the Z + jets and the γ+ jets samples. The resulting Emiss Tdistribution is then normalized to the observed data yield in the region Emiss T<50 GeV where Z + jets is the dominant background. The control sample used to estimate this background does not need to have a high purity of photons, since the Emiss Tis assumed to originate from jet mismeasurement. However, it is required that the photon-like object be well measured so as to not contribute to the Emiss Tmismeasurement. The stability of the photon selection is tested by repeating this background measurement after tightening the photon ID requirements, and it is found that the results are consistent with the measurement done using the looser selection. In order to ensure the photon-like object is sufficiently well-measured and that the Emiss Tin the γ+ jets sample comes primarily from the mismeasurement of the jet system, the following conditions are required: ∆φ(Emiss T, γ)>0.4, a veto on events where the photon can be connected to a pattern of hits in the pixel detector, and the photon to be matched to a jet within a cone of ∆R= 0.4. The requirement ∆φ(Emiss T, γ)>0.4 protects against under-measurement of the photon energy, which is much more likely for calorimeter-based quantities than over-measurement. Finally, the electromagnetic fraction of the matched jet (fraction of jet energy deposited in the electromagnetic calorimeter with respect to the total energy deposited in both, the electromagnetic and hadronic calorimeter) is required to be >0.7. The dominant uncertainties in the Emiss Ttemplate prediction come from the limited size of the samples used. The uncertainty in the prediction takes into account the statistical uncertainty of the γ+ jets sample in the signal Emiss Tregions, which ranges from 10–50%. The statistical uncertainty of the normalization for Emiss T<50 GeV is included and ranges from 4–10%, as shown in table 2. A closure test of the method is performed in simulation, using γ+jets to predict the yield of Z+ jets. An uncertainty is assigned from the results of this test as either the largest discrepancy between the γ+ jets prediction and the Z + jets yield for each Emiss Tregion, or the MC statistical uncertainty, whichever is larger. The values are listed in table 3and vary between 4 and 50%, depending on the Emiss Tregion. Finally, the impact of photon purity on the estimate is studied in data by repeating the prediction with – 8 – JHEP12(2016)013 Source of uncertainty Uncertainty (%) Luminosity 2.7 Pileup 0–6 b tag modeling 2–20 Lepton reconstruction and isolation 2–4 Fast simulation scale factors 1–6 Trigger modeling 5 Jet energy scale 0–8 ISR modeling 1–3 Statistical uncertainty 1–20 Total uncertainty 7–32 Table 6. List of systematic uncertainties taken into account for the signal yields and typical values. [GeV] g ~ m 1000 1100 1200 1300 1400 1500 [GeV] 1 0 χ ∼m 200 400 600 800 1000 1200 1400 1600 [pb]σ95% CL upper limit on -2 10 -1 10 1 [pb]σ95% CL upper limit on -2 10 -1 10 1 exp. σ 1 ±Expected limit, theory σ 1 ±Observed limit, = 1 GeV G ~ ; mG ~ Z→ 0 1 χ, 0 1 χ 2j + → g ~ , g ~ g ~ →pp NLO+NLL exclusion (13 TeV) -1 2.3 fbCMS Figure 4. Cross section upper limits and exclusions contours at 95% CL with the results of the on-Z search interpreted in the GMSB model. The region to the left of the red dotted (black solid) line shows the masses which are excluded by the expected (observed) limit. neutralino mass of ∼250 GeV corresponds to a kinematic edge located at ∼150–200 GeV. In this case the signal is spread evenly across all mass regions, while in the case of low (high) eχ0 2masses, the majority of signal events fall into the low- (high-) mass bin, which increases the sensitivity for these mass points. The expected upper limits in the bottom squark/neutralino mass plane are similar to the limits set by the 8 TeV analysis. In two parameter regions the expected limits are slightly improved due to the introduction of new signal regions. The introduction of the below-Z and above-Z signal region increases the – 15 – JHEP12(2016)013 [GeV] b ~ m 400 500 600 700 800 900 [GeV] 0 2 χ ∼ m 200 300 400 500 600 700 800 900 [pb]σ95% CL upper limit on 1− 10 1 (13 TeV) -1 2.3 fb CMS exp. σ 1 ±Expected limit, theory σ 1 ±Observed limit, ); NLO+NLL exclusion 0 1 χ ∼ + m 0 2 χ ∼ = 0.5(m l ~ m = 100 GeV 0 1 χ ∼ l; m 0 1 χ ∼ →l ~ , 0 1 χ ∼ l/Zl ~ → 0 2 χ ∼ b, 0 2 χ ∼ →b ~ , b ~ b ~ →pp Figure 5. Cross section upper limits and exclusion contours at 95% CL with the results of the edge search interpreted in the slepton-edge model. The region to the left of the red dotted (black solid) line shows the masses which are excluded by the expected (observed) limit. sensitivity of the analysis for sbottom masses of about 550 GeV and neutralino masses of around 250 GeV. The second improvement is the categorization according to the number of b-tagged jets that gives additional sensitivity close to the sbottom and neutralino mass diagonal where events with zero b-tagged jets become important since the produced b jets have less energy and are often not identified. The observed upper limits in the region with small neutralino masses have been largely improved with respect to the 8 TeV results from 500 to approximately 620 GeV. 9 Summary A search for physics beyond the standard model has been presented in the opposite-sign, same-flavor lepton final state using a data sample of pp collisions collected at a centerof-mass energy of 13 TeV, corresponding to an integrated luminosity of 2.3 fb−1, recorded with the CMS detector in 2015. Searches are performed for signals that either produce a kinematic edge, or a peak at the Z boson mass, in the dilepton invariant mass distribution. Comparing the observation to estimates for SM backgrounds obtained from data control samples, no statistically significant evidence for a signal has been observed. Notably, this is true for the two event selections where excesses of 2.6 and 3.0σsignificance had been observed by the CMS and ATLAS collaborations in their respective 8 TeV results [11,12]. The search for events containing an on-shell Z boson is interpreted in a model of gauge-mediated supersymmetry breaking, where the Z bosons are produced in decay chains initiated through gluino pair production, and where the branching ratios have been fixed – 16 – JHEP12(2016)013 to 100% to produce the desired topology. Gluino masses below 1.28 TeV for high neutralino masses and 1.03 TeV for low neutralino masses have been excluded, extending the previous exclusion limits derived from a similar analysis at 8 TeV by almost 200 GeV. The search for an edge is interpreted in a simplified model based on bottom squark pair production, where dilepton mass edges are produced in decay chains containing the two lightest neutralinos and a slepton, where again the branching ratios have been fixed to produce the desired topology. Bottom squark masses below 550 and 620 GeV have been excluded, depending on the eχ0 2mass. These limits are similar to previous exclusion limits except for low eχ0 2masses where the excluded limits have been extended by about 100 GeV. Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia); MESTD (Serbia); SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.). Individuals have received support from the Marie-Curie program and the European Research Council and EPLANET (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation `a la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the OPUS program contract 2014/13/B/ST2/02543 and contract Sonata-bis DEC2012/07/E/ST2/01406 of the National Science Center (Poland); the Thalis and Aristeia – 17 – JHEP12(2016)013 programs cofinanced by EU-ESF and the Greek NSRF; the National Priorities Research Program by Qatar National Research Fund; the Programa Clar´ın-COFUND del Principado de Asturias; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); and the Welch Foundation, contract C-1845. 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Petkov Beihang University, Beijing, China W. Fang5 Institute of High Energy Physics, Beijing, China M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen6, T. Cheng, C.H. Jiang, D. Leggat, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang, Z. Wang, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China Y. Ban, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu Universidad de Los Andes, Bogota, Colombia C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, J.P. Gomez, C.F. Gonz´alez Hern´andez, J.D. Ruiz Alvarez, J.C. Sanabria University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano University of Split, Faculty of Science, Split, Croatia Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia V. Brigljevic, D. Ferencek, K. Kadija, S. Micanovic, L. Sudic University of Cyprus, Nicosia, Cyprus A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski – 22 – JHEP12(2016)013 Charles University, Prague, Czech Republic M. Finger7, M. Finger Jr.7 Universidad San Francisco de Quito, Quito, Ecuador E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt Y. Assran8,9, T. Elkafrawy10, A. Ellithi Kamel11, A. Mahrous12 National Institute of Chemical Physics and Biophysics, Tallinn, Estonia B. Calpas, M. Kadastik, M. Murumaa, L. Perrini, M. Raidal, A. Tiko, C. Veelken Department of Physics, University of Helsinki, Helsinki, Finland P. Eerola, J. Pekkanen, M. Voutilainen Helsinki Institute of Physics, Helsinki, Finland J. H¨ark¨onen, V. Karim¨aki, R. Kinnunen, T. Lamp´en, K. Lassila-Perini, S. Lehti, T. Lind´en, P. Luukka, T. Peltola, J. Tuominiemi, E. Tuovinen, L. Wendland Lappeenranta University of Technology, Lappeenranta, Finland J. Talvitie, T. 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Grab, C. Heidegger, D. Hits, J. Hoss, G. Kasieczka, P. Lecomte†, W. Lustermann, B. Mangano, M. Marionneau, P. Martinez Ruiz del Arbol, M. Masciovecchio, M.T. Meinhard, D. Meister, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pandolfi, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, M. Quittnat, M. Rossini, M. Sch¨onenberger, A. Starodumov48, M. Takahashi, V.R. Tavolaro, K. Theofilatos, R. Wallny Universit¨at Z¨urich, Zurich, Switzerland T.K. Aarrestad, C. Amsler49, L. Caminada, M.F. Canelli, V. Chiochia, A. De Cosa, C. Galloni, A. Hinzmann, T. Hreus, B. Kilminster, C. Lange, J. Ngadiuba, D. Pinna, G. Rauco, P. Robmann, D. Salerno, Y. Yang National Central University, Chung-Li, Taiwan V. Candelise, T.H. Doan, Sh. Jain, R. Khurana, M. Konyushikhin, C.M. Kuo, W. Lin, Y.J. Lu, A. Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei, Taiwan Arun Kumar, P. Chang, Y.H. Chang, Y.W. Chang, Y. Chao, K.F. Chen, P.H. Chen, C. Dietz, F. Fiori, W.-S. Hou, Y. Hsiung, Y.F. Liu, R.-S. Lu, M. Mi˜nano Moya, E. Paganis, A. Psallidas, J.f. Tsai, Y.M. Tzeng – 31 – JHEP12(2016)013 Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok, Thailand B. Asavapibhop, G. Singh, N. Srimanobhas, N. Suwonjandee Cukurova University, Adana, Turkey A. Adiguzel, S. Cerci50, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I. Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, E. Gurpinar, I. Hos, E.E. Kangal51, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut52, K. Ozdemir53, D. Sunar Cerci50, B. Tali50, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department, Ankara, Turkey B. Bilin, S. Bilmis, B. Isildak54, G. Karapinar55, M. Yalvac, M. Zeyrek Bogazici University, Istanbul, Turkey E. G¨ulmez, M. Kaya56, O. Kaya57, E.A. Yetkin58, T. Yetkin59 Istanbul Technical University, Istanbul, Turkey A. Cakir, K. Cankocak, S. Sen60 Institute for Scintillation Materials of National Academy of Science of Ukraine, Kharkov, Ukraine B. Grynyov National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov, Ukraine L. Levchuk, P. Sorokin University of Bristol, Bristol, United Kingdom R. Aggleton, F. Ball, L. Beck, J.J. Brooke, D. Burns, E. Clement, D. Cussans, H. Flacher, J. Goldstein, M. Grimes, G.P. Heath, H.F. Heath, J. Jacob, L. Kreczko, C. Lucas, D.M. Newbold61, S. Paramesvaran, A. Poll, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith Rutherford Appleton Laboratory, Didcot, United Kingdom K.W. Bell, A. Belyaev62, C. Brew, R.M. Brown, L. Calligaris, D. Cieri, D.J.A. Cockerill, J.A. Coughlan, K. Harder, S. Harper, E. Olaiya, D. Petyt, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams Imperial College, London, United Kingdom M. Baber, R. Bainbridge, O. Buchmuller, A. Bundock, D. Burton, S. Casasso, M. Citron, D. Colling, L. Corpe, P. Dauncey, G. Davies, A. De Wit, M. Della Negra, P. Dunne, A. Elwood, D. Futyan, Y. Haddad, G. Hall, G. Iles, R. Lane, C. Laner, R. Lucas61, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, J. Nash, A. Nikitenko48, J. Pela, B. Penning, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, C. Seez, A. Tapper, K. Uchida, M. Vazquez Acosta63, T. Virdee14, S.C. Zenz Brunel University, Uxbridge, United Kingdom J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, D. Leslie, I.D. Reid, P. Symonds, L. Teodorescu, M. Turner – 32 – JHEP12(2016)013 Baylor University, Waco, U.S.A. A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika The University of Alabama, Tuscaloosa, U.S.A. O. Charaf, S.I. Cooper, C. Henderson, P. Rumerio Boston University, Boston, U.S.A. D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J. Rohlf, L. Sulak, D. Zou Brown University, Providence, U.S.A. G. Benelli, E. Berry, D. Cutts, A. Garabedian, J. Hakala, U. Heintz, J.M. Hogan, O. Jesus, E. Laird, G. Landsberg, Z. Mao, M. Narain, S. Piperov, S. Sagir, E. Spencer, R. Syarif University of California, Davis, Davis, U.S.A. R. Breedon, G. Breto, D. Burns, M. Calderon De La Barca Sanchez, S. Chauhan, M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, M. Gardner, W. Ko, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, F. Ricci-Tam, S. Shalhout, J. Smith, M. Squires, D. Stolp, M. Tripathi, S. Wilbur, R. Yohay University of California, Los Angeles, U.S.A. R. Cousins, P. Everaerts, A. Florent, J. Hauser, M. Ignatenko, D. Saltzberg, E. Takasugi, V. Valuev, M. Weber University of California, Riverside, Riverside, U.S.A. K. Burt, R. Clare, J. Ellison, J.W. Gary, G. Hanson, J. Heilman, P. Jandir, E. Kennedy, F. Lacroix, O.R. Long, M. Malberti, M. Olmedo Negrete, M.I. Paneva, A. Shrinivas, H. Wei, S. Wimpenny, B. R. Yates University of California, San Diego, La Jolla, U.S.A. J.G. Branson, G.B. Cerati, S. Cittolin, M. Derdzinski, R. Gerosa, A. Holzner, D. Klein, V. Krutelyov, J. Letts, I. Macneill, D. Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon, M. Tadel, A. Vartak, S. Wasserbaech64, C. Welke, J. Wood, F. W¨urthwein, A. Yagil, G. Zevi Della Porta University of California, Santa Barbara, Santa Barbara, U.S.A. R. Bhandari, J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta, K. Flowers, M. Franco Sevilla, P. Geffert, C. George, F. Golf, L. Gouskos, J. Gran, R. Heller, J. Incandela, N. Mccoll, S.D. Mullin, A. Ovcharova, J. Richman, D. Stuart, I. Suarez, C. West, J. Yoo California Institute of Technology, Pasadena, U.S.A. D. Anderson, A. Apresyan, J. Bendavid, A. Bornheim, J. Bunn, Y. Chen, J. Duarte, A. Mott, H.B. Newman, C. Pena, M. Spiropulu, J.R. Vlimant, S. Xie, R.Y. Zhu Carnegie Mellon University, Pittsburgh, U.S.A. M.B. Andrews, V. Azzolini, B. Carlson, T. Ferguson, M. Paulini, J. Russ, M. Sun, H. Vogel, I. Vorobiev – 33 – JHEP12(2016)013 University of Colorado Boulder, Boulder, U.S.A. J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, T. Mulholland, K. Stenson, S.R. Wagner Cornell University, Ithaca, U.S.A. J. Alexander, J. Chaves, J. Chu, S. Dittmer, K. Mcdermott, N. Mirman, G. Nicolas Kaufman, J.R. Patterson, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M. Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fairfield University, Fairfield, U.S.A. D. Winn Fermi National Accelerator Laboratory, Batavia, U.S.A. S. Abdullin, M. Albrow, G. Apollinari, S. Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, G. Bolla, K. Burkett, J.N. Butler, H.W.K. Cheung, F. Chlebana, S. Cihangir, M. Cremonesi, V.D. Elvira, I. Fisk, J. Freeman, E. Gottschalk, L. Gray, D. Green, S. Gr¨unendahl, O. Gutsche, D. Hare, R.M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima, B. Kreis, S. Lammel, J. Linacre, D. Lincoln, R. Lipton, T. Liu, R. Lopes De S´a, J. Lykken, K. Maeshima, N. Magini, J.M. Marraffino, S. Maruyama, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, C. Newman-Holmes†, V. O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L. Ristori, E. Sexton-Kennedy, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber, A. Whitbeck University of Florida, Gainesville, U.S.A. D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, A. Carnes, M. Carver, D. Curry, S. Das, R.D. Field, I.K. Furic, J. Konigsberg, A. Korytov, P. Ma, K. Matchev, H. Mei, P. Milenovic65, G. Mitselmakher, D. Rank, L. Shchutska, D. Sperka, L. Thomas, J. Wang, S. Wang, J. Yelton Florida International University, Miami, U.S.A. S. Linn, P. Markowitz, G. Martinez, J.L. Rodriguez Florida State University, Tallahassee, U.S.A. A. Ackert, J.R. Adams, T. Adams, A. Askew, S. Bein, B. Diamond, S. Hagopian, V. Hagopian, K.F. Johnson, A. Khatiwada, H. Prosper, A. Santra, M. Weinberg Florida Institute of Technology, Melbourne, U.S.A. M.M. Baarmand, V. Bhopatkar, S. Colafranceschi66, M. Hohlmann, D. Noonan, T. Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, U.S.A. M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, I. Bucinskaite, R. Cavanaugh, O. Evdokimov, L. Gauthier, C.E. Gerber, D.J. Hofman, P. Kurt, C. O’Brien, I.D. Sandoval Gonzalez, P. Turner, N. Varelas, H. Wang, Z. Wu, M. Zakaria, J. Zhang – 34 – JHEP12(2016)013 The University of Iowa, Iowa City, U.S.A. B. Bilki67, W. Clarida, K. Dilsiz, S. Durgut, R.P. Gandrajula, M. Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya68, A. Mestvirishvili, A. Moeller, J. Nachtman, H. Ogul, Y. Onel, F. Ozok69, A. Penzo, C. Snyder, E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, U.S.A. I. Anderson, B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan, P. Maksimovic, M. Osherson, J. Roskes, U. Sarica, M. Swartz, M. Xiao, Y. Xin, C. You The University of Kansas, Lawrence, U.S.A. A. Al-bataineh, P. Baringer, A. Bean, J. Bowen, C. Bruner, J. Castle, R.P. Kenny III, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, S. Sanders, R. Stringer, J.D. Tapia Takaki, Q. Wang Kansas State University, Manhattan, U.S.A. A. Ivanov, K. Kaadze, S. Khalil, M. Makouski, Y. Maravin, A. Mohammadi, L.K. Saini, N. Skhirtladze, S. Toda Lawrence Livermore National Laboratory, Livermore, U.S.A. D. Lange, F. Rebassoo, D. Wright University of Maryland, College Park, U.S.A. C. Anelli, A. Baden, O. Baron, A. Belloni, B. Calvert, S.C. Eno, C. Ferraioli, J.A. Gomez, N.J. Hadley, S. Jabeen, R.G. Kellogg, T. Kolberg, J. Kunkle, Y. Lu, A.C. Mignerey, Y.H. Shin, A. Skuja, M.B. Tonjes, S.C. Tonwar Massachusetts Institute of Technology, Cambridge, U.S.A. D. Abercrombie, B. Allen, A. Apyan, R. Barbieri, A. Baty, R. Bi, K. Bierwagen, S. Brandt, W. Busza, I.A. Cali, Z. Demiragli, L. Di Matteo, G. Gomez Ceballos, M. Goncharov, D. Hsu, Y. Iiyama, G.M. Innocenti, M. Klute, D. Kovalskyi, K. Krajczar, Y.S. Lai, Y.-J. Lee, A. Levin, P.D. Luckey, A.C. Marini, C. Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, J. Salfeld-Nebgen, G.S.F. Stephans, K. Sumorok, K. Tatar, M. Varma, D. Velicanu, J. Veverka, J. Wang, T.W. Wang, B. Wyslouch, M. Yang, V. Zhukova University of Minnesota, Minneapolis, U.S.A. A.C. Benvenuti, R.M. Chatterjee, A. Evans, A. Finkel, A. Gude, P. Hansen, S. Kalafut, S.C. Kao, Y. Kubota, Z. Lesko, J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack, N. Tambe, J. Turkewitz University of Mississippi, Oxford, U.S.A. J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, U.S.A. E. Avdeeva, R. Bartek, K. Bloom, S. Bose, D.R. Claes, A. Dominguez, C. Fangmeier, R. Gonzalez Suarez, R. Kamalieddin, D. Knowlton, I. Kravchenko, A. Malta Rodrigues, F. Meier, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger – 35 – JHEP12(2016)013 State University of New York at Buffalo, Buffalo, U.S.A. M. Alyari, J. Dolen, J. George, A. Godshalk, C. Harrington, I. Iashvili, J. Kaisen, A. Kharchilava, A. Kumar, A. Parker, S. Rappoccio, B. Roozbahani Northeastern University, Boston, U.S.A. G. Alverson, E. Barberis, D. Baumgartel, A. Hortiangtham, A. Massironi, D.M. Morse, D. Nash, T. Orimoto, R. Teixeira De Lima, D. Trocino, R.-J. Wang, D. Wood Northwestern University, Evanston, U.S.A. S. Bhattacharya, K.A. Hahn, A. Kubik, J.F. Low, N. Mucia, N. Odell, B. Pollack, M.H. Schmitt, K. Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, U.S.A. N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N. Kellams, K. Lannon, N. Marinelli, F. Meng, C. Mueller, Y. Musienko36, M. Planer, A. Reinsvold, R. Ruchti, G. Smith, S. Taroni, N. Valls, M. Wayne, M. Wolf, A. Woodard The Ohio State University, Columbus, U.S.A. J. Alimena, L. Antonelli, J. Brinson, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, A. Hart, C. Hill, R. Hughes, W. Ji, B. Liu, W. Luo, D. Puigh, B.L. Winer, H.W. Wulsin Princeton University, Princeton, U.S.A. S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, J. Luo, D. Marlow, T. Medvedeva, M. Mooney, J. Olsen, C. Palmer, P. Pirou´e, D. Stickland, C. Tully, A. Zuranski University of Puerto Rico, Mayaguez, U.S.A. S. Malik Purdue University, West Lafayette, U.S.A. A. Barker, V.E. Barnes, D. Benedetti, S. Folgueras, L. Gutay, M.K. Jha, M. Jones, A.W. Jung, K. Jung, D.H. Miller, N. Neumeister, B.C. Radburn-Smith, X. Shi, J. Sun, A. Svyatkovskiy, F. Wang, W. Xie, L. Xu Purdue University Calumet, Hammond, U.S.A. N. Parashar, J. Stupak Rice University, Houston, U.S.A. A. Adair, B. Akgun, Z. Chen, K.M. Ecklund, F.J.M. Geurts, M. Guilbaud, W. Li, B. Michlin, M. Northup, B.P. Padley, R. Redjimi, J. Roberts, J. Rorie, Z. Tu, J. Zabel University of Rochester, Rochester, U.S.A. B. Betchart, A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, T. Ferbel, M. Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M. Verzetti Rutgers, The State University of New Jersey, Piscataway, U.S.A. J.P. Chou, E. Contreras-Campana, Y. Gershtein, T.A. G´omez Espinosa, E. Halkiadakis, M. Heindl, D. Hidas, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, – 36 – JHEP12(2016)013 A. Lath, K. Nash, H. Saka, S. Salur, S. Schnetzer, D. Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker University of Tennessee, Knoxville, U.S.A. M. Foerster, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa Texas A&M University, College Station, U.S.A. O. Bouhali70, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, E. Juska, T. Kamon71, R. Mueller, Y. Pakhotin, R. Patel, A. Perloff, L. Perni`e, D. Rathjens, A. Rose, A. Safonov, A. Tatarinov, K.A. Ulmer Texas Tech University, Lubbock, U.S.A. N. Akchurin, C. Cowden, J. Damgov, C. Dragoiu, P.R. Dudero, J. Faulkner, S. Kunori, K. Lamichhane, S.W. Lee, T. Libeiro, S. Undleeb, I. Volobouev, Z. Wang Vanderbilt University, Nashville, U.S.A. A.G. Delannoy, S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, P. Sheldon, S. Tuo, J. Velkovska, Q. Xu University of Virginia, Charlottesville, U.S.A. M.W. Arenton, P. Barria, B. Cox, J. Goodell, R. Hirosky, A. Ledovskoy, H. Li, C. Neu, T. Sinthuprasith, X. Sun, Y. Wang, E. Wolfe, F. Xia Wayne State University, Detroit, U.S.A. C. Clarke, R. Harr, P.E. Karchin, P. Lamichhane, J. Sturdy University of Wisconsin - Madison, Madison, WI, U.S.A. D.A. Belknap, S. Dasu, L. Dodd, S. Duric, B. Gomber, M. Grothe, M. Herndon, A. Herv´e, P. Klabbers, A. Lanaro, A. Levine, K. Long, R. Loveless, I. Ojalvo, T. Perry, G.A. Pierro, G. Polese, T. Ruggles, A. Savin, A. Sharma, N. Smith, W.H. Smith, D. Taylor, N. Woods †: Deceased 1: Also at Vienna University of Technology, Vienna, Austria 2: Also at State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China 3: Also at Institut Pluridisciplinaire Hubert Curien, Universit´e de Strasbourg, Universit´e de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France 4: Also at Universidade Estadual de Campinas, Campinas, Brazil 5: Also at Universit´e Libre de Bruxelles, Bruxelles, Belgium 6: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany 7: Also at Joint Institute for Nuclear Research, Dubna, Russia 8: Also at Suez University, Suez, Egypt 9: Now at British University in Egypt, Cairo, Egypt 10: Also at Ain Shams University, Cairo, Egypt 11: Also at Cairo University, Cairo, Egypt 12: Now at Helwan University, Cairo, Egypt 13: Also at Universit´e de Haute Alsace, Mulhouse, France 14: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland – 37 – JHEP12(2016)013 15: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia 16: Also at Tbilisi State University, Tbilisi, Georgia 17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany 18: Also at University of Hamburg, Hamburg, Germany 19: Also at Brandenburg University of Technology, Cottbus, Germany 20: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary 21: Also at MTA-ELTE Lend¨ulet CMS Particle and Nuclear Physics Group, E¨otv¨os Lor´and University, Budapest, Hungary 22: Also at University of Debrecen, Debrecen, Hungary 23: Also at Indian Institute of Science Education and Research, Bhopal, India 24: Also at Institute of Physics, Bhubaneswar, India 25: Also at University of Visva-Bharati, Santiniketan, India 26: Also at University of Ruhuna, Matara, Sri Lanka 27: Also at Isfahan University of Technology, Isfahan, Iran 28: Also at University of Tehran, Department of Engineering Science, Tehran, Iran 29: Also at Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran 30: Also at Universit`a degli Studi di Siena, Siena, Italy 31: Also at Purdue University, West Lafayette, U.S.A. 32: Also at International Islamic University of Malaysia, Kuala Lumpur, Malaysia 33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia 34: Also at Consejo Nacional de Ciencia y Tecnolog´ıa, Mexico city, Mexico 35: Also at Warsaw University of Technology, Institute of Electronic Systems, Warsaw, Poland 36: Also at Institute for Nuclear Research, Moscow, Russia 37: Now at National Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI), Moscow, Russia 38: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia 39: Also at University of Florida, Gainesville, U.S.A. 40: Also at P.N. Lebedev Physical Institute, Moscow, Russia 41: Also at California Institute of Technology, Pasadena, U.S.A. 42: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia 43: Also at INFN Sezione di Roma; Universit`a di Roma, Roma, Italy 44: Also at National Technical University of Athens, Athens, Greece 45: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy 46: Also at National and Kapodistrian University of Athens, Athens, Greece 47: Also at Riga Technical University, Riga, Latvia 48: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia 49: Also at Albert Einstein Center for Fundamental Physics, Bern, Switzerland 50: Also at Adiyaman University, Adiyaman, Turkey 51: Also at Mersin University, Mersin, Turkey 52: Also at Cag University, Mersin, Turkey 53: Also at Piri Reis University, Istanbul, Turkey 54: Also at Ozyegin University, Istanbul, Turkey 55: Also at Izmir Institute of Technology, Izmir, Turkey 56: Also at Marmara University, Istanbul, Turkey 57: Also at Kafkas University, Kars, Turkey 58: Also at Istanbul Bilgi University, Istanbul, Turkey – 38 – JHEP12(2016)013 59: Also at Yildiz Technical University, Istanbul, Turkey 60: Also at Hacettepe University, Ankara, Turkey 61: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom 62: Also at School of Physics and Astronomy, University of Southampton, Southampton, United Kingdom 63: Also at Instituto de Astrof´ısica de Canarias, La Laguna, Spain 64: Also at Utah Valley University, Orem, U.S.A. 65: Also at University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia 66: Also at Facolt`a Ingegneria, Universit`a di Roma, Roma, Italy 67: Also at Argonne National Laboratory, Argonne, U.S.A. 68: Also at Erzincan University, Erzincan, Turkey 69: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey 70: Also at Texas A&M University at Qatar, Doha, Qatar 71: Also at Kyungpook National University, Daegu, Korea – 39 –