Search for supersymmetry in events with b-tagged jets and missing transverse momentum in pp collisions at √s = 13 TeV with the ATLAS detector

JHEP11(2017)195 Published for SISSA by Springer Received:August 31, 2017 Revised:October 20, 2017 Accepted:November 21, 2017 Published:November 29, 2017 Search for supersymmetry in events with b-tagged jets and missing transverse momentum in pp collisions at √s= 13 TeV with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: A search for the supersymmetric partners of the Standard Model bottom and top quarks is presented. The search uses 36.1 fb−1of pp collision data at √s= 13 TeV collected by the ATLAS experiment at the Large Hadron Collider. Direct production of pairs of bottom and top squarks (¯ b1and ¯ t1) is searched for in final states with b-tagged jets and missing transverse momentum. Distinctive selections are defined with either no charged leptons (electrons or muons) in the final state, or one charged lepton. The zero-lepton selection targets models in which the ¯ b1is the lightest squark and decays via ¯ b1→b¯χ0 1, where ¯χ0 1is the lightest neutralino. The one-lepton final state targets models where bottom or top squarks are produced and can decay into multiple channels, ¯ b1→b¯χ0 1and ¯ b1→t¯χ± 1, or ¯ t1→t¯χ0 1and ¯ t1→b¯χ± 1, where ¯χ± 1is the lightest chargino and the mass difference m¯χ± 1−m¯χ0 1is set to 1 GeV. No excess above the expected Standard Model background is observed. Exclusion limits at 95% confidence level on the mass of third-generation squarks are derived in various supersymmetry-inspired simplified models. Keywords: Hadron-Hadron scattering (experiments) ArXiv ePrint: 1708.09266 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP11(2017)195 JHEP11(2017)195 Contents 1 Introduction 1 2 ATLAS detector 2 3 Data and simulated event samples 3 4 Event reconstruction 5 5 Event selection 7 5.1 Discriminating variables 7 5.2 Zero-lepton channel selections 10 5.3 One-lepton channel selections 11 6 Background estimation 12 6.1 Background estimation in the zero-lepton signal regions 13 6.2 Background estimation in the one-lepton signal regions 16 6.3 Validation regions 16 7 Systematic uncertainties 18 8 Results and interpretation 21 9 Conclusion 26 The ATLAS collaboration 33 1 Introduction Supersymmetry (SUSY) [1–6] provides an extension of the Standard Model (SM) that solves the hierarchy problem [7–10] by introducing partners of the known bosons and fermions. In the framework of R-parity-conserving models, SUSY particles are produced in pairs and the lightest supersymmetric particle (LSP) is stable, providing a possible candidate for dark matter [11,12]. In a large variety of models the LSP is the lightest neutralino (˜χ0 1). Naturalness considerations [13,14] suggest that the supersymmetric partners of the thirdgeneration SM quarks are the lightest coloured supersymmetric particles. This may lead to the lightest bottom squark (˜ b1) and top squark (˜ t1) mass eigenstates1being significantly lighter than the other squarks and the gluinos. As a consequence, ˜ b1and ˜ t1could be pair-produced with relatively large cross-sections at the Large Hadron Collider (LHC). 1Scalar partners of the left-handed and right-handed chiral components of the bottom quark (˜ bL,R) or top quark (˜ tL,R) mix to form mass eigenstates for which ˜ b1and ˜ t1are defined as the lighter of the two. – 1 – JHEP11(2017)195 This paper presents a search for the direct pair production of bottom and top squarks decaying into final states with jets, two of them originating from the fragmentation of bquarks (b-jets), and missing transverse momentum (pmiss T, whose magnitude is referred to as Emiss T). The dataset analysed corresponds to 36.1 fb−1of proton-proton (pp) collisions data at √s= 13 TeV collected by the ATLAS experiment during Run 2 of the LHC in 2015 and 2016. The third-generation squarks are assumed to decay to the lightest neutralino (LSP) directly or through one intermediate stage. The search is based on simplified models inspired by the minimal supersymmetric extension of the SM (MSSM) [15–17], where the ˜ b1exclusively decays as ˜ b1→b˜χ0 1or where two decay modes for the bottom (top) squark are allowed and direct decays to the LSP, ˜ b1→b˜χ0 1(˜ t1→t˜χ0 1) compete with decays via an intermediate chargino (˜χ± 1) state, ˜ b1→t˜χ± 1(˜ t1→b˜χ± 1). In this case it is assumed that the ˜χ± 1is the next-to-lightest supersymmetric particle (NLSP) and is almost degenerate with ˜χ0 1, such that other decay products are too low in momentum to be efficiently reconstructed. The first set of models lead to final-state events from bottom squark pair production characterized by the presence of two b-jets, Emiss Tand no charged leptons (`=e, µ), referred to as the zero-lepton channel (figure 1a). For mixed decays models (intended as models where both direct decays and decays through an intermediate stage are kinematically allowed), the final state of bottom or top squark pair production depends on the branching ratios of the competing decay modes. If the decay modes are equally probable, a large fraction of the signal events are characterized by the presence of a top quark, a bottom quark, and neutralinos. Hadronic decays of the top quark are targeted by the zero-lepton channel, whilst novel dedicated selections requiring one charged lepton, two b-jets and Emiss Tare developed for semi-leptonic decays of the top quark, referred to as the one-lepton channel (figure 1b). A statistical combination of the two channels is performed when interpreting the results in terms of exclusion limits on the third-generation squark masses. Previous searches for the exclusive decay ˜ b1→b˜χ0 1with the √s= 13 TeV LHC Run-2 dataset at ATLAS and CMS have set exclusion limits at 95% confidence level (CL) on ˜ b1masses in such scenarios [18,19]. Searches in the context of mixed-decay models were performed only by ATLAS using the Run-1 √s= 8 TeV dataset and resulted in exclusion limits on the third-generation squark mass that depend on the branching ratios of the competing decay modes [20]. 2 ATLAS detector The ATLAS detector [21] is a multi-purpose particle physics detector with a forwardbackward symmetric cylindrical geometry and nearly 4πcoverage in solid angle.2The 2ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point in the centre of the detector. The positive x-axis is defined by the direction from the interaction point to the centre of the LHC ring, with the positive y-axis pointing upwards, while the beam direction defines the z-axis. Cylindrical coordinates (r, φ) are used in the transverse plane, φbeing the azimuthal angle around the z-axis. The pseudorapidity ηis defined in terms of the polar angle θby η=−ln tan(θ/2). Rapidity is defined as y= 0.5 ln[(E+pz)/(E−pz)] where Edenotes the energy and pzis the component of the momentum along the beam direction. – 2 – JHEP11(2017)195 (a) (b) Figure 1. Diagrams illustrating the most relevant signal scenarios considered for the pair production of bottom and top squarks targeted by the (a) zero-lepton and (b) one-lepton channel selections. In (a) bottom squarks decay to a bottom quark and the lightest neutralino. In (b), decays via intermediate charginos are kinematically available and compete. If the mass difference ∆m(˜χ± 1,˜χ0 1) is small, the Wbosons from chargino decays are off-shell. inner tracking detector consists of pixel and silicon microstrip detectors covering the pseudorapidity region |η|<2.5, surrounded by a transition radiation tracker which enhances electron identification in the region |η|<2.0. Between Run 1 and Run 2, a new inner pixel layer, the insertable B-layer [22], was added at a mean sensor radius of 3.3 cm. The inner detector is surrounded by a thin superconducting solenoid providing an axial 2 T magnetic field and by a fine-granularity lead/liquid-argon (LAr) electromagnetic calorimeter covering |η|<3.2. A steel/scintillator-tile calorimeter provides hadronic coverage in the central pseudorapidity range (|η|<1.7). The endcap and forward regions (1.5<|η|<4.9) of the hadronic calorimeter are made of LAr active layers with either copper or tungsten as the absorber material. An extensive muon spectrometer with an air-core toroidal magnet system surrounds the calorimeters. Three layers of high-precision tracking chambers provide coverage in the range |η|<2.7, while dedicated fast chambers allow triggering in the region |η|<2.4. The ATLAS trigger system consists of a hardware-based level-1 trigger followed by a software-based high-level trigger [23]. 3 Data and simulated event samples The data used in this analysis were collected by the ATLAS detector in pp collisions at the LHC with a centre-of-mass energy of 13 TeV and a 25 ns proton bunch crossing interval during 2015 and 2016. The full dataset corresponds to an integrated luminosity of 36.1 fb−1after requiring that all detector subsystems were operational during data recording. The uncertainty in the combined 2015+2016 integrated luminosity is 3.2%. It is derived following a methodology similar to that detailed in ref. [24] from a preliminary calibration of the luminosity scale using x–ybeam-separation scans performed in August 2015 and May 2016. Each event includes on average 13.7 and 24.9 inelastic pp collisions (“pile-up”) in the same bunch crossing in the 2015 and 2016 dataset, respectively. In the zero-lepton channel, events are required to pass an Emiss Ttrigger [25]. This trigger is – 3 – JHEP11(2017)195 fully efficient for events passing the preselection defined in section 5, which requires the offline reconstructed Emiss Tto exceed 200 GeV. Events in the one-lepton channel, as well as events used for control regions, are selected online by a trigger requiring the presence of one electron or muon. The online selection thresholds are such that a plateau of the efficiency is reached for charged-lepton transverse momenta of 27 GeV and above. Monte Carlo (MC) samples of simulated events are used to model the signal and to aid in the estimation of SM background processes, except multijet processes, which are estimated from data only. All simulated samples were produced using the ATLAS simulation infrastructure [26] using GEANT4 [27], or a faster simulation [28] based on a parameterization of the calorimeter response and GEANT4 for the other detector systems. The simulated events are reconstructed with the same algorithm as that used for data. SUSY signal samples were generated with MadGraph5 aMC@NLO [29] v2.2.3 at leading order (LO) and interfaced to Pythia v8.186 [30] with the A14 [31] set of tuned parameters (tune) for the modelling of the parton showering (PS), hadronization and underlying event. The matrix element (ME) calculation was performed at tree level and includes the emission of up to two additional partons. The ME-PS matching was done using the CKKW-L [32] prescription, with a matching scale set to one quarter of the thirdgeneration squark mass. The NNPDF23LO [33] parton distribution function (PDF) set was used. The cross-sections used to evaluate the signal yields are calculated to next-toleading-order (NLO) accuracy in the strong coupling constant, adding the resummation of soft gluon emission at next-to-leading-logarithmic accuracy (NLO+NLL) [34–36]. The nominal cross-section and uncertainty are taken as the midpoint and half-width of an envelope of cross-section predictions using different PDF sets and factorization and renormalization scales, as described in ref. [37]. SM background samples were simulated using different MC event generator programs depending on the process. The generation of t¯ twas performed by the Powheg-Box [38] v2 generator with the CT10 [39] PDF set for the matrix element calculations. Singletop-quark events in the W t,s-, and t−channels were generated using the PowhegBox v1 generator. For all processes involving top quarks, top quark spin correlations were preserved. The parton shower, fragmentation and the underlying event were simulated using Pythia v6.428 [40] with the CTEQ6L1 PDF set and the Perugia 2012 [41] tune for the underlying event. The hdamp parameter in Powheg, which controls the pTof the first additional emission beyond the Born level and thus regulates the pTof the recoil emission against the t¯ tsystem, was set to the mass of the top quark (mt= 172.5 GeV). All events with at least one leptonically decaying Wboson are retained. Fully hadronic t¯ tand singletop events do not contain sufficient Emiss Tto contribute significantly to the background. The t¯ tsamples are normalized using their next-to-NLO (NNLO) cross-section including the resummation of soft gluon emission at next-to-NLL accuracy using Top++2.0 [42]. Samples of single-top-quark events are normalized using the NLO cross-sections reported in refs. [43–45] for the s-, tand W t-channels, respectively. Events containing Wor Zbosons with associated jets, including jets from the fragmentation of band c-quarks, were simulated using the Sherpa v2.2.1 [46] generator. – 4 – JHEP11(2017)195 Matrix elements were calculated for up to two additional partons at NLO and four partons at LO using the Comix [47] and OpenLoops [48] matrix element event generators and merged with the Sherpa PS [49] using the ME+PS@NLO prescription [50]. The NNPDF30NNLO [33] PDF set was used in conjunction with a dedicated PS tune developed by the Sherpa authors. Additional Sherpa Z+jets samples were produced with similar settings but with up to four partons LO, for the γ+jets studies detailed in section 6. The W/Z+jets events are normalized using their NNLO QCD theoretical cross-sections [51]. Diboson processes were also simulated using the Sherpa generator using the NNPDF30NNLO PDF set in conjunction with a dedicated PS tune developed by the Sherpa authors. They were calculated for up to one (ZZ) or zero (WW, W Z) additional partons at NLO and up to three additional partons at LO. Additional contributions to the SM backgrounds in the signal regions arise from the production of top quark pairs in association with W/Z/h bosons and possibly additional jets. The production of top quark pairs in association with electroweak vector bosons (W, Z) or Higgs bosons was modeled by samples generated at NLO using MadGraph5 aMC@NLO v2.2.3 and showered with Pythia v8.212. Other potential sources of backgrounds, such as the production of three or four top quarks or three gauge bosons, are found to be negligible. For all samples, except the ones generated using Sherpa, the EvtGen v1.2.0 program [52] was used to simulate the properties of the bottomand charm-hadron decays. In-time and out-of-time pile-up interactions from the same or nearby bunch-crossings were simulated by overlaying additional pp collisions generated by Pythia v8.186, with the MSTW2008LO [53] PDF set, superimposed onto the hard-scattering events to reproduce the observed distribution of the average number of interactions per bunch crossing [54]. Several samples produced without detector simulation are employed to estimate systematic uncertainties associated with the specific configuration of the MC event generators used for the nominal SM background samples. They include variations of the renormalization and factorization scales, the CKKW-L matching scale, as well as different PDF sets and fragmentation/hadronization models. Details of the MC modelling uncertainties are discussed in section 7. 4 Event reconstruction The search for pair production of bottom and top squarks is based on two distinct selections of events with b-jets and large missing transverse momentum, with either no charged leptons in the final state, or requiring exactly one electron or muon (for details, see section 5). For the zero-lepton channel selection, events containing charged leptons are explicitly vetoed in the signal and validation regions. Events characterized by the presence of exactly one electron or muon with transverse momentum above 27 GeV are retained in the one-lepton selection and are also used to define control regions for the zero-lepton channel. Finally, same-flavour opposite-sign (SFOS) two-lepton (electron or muon) events with dilepton invariant mass near the Zboson mass are used for control regions employed to aid in the estimation of the Z+jets background for the zero-lepton channel. The details of the reconstruction and selection, as well as the overlap removal procedure are given below. – 5 – JHEP11(2017)195 Selected events are required to have a reconstructed primary vertex consistent with the beamspot envelope and to consist of at least two tracks in the inner detector with pT >0.4 GeV. When more than one such vertex is found, the one with the largest sum of the squares of transverse momenta of associated tracks [55] is chosen. Jet candidates are reconstructed from three-dimensional energy clusters [56] in the calorimeter using the anti-ktjet algorithm [57,58] with a radius parameter of 0.4. The reconstructed jets are then calibrated to the particle level by the application of a jet energy scale (JES) derived from √s= 13 TeV data and simulation [59]. Quality criteria are imposed to identify jets arising from non-collision sources or detector noise, and any event containing such a jet is removed [60]. Further track-based selections are applied to reject jets with pT<60 GeV and |η|<2.4 that originate from pile-up interactions [61], and the jet momentum is corrected by subtracting the expected average energy contribution from pile-up using the jet area method [62]. Jets are classified as “baseline” and “signal”. Baseline jets are required to have pT>20 GeV and |η|<4.8. Signal jets, selected after resolving overlaps with electrons and muons, are required to pass the stricter requirement of pT>35 GeV and |η|<2.8. Jets are identified as b-jets if tagged by a multivariate algorithm which uses information about the impact parameters of inner detector tracks matched to the jet, the presence of displaced secondary vertices, and the reconstructed flight paths of band c-hadrons inside the jet [63]. The b-tagging working point with a 77% efficiency, as determined in a sample of simulated t¯ tevents, was chosen as part of the optimization procedure. The corresponding rejection factors against jets originating from c-quarks and from light quarks and gluons at this working point are 6.2 and 134, respectively [64]. To compensate for differences between data and MC simulation in the b-tagging efficiencies and mis-tag rates, correction factors are derived from data and applied to the samples of simulated events [63]. Candidate b-jets are required to have pT>20 GeV and |η|<2.5. Electron candidates are reconstructed from energy clusters in the electromagnetic calorimeter matched to a track in the inner detector and are required to satisfy a set of “loose” quality criteria [65–67]. They are also required to lie within the fiducial volume |η|<2.47. Muon candidates are reconstructed by matching tracks in the inner detector with tracks in the muon spectrometer. Events containing one or more muon candidates that have a transverse (longitudinal) impact parameter with respect to the primary vertex larger than 0.2 mm (1 mm) are rejected to suppress muons from cosmic rays. Muon candidates are also required to satisfy “medium” quality criteria [68] and have |η|¡ 2.5. All electron and muon candidates must have pT>10 GeV. Lepton candidates remaining after resolving overlaps with baseline jets (see next paragraph) are called “baseline” leptons. In the control and signal regions where lepton identification is required, “signal” leptons are chosen from the baseline set with pT>27 GeV to ensure full efficiency of the trigger and are required to be isolated from other activity in the detector using a criterion designed to accept at least 95% of leptons from Zboson decays as detailed in ref. [69]. The angular separation between the lepton and the b-jet arising from a semi-leptonically decaying top quark narrows as the top quark’s pTincreases. This increased collimation is accounted for by varying the radius of the isolation cone as max(0.2, 10 GeV/plep T), where plep Tis the – 6 – JHEP11(2017)195 lepton pT. Signal electrons are further required to satisfy “tight” quality criteria. Electrons (muons) are matched to the primary vertex by requiring the transverse impact parameter (d0) to satisfy |d0|/σ(d0)<5 (3), and the longitudinal impact parameter (z0) to satisfy |z0sin θ|<0.5 mm for both the electrons and muons. The MC events are corrected to account for differences in the lepton trigger, reconstruction and identification efficiencies between data and MC simulation. The sequence to resolve overlapping electrons, muons and jets begins by removing electron candidates sharing an inner detector track with a muon candidate. Next, jet candidates within ∆R=p(∆y)2+ (∆φ)2= 0.2 of an electron candidate are discarded, unless the jet is b-tagged, in which case the electron is discarded since it is likely to originate from a semileptonic b-hadron decay. Electrons are discarded if they lie within ∆R= 0.4 of a jet. Muons with pTbelow (above) 50 GeV are discarded if they lie within ∆R= 0.4 (∆R= 0.04 + 10 GeV/pT) of any remaining jet, except for the case where the number of tracks associated with the jet is less than three. The missing transverse momentum is defined as the negative vector sum of the pTof all selected and calibrated physics objects (electrons, muons and jets) in the event, with an extra term added to account for soft energy in the event which is not associated with any of the selected objects. This soft term is calculated from inner detector tracks with pTabove 0.4 GeV matched to the primary vertex to make it more robust against pile-up contamination [70,71]. Reconstructed photons are not used in the main signal event selections but are selected in the regions employed in one of the alternative methods used to estimate the Z+jets background, as explained in section 6. Photon candidates are required to have pT> 145 GeV and |η|<2.37, whilst being outside the transition region 1.37 <|η|<1.52, to satisfy the tight photon shower shape and electron rejection criteria [72], and to be isolated. 5 Event selection Two sets of signal regions (SRs) are defined and optimized to target different thirdgeneration squark decay modes and mass hierarchies of the particles involved. The zerolepton channel SRs (b0L) are designed to maximize the efficiency to retain bottom-squark pair production events where ˜ b1→b˜χ0 1. The one-lepton channel selections (b1L) target SUSY models where bottom squarks decay with a significant branching ratio as ˜ b1→t˜χ± 1 and the lightest chargino is almost degenerate with the lightest neutralino. With these assumptions, the final decay products of the off-shell Wboson from ˜χ± 1→˜χ0 1W∗are too soft to be detected. If the branching ratios of the two competing decay modes (b˜χ0 1, t˜χ± 1) are around 50%, the final state for the largest fraction of signal events is characterized by the presence of a top quark, a bottom quark, and neutralinos escaping the detector. Similarly, ˜ t1pair production can lead to an equivalent final state if the ˜ t1→t˜χ0 1and ˜ t1→b˜χ± 1decay modes compete. 5.1 Discriminating variables Several kinematic variables and angular correlations, built from the physics objects defined in the previous section, are employed to discriminate SUSY from SM background events – 7 – JHEP11(2017)195 and are reported below. In the following, signal jets are used and are ordered according to decreasing pT. •∆φj min, min[∆φ(jet1−4, Emiss T)], min[∆φ(jet1−2, Emiss T)]: these variables are the minimum ∆φbetween any of the leading jets and the missing transverse momentum vector. The background from multijet processes is characterized by small values of this variable. Depending on the signal regions, all, four or two jets are used. •HT: this is defined as the scalar sum of the pTof all jets in the event HT=X i (pjet T)i, where the number of jets involved depends on the signal region. In addition, the modified form of HT, referred to as the HT4 variable, is used to reject events with extra-jet activity in signal regions targeting models characterized by small masssplitting between the bottom squark and the neutralino. In HT4 the sum starts with the fourth jet (if any). •meff: this is defined as the scalar sum of the pTof the jets and the Emiss T, i.e.: meff =X(pjet T)i+Emiss T. The meff observable is correlated with the mass of the pair-produced SUSY particles and is employed as a discriminating variable in some of the zero-lepton and one-lepton channel selections, as well as in the computation of other composite observables. •Emiss T/meff,Emiss T/√HT: the first ratio is the Emiss Tdivided by the meff, while the second emulates the global Emiss Tsignificance, given that the Emiss Tresolution scales approximately with the square root of the total hadronic energy in the event. Events with low values for these variables are rejected as it is most probable that Emiss Tarises from jets mismeasurements, caused by instrumental and resolution effects. •mjj: this variable is calculated as the invariant mass of the leading two jets. In events where at least one of the leading jets is b-tagged, this variable aids in reducing the contamination from t¯ tevents. It is referred to as mbb for events where the two leading jets are b-tagged. •mT: the event transverse mass mTis defined as mT=q2plep TEmiss T−2plep T·pmiss T and is used in the one-lepton control and signal regions to reduce the W+jets and t¯ tbackgrounds. •mmin b` : the minimum invariant mass of the lepton and one of the two b-jets is defined as: mmin b` = mini=1,2(m`bi). This variable is bound from above by qm2 t−m2 Wfor t¯ tproduction, and it is used to distinguish t¯ tcontributions from Wt-channel single-top-quark events in the onelepton control regions. – 8 – JHEP11(2017)195 170 GeV is applied. For the CRs corresponding to b0L-SRB, selections on the azimuthal angle between the b-jets and the Emiss Tvalue are applied to enhance the t¯ tand W+jets contributions, while the single-top-quark background is estimated from MC simulation. The CRs corresponding to the b0L-SRC are defined with one or two b-jets to enhance the t¯ tand W+jets contributions, respectively. Finally, the single top quark production is estimated using the MC normalization. The contributions from dibosons (WW, WZ, ZZ), t¯ tproduction associated with W and Zbosons, and other rare backgrounds are estimated from MC simulation for both the signal and the control regions and included in the fit procedure, and are allowed to vary within their normalization uncertainty. The background from multijet production is estimated from data using a procedure described in detail in ref. [79] and modified to account for the heavy flavour of the jets. The contribution from multijet production in all regions is found to be negligible. In total, four CRs are defined for the b0L-SRA to estimate the contributions from W+jets, Z+jets, t¯ tand single top quark production independently, while three CRs are defined for each of the b0L-SRB and b0L-SRC to estimate W+jets, Z+jets and t¯ t. The Emiss Tdistribution in b0L-CRwA and b0L-CRzC is shown in figures 2a and 2b, where good agreement with the SM prediction is achieved after the background-only fit. The yields in all these CRs are shown in figure 3and compared to the MC predictions before the likelihood fit is performed, including only the statistical uncertainty of the MC samples. The bottom panel shows the value of the normalization factors, µ, used for each of the backgrounds fitted and given taking into account statistical and detector-related systematic uncertainties. As a further validation, two alternative methods are used to estimate the Z+jets contribution. The first method exploits the similarity of the Z+jets and γ+jets processes [79]. For a photon with pTsignificantly larger than the mass of the Zboson, the kinematics of γ+jets events strongly resemble those of Z+jets events. A set of dedicated control regions is defined by requiring one isolated photon with pT>145 GeV. The pTof the photon is vectorially added to the pmiss T, and the magnitude of this sum is used to replace the Emiss Tbased selections. The yields are then propagated to the SRs using a reweighting factor derived using the MC simulation. This factor takes into account the different kinematics of the two processes and residual effects arising from the different geometrical acceptance and reconstruction efficiency for photons. In the second alternative method, applied to b0L-SRA only, the MC simulation is used to verify that the shape of the mCT distribution for events with no b-tagged jets is compatible with the shape of the mCT distribution for events where two b-tagged jets are present. A new highly populated Z+jets CR is defined, selecting Z→`` events with no b-tagged jets. The mCT distribution in this CR is constructed using the two leading jets and is used to estimate the shape of the mCT distribution in the b0L-SRA, whilst the normalization in SRA is rescaled based on the ratio in data of Z→`` events with no b-tagged jets to events with two b-tagged jets. Additional MC-based corrections are applied to take into account the two-lepton selection in this CR. The two alternative methods are in agreement within uncertainties with the estimates obtained with the profile likelihood fit to the control regions. Experimental and theoretical – 15 – JHEP11(2017)195 systematic uncertainties in the estimates from the nominal and alternative methods are taken into account (see section 7). 6.2 Background estimation in the one-lepton signal regions The main SM background in the b1L signal regions is the production of t¯ tand singletop-quark events in the Wt channel. Two control regions (b1L-CRttA and b1L-CRttB) where the t¯ tproduction is enhanced are defined by inverting the amT2 selection. In the case of b1L-CRttA the mbb selection is also inverted, while for b1L-CRttB the min[mT(b-jet, Emiss T)] requirement is inverted. To allow a statistical combination of the results from the b0L-SRA and b1L-SRA regions the corresponding t¯ tCRs are defined to be orthogonal via the mCT selection. The single-top-quark contribution is estimated with the same CR employed by the b0L analysis. In the case of b1L-SRB the production of W+jets is no longer negligible, and is estimated by using a dedicated control region b1L-CRwB, where only one b-tagged jet is required. In total, two CRs are used to estimate the event yields in b1L-SRA and three CRs to estimate the yields in b1L-SRB. Full details of the CR selections are given in table 5. The distribution of mbb in b1L-CRstA and of mT in b1L-CRttB are presented in figures 2c and 2d to show the level of agreement achieved after the background-only fit. The yields in all these CRs are also shown in figure 3and compared to the direct MC prediction before the likelihood fit is performed. The normalization parameters reported for each SR and SM background process include the statistical and detector-related systematic uncertainties. The decrease of the µt¯ tparameter from SRA to SRC is related to mismodelling in the description of t¯ tprocesses by Powheg +Pythia 6 MC samples. Previous analyses [80] also found normalization factors considerably smaller than unity for t¯ tbackground processes in similar regions of phase space. The W+jets and Z+jets normalization factors are larger than unity. This is possibly related to the fact that in the default Sherpa 2.2.1 the heavy-flavour production fractions are not consistent with the measured values [81]. 6.3 Validation regions The results of the background-only fit to the CRs are extrapolated to a set of VRs defined to be similar to the SRs, with some of the selection criteria modified to enhance the background contribution, while maintaining a small signal contribution. For each SR, one or more VRs are defined starting from the SR definition and inverting or changing some of the selections as summarized in table 6. The number of events predicted by the background-only fit is compared to the data in the upper panel of figure 4. The pull, defined by the difference between the observed number of events (nobs) and the predicted background yield (npred) divided by the total uncertainty (σtot), is shown for each region in the lower panel. No evidence of significant background mismodelling is observed in the VRs. – 16 – JHEP11(2017)195 b1LCRttA CRstA CRttB CRstB CRwB Number of leptons (`=e, µ) 1 1 1 1 1 pT(`) [GeV] >27 >27 >27 >27 >27 Njets (pT>35 GeV) ≥2 [2–4] ≥2≥2≥2 pT(j1) [GeV] >35 >130 >35 >35 >35 pT(j2) [GeV] >35 >50 >35 >35 >35 pT(j4) [GeV] >35 [35–50] — — — min[∆φ(jet1−4, Emiss T)] >0.4>0.4>0.4>0.4>0.4 b-jets any 2 j1and (j2or j3or j4) any 2 any 2 any 1 mbb [GeV] <200 >200 <200 >200 >200 mmin b` [GeV] <170 >170 <170 >170 <170 Emiss T[GeV] >200 >200 >200 >200 >200 mT[GeV] >140 — >120 [30–120] [30–120] amT2 [GeV] <250 — <200 — >200 meff [GeV] >300 — — — — mCT [GeV] <250 >250 — — — Emiss T/√HT[GeV1/2]>8 — >8>8>8 Emiss T/meff —>0.25 — — — mmin T(b-jet1−2, Emiss T) [GeV] — — <200 >200 >200 ∆φ(b1, Emiss T) — — >2.0>2.0>2.0 Table 5. Summary of the event selection in each control region corresponding to the b1L signal regions. The term lepton is used in the table to refer to signal electrons and muons. Jets (j1,j2,j3 and j4) are labelled with an index corresponding to their decreasing order in pT. VR Corresponding SR Selection changes b0L-VRmctA b0L-SRA mmin T(jet1−4, Emiss T)<250 GeV, 150 < mCT <250 GeV b0L-VRmbbA b0L-SRA mmin T(jet1−4, Emiss T)<250 GeV, 100 < mbb <200 GeV b0L-VRzB b0L-SRB mCT <250 GeV, 200 < mmin T(jet1−4, Emiss T)<250 GeV, A<0.8, no selection on ∆φ(b1, Emiss T) and ∆φ(b2, Emiss T) b0L-VRttB b0L-SRB mCT <250 GeV, 150 < mmin T(jet1−4, Emiss T)<200 GeV, A<0.8 b0L-VRttC b0L-SRC mCT <250 GeV, mmin T(jet1−4, Emiss T)<250 GeV, 0.6<A<0.8 b1L-VRamt2A b1L-SRA300-2j 30 < mT<140 GeV, mbb <200 GeV b1L-VRmbbA b1L-SRA300-2j amT2 <250 GeV b1L-VRamt2B b1L-SRB ∆φ(b1, Emiss T)>2.0, mbb >200 GeV b1L-VRmbbB b1L-SRB ∆φ(b1, Emiss T)>2.0, 30 < mT<120 GeV Table 6. Summary of the VRs used in the analysis. Each VR (left column) corresponds to a SR (middle column) defined in tables 1and 2, with a few selection requirements changed (right column) to ensure the selection has low efficiency for the expected signal. – 17 – JHEP11(2017)195 Events / 50 GeV 1− 10 1 10 2 10 3 10 4 10 b0L-CRwA ATLAS ATLAS -1 = 13 TeV, 36.1 fbs Data SM total W + jets tt Single top Z + jets Others Vtt [GeV] miss T E 200 300 400 500 600 700 Data / SM 0 1 2 (a) Events / 50 GeV 1− 10 1 10 2 10 3 10 b0L-CRzC ATLAS ATLAS -1 = 13 TeV, 36.1 fbs Data SM total Z + jets tt Others [GeV] miss,cor T E 200 250 300 350 400 450 500 550 600 Data / SM 0 1 2 (b) Events / 50 GeV 1− 10 1 10 2 10 3 10 b1L-CRstA ATLAS ATLAS -1 = 13 TeV, 36.1 fbs Data SM total Single top W + jets tt Others Z + jets Vtt [GeV] bb m 200 300 400 500 600 700 800 900 1000 Data / SM 0 1 2 (c) Events / 30 GeV 1− 10 1 10 2 10 3 10 4 10 5 10 b1L-CRttB ATLAS ATLAS -1 = 13 TeV, 36.1 fbs Data SM total tt Single top W + jets Vtt Others Z + jets [GeV] T m 150 200 250 300 350 400 450 500 550 600 Data / SM 0 1 2 (d) Figure 2. Example kinematic distributions in some of the control regions. (a) Emiss Tin b0L-CRwA, (b) Emiss,cor Tin b0L-CRzC, (c) mbb in b1L-CRstA and (d) mTin b1L-CRttB. In all distributions the MC normalization is rescaled using the results from the background-only fit, showing good agreement between data and the predicted SM shapes. The contributions from diboson, multijet and rare backgrounds are collectively called “Others”. The shaded-grey band shows the detectorrelated systematic uncertainties and the statistical uncertainties of the MC samples as detailed in section 7and the last bin includes overflow events. 7 Systematic uncertainties Several sources of experimental and theoretical systematic uncertainty in the signal and background estimates are considered in these analyses. Their impact is reduced through the normalization of the dominant backgrounds in the control regions defined with kinematic selections resembling those of the corresponding signal region (see section 6). Experimental and theoretical uncertainties are included as nuisance parameters with Gaussian constraints in the likelihood fits, taking into account correlations between different regions. Uncertainties due to the numbers of events in the CRs are also introduced in the fit for each region. The dominant contributions are summarized in table 7. – 18 – JHEP11(2017)195 CRB μtt 0.97 ± 0.05 μW 1.50 ± 0.22 μst 0.71 ± 0.19 CRA μtt 1.07 ± 0.04 μst 0.64 ± 0.25 CRC μZ 1.17 ± 0.19 μtt 0.66 ± 0.18 μW 1.11 ± 0.21 CRB μZ 1.52 ± 0.07 μtt 1.33 ± 0.21 μW 1.31 ± 0.12 CRA μZ 1.33 ± 0.20 μtt 1.03 ± 0.21 μW 1.26 ± 0.17 μst 0.49 ± 0.23 b0L b1L Number of Events 1− 10 1 10 2 10 3 10 4 10 5 10 Preliminary ATLAS -1 = 13 TeV, 36.1 fbs Data SM Total Z + jets W + jets Others Vtt Single top tt b0L-CRstA b0L-CRwA b0L-CRttA b0L-CRzA b0L-CRwB b0L-CRttB b0L-CRzB b0L-CRwC b0L-CRttC b0L-CRzC b1L-CRttA b0L-CRstA b1L-CRttB b1L-CRstB b1L-CRwB Data/SM 0 0.5 1 1.5 Number of Events 1 10 1 10 2 10 3 10 4 10 5 10 Preliminary ATLAS -1 = 13 TeV, 36.1 fbs Data SM Total Z + jets W + jets Others Vtt Single top tt b0L-CRstA b0L-CRwA b0L-CRttA b0L-CRzA b0L-CRwB b0L-CRttB b0L-CRzB b0L-CRwC b0L-CRttC b0L-CRzC b1L-CRttA b0L-CRstA b1L-CRttB b1L-CRstB b1L-CRwB Data/SM 0.5 1 1.5 Number of Events 1 10 1 10 2 10 3 10 4 10 5 10 ATLAS -1 = 13 TeV, 36.1 fbs Data SM Total Z + jets W + jets Others Vtt Single top tt b0L-CRstA b0L-CRwA b0L-CRttA b0L-CRzA b0L-CRwB b0L-CRttB b0L-CRzB b0L-CRwC b0L-CRttC b0L-CRzC b1L-CRttA b0L-CRstA b1L-CRttB b1L-CRstB b1L-CRwB Data/SM 0.5 1 1.5 Figure 3. Data and MC predictions for all CRs associated with all b0L and b1L SRs before the likelihood fit, as well as the results obtained by the likelihood fit. In the top panel the normalization of the backgrounds is obtained from MC simulation and is the input value to the fit. The contributions from diboson, multijet and rare backgrounds are collectively called “Others”. The panels at the bottom show the ratio of the observed events in each CR to the MC estimate, and the value of the normalization factors (µ) obtained for each of the backgrounds fitted. The uncertainty band around the MC prediction includes only the statistical uncertainty of the MC samples. The normalization factors µare presented for each region and SM background process and take into account statistical and detector-related systematic uncertainties. Source \Region b0L-SRAx b0L-SRB b0L-SRC b1L-SRAx b1L-SRB b1L-SRA300-2j Experimental uncertainty JES 2.3 – 3.4% 5.7% 4.3% 1.2 – 1.5% 0.9% 6.9% JER 0.9 – 3.3% 3.5% 11% 5.3 – 8.6% 0.9% 4% b-tagging 3.3 – 4.3% 7.5% 4.7% 6.1 – 6.3% 2% 6.6% Theoretical modelling uncertainty Z+jets 9.6 – 12% 13% 11% — — — W+jets 3.4 – 5.2% 4.7% 7.6% 1.3 – 1.6% 8.6% 7.9% Top production 2.2 – 3.1% 6% 3.6% 19% 13% 22% Table 7. Summary of the dominant experimental and theoretical uncertainties for each signal region in zero-lepton and one-lepton channels. Uncertainties are quoted as relative to the total SM background predictions, with a range indicated for the three b0L-SRAs and the two b1L-SRAs. For theoretical modelling, uncertainties per dominant SM background process are quoted. The individual uncertainties can be correlated, and do not necessarily add in quadrature to the total background uncertainty. – 19 – JHEP11(2017)195 Number of Events 1 10 2 10 3 10 4 10 5 10 ATLAS -1 = 13 TeV, 36.1 fbs Data SM Total Z + jets W + jets Others Vtt Single top tt b0L-VRmctA b0L-VRmbbA b0L-VRttB b0L-VRzB b0L-VRttC b1L-VRamt2A b1L-VRmbbA b1L-VRmbbB b1L-VRamt2B tot σ) / pred - n obs (n 2− 0 2 Figure 4. Results of the likelihood fit extrapolated to the VRs associated with the b0L and b1L analyses. The normalization of the backgrounds is obtained from the fit to the CRs. The upper panel shows the observed number of events and the predicted background yield. The contributions from diboson, multijet and rare backgrounds are collectively called “Others”. All uncertainties defined in section 7are included in the uncertainty band. The lower panel shows the pulls in each VR, where σtot is the error on the background estimation as a sum in quadrature of the systematic uncertainty and the statistical uncertainty on the estimate. The dominant detector-related systematic effects are due to the uncertainties in the jet energy scale (JES) [59] and resolution (JER) [82], and in the b-tagging efficiency and mistagging rates. The latter are estimated by varying the η-, pTand flavour-dependent scale factors applied to each jet in the simulation within a range that reflects the systematic uncertainty in the measured tagging efficiency and mis-tag rates in 13 TeV data. The uncertainties associated with lepton and photon reconstruction and energy measurements are also considered but have a negligible impact on the final results. Lepton, photon and jetrelated uncertainties are propagated to the Emiss Tcalculation, and additional uncertainties are included in the energy scale and resolution of the soft term. Uncertainties in the modelling of the SM background processes from MC simulation and their theoretical cross-section uncertainties are also taken into account. The dominant uncertainty arises from Z+jets MC modelling for b0L-SRs and t¯ tand single-top modelling (collectively referred to as “Top production” in table 7) for b1L-SRs. The Z+jets (as well as W+jets) modelling uncertainties are estimated by considering different merging (CKKW-L) and resummation scales using alternative samples, PDF variations from the NNPDF30NNLO replicas [46], as well as an envelope formed from seven-point scale variations of the renormalization and factorization scales. The various components are added in quadrature. A 40% uncertainty [83] is assigned to the heavy-flavour jet content in W+jets background, which is estimated from MC simulation in the one-lepton channel control re- – 20 – JHEP11(2017)195 gions. For b0L-SRA, b0L-SRC and b1L-SRB the uncertainty accounts for the different requirements on b-jets between the signal regions and the corresponding control regions. Theoretical and modelling uncertainties of the top quark pair and single-top-quark (Wt) backgrounds are computed as the difference between the prediction from nominal samples and those from additional samples differing in generator or parameter settings. Hadronization and PS uncertainties are estimated using samples generated using PowhegBox v2 and showered by Herwig++ v2.7.1 [84] with the UEEE5 [85] underlying-event tune. Uncertainties related to initialand final-state radiation modelling, PS tune and (for t¯ tonly) choice of hdamp parameter in Powheg-Box v2 are estimated using alternative settings of the event generators. Finally, an alternative generator MadGraph5 aMC@NLO with showering by Herwig++ v2.7.1 is used to estimate the event generator uncertainties. One additional uncertainty stems from the modelling of the interference between the t¯ tand Wt processes at NLO. Predictions from an inclusive W Wbb sample generated at LO using MadGraph5 aMC@NLO are compared with the sum of the t¯ tand Wt predictions, and differences from the nominal predictions are taken as systematic uncertainties. Uncertainties in backgrounds such as diboson and ttV are also estimated by comparisons of the nominal sample with alternative samples differing in generator or parameter settings (Powheg v2 with showering by Pythia v8.210 for dibosons; renormalization and factorization scale and A14 tune variations for ttV ) and contribute less than 5% to the total uncertainty. The cross-sections used to normalize the MC yields to the highest order available are varied according to the scale uncertainty of the theoretical calculation. The cross-section uncertainties are 5% for Wboson, Zboson and top quark pair production, 6% for dibosons, and 13% and 12% for ttW and ttZ, respectively. Finally, a conservative 100% systematic uncertainty associated to the multijet background estimate is considered and found to have a negligible effect. For the SUSY signal processes, both the experimental and theoretical uncertainties in the expected signal yield are considered. Experimental uncertainties are found to be between 15% and 30% across the ˜ b1–˜χ0 1mass plane for exclusive ˜ b1→b˜χ0 1decays and between 10% and 25% for models where bottom squarks decay with a significant branching ratio as ˜ b1→t˜χ± 1, assuming the one-lepton channel selection. In all SRs, they are largely dominated by the uncertainty in the b-tagging efficiency. Theoretical uncertainties in the NLO+NLL cross-section are calculated for each SUSY signal scenario and are dominated by the uncertainties in the renormalization and factorization scales, followed by the uncertainty in the PDF. They vary between 15% and 25% for bottom squark masses in the range between 400 GeV and 1100 GeV. Additional uncertainties in the acceptance and efficiency due to the modelling of initial-state radiation and scale variations in SUSY signal MC samples are also taken into account and contribute up to about 10%. 8 Results and interpretation Tables 8and 9report the observed number of events and the SM prediction after the background-only fit for each signal region in the zero-lepton and one-lepton channels, respectively. The background-only fit results are compared to the pre-fit predictions based – 21 – JHEP11(2017)195 b0LSignal Region SRA350 SRA450 SRA550 SRB SRC Observed 81 24 10 45 7 Total background (fit) 70 ±13 22 ±5 7.2±1.5 37 ±7 5.5±1.5 Z+jets 46 ±12 13.6±3.7 4.0±1.2 20.0±5.2 2.3±0.8 t¯ t2.0±0.6 0.5±0.2 0.16 ±0.07 5.1±2.7 0.8±0.3 Single top 4.7±3.4 1.2±1.0 0.5±0.3 2.6±1.1 0.7±0.3 W+jets 15 ±5 5.0±1.8 2.4±1.0 5.5±2.0 1.3±0.8 Others 2.5±1.7 1.4±1.2 0.07 ±0.03 4.0±1.1 0.4±0.1 Total background (MC exp.) 60.4 18.5 6.2 28 5.4 Z+jets 34.9 10.3 3.0 13.1 1.9 t¯ t1.9 0.45 0.16 3.8 1.2 Single top 10 2.5 1.0 2.6 0.7 W+jets 11.6 4.0 1.9 4.2 1.2 Others 2.5 1.3 0.07 4.0 0.4 Table 8. Fit results in the b0L signal regions. The background normalization parameters are obtained from the fit in the control regions and are applied to the SRs. Smaller backgrounds such as diboson, ttV , multijet and rare processes are indicated as “Others”. The individual uncertainties, including statistical, detector-related and theoretical systematic components, are symmetrized and can be correlated. They do not necessarily add in quadrature to the total systematic uncertainty. on MC simulation. The largest background contribution in b0L-SRs arises from Z→ν¯ν produced in association with b-quarks followed by W+jets production, whilst top quark and W+jets production dominates SM predictions for b1L-SRs. The results are also summarized in figure 5, where the pulls for each of the SRs are also presented. No significant excess above the expected Standard Model background yield is observed, although b1LSRA300-2j presents a discrepancy between data and SM predictions of about 1.5σ. Figure 6shows the comparison between the observed data and the SM predictions for some relevant kinematic distributions for the b0L and b1L selections. For illustrative purposes, the distributions expected for scenarios with different bottom squark and neutralino masses depending on the SR considered are shown. The results are translated into upper limits on contributions from physics beyond the SM (BSM) for each signal region. The CLsmethod [86,87] is used to derive the confidence level of the exclusion; signal models with a CLsvalue below 0.05 are said to be excluded at 95% CL. The profile-likelihood-ratio test statistic is used to exclude the signal-plusbackground hypothesis for specific signal models. S95 obs (S95 exp) is the observed (expected) upper limit at 95% CL on the number of events from BSM phenomena for each signal region. These limits, when normalized by the integrated luminosity of the data sample, may be interpreted as upper limits on the visible cross-section of BSM physics, σvis, defined as the product of the production cross-section, the acceptance and the selection efficiency of a BSM signal. Table 10 summarizes S95 obs,S95 exp, and σvis for all SRs, together with the p0-values, which represent the probability of the SM background alone to fluctuate to the observed number of events or higher. – 22 – JHEP11(2017)195 b1LSignal Region SRA600 SRA750 SRB SRA300-2j Observed 21 13 69 12 Total background (fit) 24 ±6 15 ±4 53 ±12 6.7±2.3 t¯ t10 ±5 5.5±2.7 16 ±7 2.4±1.3 Single top 7 ±4 4.5±2.8 10 ±5 3.3±2.0 W+jets 0.9±0.5 0.6±0.3 17 ±8 0.4±0.3 ttV 5.4±0.6 4.0±0.5 9 ±1 0.6±0.1 Others 0.07 ±0.02 0.07 ±0.03 1.8±0.3 0.07 ±0.02 Total background (MC exp.) 27 17 52 8.4 t¯ t9 5.1 16 2.2 Single top 11 7.1 14 5.2 W+jets 0.9 0.6 11 0.4 ttV 5.4 4.090.6 Others 0.07 0.07 1.8 0.07 Table 9. Fit results in the b1L signal regions. The background normalization parameters are obtained from the background-only fit in the control regions and are applied to the SRs. Smaller backgrounds such as diboson, Z+jets, multijet and rare processes are indicated as “Others”. The individual uncertainties, including detector-related and theoretical systematic components, are symmetrized and can be correlated. They do not necessarily add in quadrature to the total systematic uncertainty. Signal channel hAσi95 obs[fb] S95 obs S95 exp p0(Z) b0L-SRA350 1.06 38.2 30.9+11.3 −8.40.28 (0.60) b0L-SRA450 0.43 15.6 13.9+5.6 −3.80.37 (0.34) b0L-SRA550 0.30 10.7 7.8+3.7 −1.60.20 (0.85) b0L-SRB 0.72 26.1 19.9+8.3 −5.40.23 (0.74) b0L-SRC 0.24 8.7 6.8+3.3 −1.30.30 (0.54) b1L-SRA300-2j 0.39 14.1 9.3+3.5 −3.10.08 (1.43) b1L-SRA600 0.38 13.6 14.8+5.4 −4.40.50 (0.00) b1L-SRA750 0.27 9.9 11.2+4.0 −2.30.50 (0.00) b1L-SRB 1.12 40.3 28.7+10.7 −8.20.21 (0.80) Table 10. Left to right: 95% CL upper limits on the visible cross-section (hAσi95 obs) and on the number of signal events (S95 obs ). The third column (S95 exp) shows the 95% CL upper limit on the number of signal events, given the expected number (and ±1σvariations of the expected number) of background events. The last column reports the p0-values and Z(the number of equivalent Gaussian standard deviations). The maximum allowed p0-value is truncated at 0.5. – 23 – JHEP11(2017)195 Number of Events 1− 10 1 10 2 10 3 10 4 10 ATLAS -1 = 13 TeV, 36.1 fbs Data SM Total Z + jets W + jets Others Vtt Single top tt b0L-SRA350 b0L-SRA450 b0L-SRA550 b0L-SRB b0L-SRC b1L-SRA300-2j b1L-SRA600 b1L-SRA750 b1L-SRB tot σ) / pred - n obs (n 2− 0 2 Figure 5. Results of the likelihood fit extrapolated to the SRs associated with the b0L and b1L analyses. The normalization of the backgrounds is obtained from the fit to the CRs. The upper panel shows the observed number of events and the predicted background yields. The contributions from diboson, multijet and rare backgrounds are collectively called “Others”. All uncertainties defined in section 7are included in the uncertainty band. The lower panel shows the pulls in each SR, where σtot is the error on the background estimation as a sum in quadrature of the systematic uncertainty and the statistical uncertainty on the estimate. Exclusion limits are obtained assuming two types of SUSY particle mass hierarchy such that the lightest bottom squark decays either exclusively via ˜ b1→b˜χ0 1or into multiple channels, ˜ b1→b˜χ0 1and ˜ b1→t˜χ± 1, assuming a 50% branching ratio and ∆m(˜χ± 1,˜χ0 1)∼ 1 GeV. The first set of scenarios is targeted by the zero-lepton channel SRs only. For models with mixed decays, the expected limits from the SRs are compared and the observed limits are obtained by statistically combining the most sensitive zero-lepton SR with the most sensitive one-lepton SR. In all cases, the fit procedure takes into account correlations in the yield predictions between control and signal regions due to common background normalization parameters and systematic uncertainties. The experimental systematic uncertainties in the signal are taken into account for this calculation and are assumed to be fully correlated with those in the SM background. For the exclusive ˜ b1→b˜χ0 1decay mode, at each point of the parameter space the SR with the best expected sensitivity is used. Sensitivity to scenarios with the largest mass difference between the ˜ b1and the ˜χ0 1is achieved with the most stringent mCT threshold (b0L-SRA550). Sensitivity to scenarios with intermediate and small mass differences is obtained with the dedicated b0L-SRB and b0L-SRC selections, respectively. For the mixeddecays scenarios, a statistical combination is computed with the results of the zero-lepton and one-lepton channels as explained above. A combined fit is performed simultaneously on the control and signal regions of the two analyses. 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Fang35a, M. Fanti94a,94b, A. Farbin8, A. Farilla136a, C. Farina127, E.M. Farina123a,123b, T. Farooque93, S. Farrell16, S.M. Farrington173, P. Farthouat32, F. Fassi137e, P. Fassnacht32, D. Fassouliotis9, M. Faucci Giannelli49, A. Favareto53a,53b, W.J. Fawcett122, L. Fayard119, O.L. Fedin125,q, W. Fedorko171, S. Feigl121, L. Feligioni88, C. Feng36b, E.J. Feng32, H. Feng92, M.J. Fenton56, A.B. Fenyuk132, L. Feremenga8, P. Fernandez Martinez170, S. Fernandez Perez13, J. Ferrando45, A. Ferrari168, P. Ferrari109, R. Ferrari123a, D.E. Ferreira de Lima60b, A. Ferrer170, D. Ferrere52, C. Ferretti92, F. Fiedler86, A. Filipˇciˇc78, M. Filipuzzi45, F. Filthaut108, – 35 – JHEP11(2017)195 M. Fincke-Keeler172, K.D. Finelli152, M.C.N. Fiolhais128a,128c,r, L. Fiorini170, A. Fischer2, C. Fischer13, J. Fischer178, W.C. Fisher93, N. Flaschel45, I. Fleck143, P. Fleischmann92, R.R.M. Fletcher124, T. Flick178, B.M. Flierl102, L.R. Flores Castillo62a, M.J. Flowerdew103, G.T. Forcolin87, A. Formica138, F.A. F¨orster13, A. Forti87, A.G. Foster19, D. Fournier119, H. Fox75, S. Fracchia141, P. Francavilla83, M. Franchini22a,22b, S. Franchino60a, D. Francis32, L. Franconi121, M. Franklin59, M. Frate166, M. Fraternali123a,123b, D. Freeborn81, S.M. Fressard-Batraneanu32, B. Freund97, D. Froidevaux32, J.A. Frost122, C. Fukunaga158, T. Fusayasu104, J. Fuster170, C. Gabaldon58, O. Gabizon154, A. Gabrielli22a,22b, A. Gabrielli16, G.P. Gach41a, S. Gadatsch32, S. Gadomski80, G. Gagliardi53a,53b, L.G. Gagnon97, C. Galea108, B. Galhardo128a,128c, E.J. Gallas122, B.J. Gallop133, P. Gallus130, G. Galster39, K.K. Gan113, S. Ganguly37, Y. Gao77, Y.S. Gao145,g, F.M. Garay Walls34a, C. Garc´ıa170, J.E. Garc´ıa Navarro170, J.A. Garc´ıa Pascual35a, M. Garcia-Sciveres16, R.W. Gardner33, N. Garelli145, V. Garonne121, A. Gascon Bravo45, K. Gasnikova45, C. Gatti50, A. Gaudiello53a,53b, G. Gaudio123a, I.L. Gavrilenko98, C. Gay171, G. Gaycken23, E.N. Gazis10, C.N.P. Gee133, J. Geisen57, M. Geisen86, M.P. Geisler60a, K. Gellerstedt148a,148b, C. Gemme53a, M.H. Genest58, C. Geng92, S. Gentile134a,134b, C. Gentsos156, S. George80, D. Gerbaudo13, A. Gershon155, G. Geßner46, S. Ghasemi143, M. Ghneimat23, B. Giacobbe22a, S. Giagu134a,134b, N. Giangiacomi22a,22b, P. Giannetti126a,126b, S.M. Gibson80, M. Gignac171, M. Gilchriese16, D. Gillberg31, G. Gilles178, D.M. Gingrich3,d, M.P. Giordani167a,167c, F.M. Giorgi22a, P.F. Giraud138, P. Giromini59, G. Giugliarelli167a,167c, D. Giugni94a, F. Giuli122, C. Giuliani103, M. Giulini60b, B.K. Gjelsten121, S. Gkaitatzis156, I. Gkialas9,s, E.L. Gkougkousis13, P. Gkountoumis10, L.K. Gladilin101, C. Glasman85, J. Glatzer13, P.C.F. Glaysher45, A. Glazov45, M. Goblirsch-Kolb25, J. Godlewski42, S. Goldfarb91, T. Golling52, D. Golubkov132, A. Gomes128a,128b,128d, R. Gon¸calo128a, R. Goncalves Gama26a, J. Goncalves Pinto Firmino Da Costa138, G. Gonella51, L. Gonella19, A. Gongadze68, S. Gonz´alez de la Hoz170, S. Gonzalez-Sevilla52, L. Goossens32, P.A. Gorbounov99, H.A. Gordon27, I. Gorelov107, B. Gorini32, E. Gorini76a,76b, A. Goriˇsek78, A.T. Goshaw48, C. G¨ossling46, M.I. Gostkin68, C.A. Gottardo23, C.R. Goudet119, D. Goujdami137c, A.G. Goussiou140, N. Govender147b,t, E. Gozani154, L. Graber57, I. Grabowska-Bold41a, P.O.J. Gradin168, J. Gramling166, E. Gramstad121, S. Grancagnolo17, V. Gratchev125, P.M. Gravila28f , C. Gray56, H.M. Gray16, Z.D. Greenwood82,u, C. Grefe23, K. Gregersen81, I.M. Gregor45, P. Grenier145, K. Grevtsov5, J. Griffiths8, A.A. Grillo139, K. Grimm75, S. Grinstein13,v, Ph. Gris37, J.-F. Grivaz119, S. Groh86, E. Gross175, J. Grosse-Knetter57, G.C. Grossi82, Z.J. Grout81, A. Grummer107, L. Guan92, W. Guan176, J. Guenther65, F. Guescini163a, D. Guest166, O. Gueta155, B. Gui113, E. Guido53a,53b, T. Guillemin5, S. Guindon32, U. Gul56, C. Gumpert32, J. Guo36c, W. Guo92, Y. Guo36a, R. Gupta43, S. Gupta122, G. Gustavino115, B.J. Gutelman154, P. Gutierrez115, N.G. Gutierrez Ortiz81, C. Gutschow81, C. Guyot138, M.P. Guzik41a, C. Gwenlan122, C.B. Gwilliam77, A. Haas112, C. Haber16, H.K. Hadavand8, N. Haddad137e, A. Hadef88, S. Hageb¨ock23, M. Hagihara164, H. Hakobyan180,∗, M. Haleem45, J. Haley116, G. Halladjian93, G.D. Hallewell88, K. Hamacher178, P. Hamal117, K. Hamano172, A. Hamilton147a, G.N. Hamity141, P.G. Hamnett45, L. Han36a, S. Han35a, K. Hanagaki69,w, K. Hanawa157, M. Hance139, B. Haney124, P. Hanke60a, J.B. Hansen39, J.D. Hansen39, M.C. Hansen23, P.H. Hansen39, K. Hara164, A.S. Hard176, T. Harenberg178, F. Hariri119, S. Harkusha95, P.F. Harrison173, N.M. Hartmann102, Y. Hasegawa142, A. Hasib49, S. Hassani138, S. Haug18, R. Hauser93, L. Hauswald47, L.B. Havener38, M. Havranek130, C.M. Hawkes19, R.J. Hawkings32, D. Hayakawa159, D. Hayden93, C.P. Hays122, J.M. Hays79, H.S. Hayward77, S.J. Haywood133, S.J. Head19, T. Heck86, V. Hedberg84, L. Heelan8, S. Heer23, K.K. Heidegger51, S. Heim45, T. Heim16, B. Heinemann45,x, J.J. Heinrich102, – 36 – JHEP11(2017)195 L. Heinrich112, C. Heinz55, J. Hejbal129, L. Helary32, A. Held171, S. Hellman148a,148b, C. Helsens32, R.C.W. Henderson75, Y. Heng176, S. Henkelmann171, A.M. Henriques Correia32, S. Henrot-Versille119, G.H. Herbert17, H. Herde25, V. Herget177, Y. Hern´andez Jim´enez147c, H. Herr86, G. Herten51, R. Hertenberger102, L. Hervas32, T.C. Herwig124, G.G. Hesketh81, N.P. Hessey163a, J.W. Hetherly43, S. Higashino69, E. Hig´on-Rodriguez170, K. Hildebrand33, E. Hill172, J.C. Hill30, K.H. Hiller45, S.J. Hillier19, M. Hils47, I. Hinchliffe16, M. Hirose51, D. Hirschbuehl178, B. Hiti78, O. Hladik129, X. Hoad49, J. Hobbs150, N. Hod163a, M.C. Hodgkinson141, P. Hodgson141, A. Hoecker32, M.R. Hoeferkamp107, F. Hoenig102, D. Hohn23, T.R. Holmes33, M. Homann46, S. Honda164, T. Honda69, T.M. Hong127, B.H. Hooberman169, W.H. Hopkins118, Y. Horii105, A.J. Horton144, J-Y. Hostachy58, A. Hostiuc140, S. Hou153, A. Hoummada137a, J. Howarth87, J. Hoya74, M. Hrabovsky117, J. Hrdinka32, I. Hristova17, J. Hrivnac119, T. Hryn’ova5, A. Hrynevich96, P.J. Hsu63, S.-C. Hsu140, Q. Hu36a, S. Hu36c, Y. Huang35a, Z. Hubacek130, F. Hubaut88, F. Huegging23, T.B. Huffman122, E.W. Hughes38, G. Hughes75, M. Huhtinen32, P. Huo150, N. Huseynov68,b, J. Huston93, J. Huth59, G. Iacobucci52, G. Iakovidis27, I. Ibragimov143, L. Iconomidou-Fayard119, Z. Idrissi137e, P. Iengo32, O. Igonkina109,y, T. Iizawa174, Y. Ikegami69, M. Ikeno69, Y. Ilchenko11,z, D. Iliadis156, N. Ilic145, G. Introzzi123a,123b, P. Ioannou9,∗, M. Iodice136a, K. Iordanidou38, V. Ippolito59, M.F. Isacson168, N. Ishijima120, M. Ishino157, M. Ishitsuka159, C. Issever122, S. Istin20a, F. Ito164, J.M. Iturbe Ponce62a, R. Iuppa162a,162b, H. Iwasaki69, J.M. Izen44, V. Izzo106a, S. Jabbar3, P. Jackson1, R.M. Jacobs23, V. Jain2, K.B. Jakobi86, K. Jakobs51, S. Jakobsen65, T. Jakoubek129, D.O. Jamin116, D.K. Jana82, R. Jansky52, J. Janssen23, M. Janus57, P.A. Janus41a, G. Jarlskog84, N. Javadov68,b, T. Jav˚urek51, M. Javurkova51, F. Jeanneau138, L. Jeanty16, J. Jejelava54a,aa, A. Jelinskas173, P. Jenni51,ab, C. Jeske173, S. J´ez´equel5, H. Ji176, J. Jia150, H. Jiang67, Y. Jiang36a, Z. Jiang145, S. Jiggins81, J. Jimenez Pena170, S. Jin35a, A. Jinaru28b, O. Jinnouchi159, H. Jivan147c, P. Johansson141, K.A. Johns7, C.A. Johnson64, W.J. Johnson140, K. Jon-And148a,148b, R.W.L. Jones75, S.D. Jones151, S. Jones7, T.J. Jones77, J. Jongmanns60a, P.M. Jorge128a,128b, J. Jovicevic163a, X. Ju176, A. Juste Rozas13,v, M.K. K¨ohler175, A. Kaczmarska42, M. Kado119, H. Kagan113, M. Kagan145, S.J. Kahn88, T. Kaji174, E. Kajomovitz48, C.W. Kalderon84, A. Kaluza86, S. Kama43, A. Kamenshchikov132, N. Kanaya157, L. Kanjir78, V.A. Kantserov100, J. Kanzaki69, B. Kaplan112, L.S. Kaplan176, D. Kar147c, K. Karakostas10, N. Karastathis10, M.J. Kareem57, E. Karentzos10, S.N. Karpov68, Z.M. Karpova68, K. Karthik112, V. Kartvelishvili75, A.N. Karyukhin132, K. Kasahara164, L. Kashif176, R.D. Kass113, A. Kastanas149, Y. Kataoka157, C. Kato157, A. Katre52, J. Katzy45, K. Kawade70, K. Kawagoe73, T. Kawamoto157, G. Kawamura57, E.F. Kay77, V.F. Kazanin111,c, R. Keeler172, R. Kehoe43, J.S. Keller31, E. Kellermann84, J.J. Kempster80, J Kendrick19, H. Keoshkerian161, O. Kepka129, B.P. Kerˇsevan78, S. Kersten178, R.A. Keyes90, M. Khader169, F. Khalil-zada12, A. Khanov116, A.G. Kharlamov111,c, T. Kharlamova111,c, A. Khodinov160, T.J. Khoo52, V. Khovanskiy99,∗, E. Khramov68, J. Khubua54b,ac, S. Kido70, C.R. Kilby80, H.Y. Kim8, S.H. Kim164, Y.K. Kim33, N. Kimura156, O.M. Kind17, B.T. King77, D. Kirchmeier47, J. Kirk133, A.E. Kiryunin103, T. Kishimoto157, D. Kisielewska41a, V. Kitali45, O. Kivernyk5, E. Kladiva146b, T. Klapdor-Kleingrothaus51, M.H. Klein92, M. Klein77, U. Klein77, K. Kleinknecht86, P. Klimek110, A. Klimentov27, R. Klingenberg46, T. Klingl23, T. Klioutchnikova32, E.-E. Kluge60a, P. Kluit109, S. Kluth103, E. Kneringer65, E.B.F.G. Knoops88, A. Knue103, A. Kobayashi157, D. Kobayashi159, T. Kobayashi157, M. Kobel47, M. Kocian145, P. Kodys131, T. Koffas31, E. Koffeman109, N.M. K¨ohler103, T. Koi145, M. Kolb60b, I. Koletsou5, A.A. Komar98,∗, T. Kondo69, N. Kondrashova36c, K. K¨oneke51, A.C. K¨onig108, T. Kono69,ad, R. Konoplich112,ae, N. Konstantinidis81, R. Kopeliansky64, – 37 – JHEP11(2017)195 S. Koperny41a, A.K. Kopp51, K. Korcyl42, K. Kordas156, A. Korn81, A.A. Korol111,c, I. Korolkov13, E.V. Korolkova141, O. Kortner103, S. Kortner103, T. Kosek131, V.V. Kostyukhin23, A. Kotwal48, A. Koulouris10, A. Kourkoumeli-Charalampidi123a,123b, C. Kourkoumelis9, E. Kourlitis141, V. Kouskoura27, A.B. Kowalewska42, R. Kowalewski172, T.Z. Kowalski41a, C. Kozakai157, W. Kozanecki138, A.S. Kozhin132, V.A. Kramarenko101, G. Kramberger78, D. Krasnopevtsev100, M.W. Krasny83, A. Krasznahorkay32, D. Krauss103, J.A. Kremer41a, J. Kretzschmar77, K. Kreutzfeldt55, P. Krieger161, K. Krizka16, K. Kroeninger46, H. Kroha103, J. Kroll129, J. Kroll124, J. Kroseberg23, J. Krstic14, U. Kruchonak68, H. Kr¨uger23, N. Krumnack67, M.C. Kruse48, T. Kubota91, H. Kucuk81, S. Kuday4b, J.T. Kuechler178, S. Kuehn32, A. Kugel60a, F. Kuger177, T. Kuhl45, V. Kukhtin68, R. Kukla88, Y. Kulchitsky95, S. Kuleshov34b, Y.P. Kulinich169, M. Kuna134a,134b, T. Kunigo71, A. Kupco129, T. Kupfer46, O. Kuprash155, H. Kurashige70, L.L. Kurchaninov163a, Y.A. Kurochkin95, M.G. Kurth35a, V. Kus129, E.S. Kuwertz172, M. Kuze159, J. Kvita117, T. Kwan172, D. Kyriazopoulos141, A. La Rosa103, J.L. La Rosa Navarro26d, L. La Rotonda40a,40b, F. La Ruffa40a,40b, C. Lacasta170, F. Lacava134a,134b, J. Lacey45, D.P.J. Lack87, H. Lacker17, D. Lacour83, E. Ladygin68, R. Lafaye5, B. Laforge83, T. Lagouri179, S. Lai57, S. Lammers64, W. Lampl7, E. Lan¸con27, U. Landgraf51, M.P.J. Landon79, M.C. Lanfermann52, V.S. Lang45, J.C. Lange13, R.J. Langenberg32, A.J. Lankford166, F. Lanni27, K. Lantzsch23, A. Lanza123a, A. Lapertosa53a,53b, S. Laplace83, J.F. Laporte138, T. Lari94a, F. Lasagni Manghi22a,22b, M. Lassnig32, T.S. Lau62a, P. Laurelli50, W. Lavrijsen16, A.T. Law139, P. Laycock77, T. Lazovich59, M. Lazzaroni94a,94b, B. Le91, O. Le Dortz83, E. Le Guirriec88, E.P. Le Quilleuc138, M. LeBlanc172, T. LeCompte6, F. Ledroit-Guillon58, C.A. Lee27, G.R. Lee133,af , S.C. Lee153, L. Lee59, B. Lefebvre90, G. Lefebvre83, M. Lefebvre172, F. Legger102, C. Leggett16, G. Lehmann Miotto32, X. Lei7, W.A. Leight45, M.A.L. Leite26d, R. Leitner131, D. Lellouch175, B. Lemmer57, K.J.C. Leney81, T. Lenz23, B. Lenzi32, R. Leone7, S. Leone126a,126b, C. Leonidopoulos49, G. Lerner151, C. Leroy97, A.A.J. Lesage138, C.G. Lester30, M. Levchenko125, J. Levˆeque5, D. Levin92, L.J. Levinson175, M. Levy19, D. Lewis79, B. Li36a,ag, Changqiao Li36a, H. Li150, L. Li36c, Q. Li35a, Q. Li36a, S. Li48, X. Li36c, Y. Li143, Z. Liang35a, B. Liberti135a, A. Liblong161, K. Lie62c, J. Liebal23, W. Liebig15, A. Limosani152, S.C. Lin182, T.H. Lin86, R.A. Linck64, B.E. Lindquist150, A.E. Lionti52, E. Lipeles124, A. Lipniacka15, M. Lisovyi60b, T.M. Liss169,ah, A. Lister171, A.M. Litke139, B. Liu67, H. Liu92, H. Liu27, J.K.K. Liu122, J. Liu36b, J.B. Liu36a, K. Liu88, L. Liu169, M. Liu36a, Y.L. Liu36a, Y. Liu36a, M. Livan123a,123b, A. Lleres58, J. Llorente Merino35a, S.L. Lloyd79, C.Y. Lo62b, F. Lo Sterzo153, E.M. Lobodzinska45, P. Loch7, F.K. Loebinger87, A. Loesle51, K.M. Loew25, A. Loginov179,∗, T. Lohse17, K. Lohwasser141, M. Lokajicek129, B.A. Long24, J.D. Long169, R.E. Long75, L. Longo76a,76b, K.A. Looper113, J.A. Lopez34b, D. Lopez Mateos59, I. Lopez Paz13, A. Lopez Solis83, J. Lorenz102, N. Lorenzo Martinez5, M. Losada21, P.J. L¨osel102, X. Lou35a, A. Lounis119, J. Love6, P.A. Love75, H. Lu62a, N. Lu92, Y.J. Lu63, H.J. Lubatti140, C. Luci134a,134b, A. Lucotte58, C. Luedtke51, F. Luehring64, W. Lukas65, L. Luminari134a, O. Lundberg148a,148b, B. Lund-Jensen149, M.S. Lutz89, P.M. Luzi83, D. Lynn27, R. Lysak129, E. Lytken84, F. Lyu35a, V. Lyubushkin68, H. Ma27, L.L. Ma36b, Y. Ma36b, G. Maccarrone50, A. Macchiolo103, C.M. Macdonald141, B. Maˇcek78, J. Machado Miguens124,128b, D. Madaffari170, R. Madar37, W.F. Mader47, A. Madsen45, J. Maeda70, S. Maeland15, T. Maeno27, A.S. Maevskiy101, V. Magerl51, J. Mahlstedt109, C. Maiani119, C. Maidantchik26a, A.A. Maier103, T. Maier102, A. Maio128a,128b,128d, O. Majersky146a, S. Majewski118, Y. Makida69, N. Makovec119, B. Malaescu83, Pa. Malecki42, V.P. Maleev125, F. Malek58, U. Mallik66, D. Malon6, C. Malone30, S. Maltezos10, S. Malyukov32, J. Mamuzic170, G. Mancini50, I. Mandi´c78, J. Maneira128a,128b, L. Manhaes de Andrade Filho26b, J. Manjarres Ramos47, K.H. Mankinen84, A. Mann102, A. Manousos32, B. Mansoulie138, – 38 – JHEP11(2017)195 J.D. Mansour35a, R. Mantifel90, M. Mantoani57, S. Manzoni94a,94b, L. Mapelli32, G. Marceca29, L. March52, L. Marchese122, G. Marchiori83, M. Marcisovsky129, C.A. Marin Tobon32, M. Marjanovic37, D.E. Marley92, F. Marroquim26a, S.P. Marsden87, Z. Marshall16, M.U.F Martensson168, S. Marti-Garcia170, C.B. Martin113, T.A. Martin173, V.J. Martin49, B. Martin dit Latour15, M. Martinez13,v, V.I. Martinez Outschoorn169, S. Martin-Haugh133, V.S. Martoiu28b, A.C. Martyniuk81, A. Marzin32, L. Masetti86, T. Mashimo157, R. Mashinistov98, J. Masik87, A.L. Maslennikov111,c, L. Massa135a,135b, P. Mastrandrea5, A. Mastroberardino40a,40b, T. Masubuchi157, P. M¨attig178, J. Maurer28b, S.J. Maxfield77, D.A. Maximov111,c, R. Mazini153, I. Maznas156, S.M. Mazza94a,94b, N.C. Mc Fadden107, G. Mc Goldrick161, S.P. Mc Kee92, A. McCarn92, R.L. McCarthy150, T.G. McCarthy103, L.I. McClymont81, E.F. McDonald91, J.A. Mcfayden32, G. Mchedlidze57, S.J. McMahon133, P.C. McNamara91, C.J. McNicol173, R.A. McPherson172,o, S. Meehan140, T.J. Megy51, S. Mehlhase102, A. Mehta77, T. Meideck58, K. Meier60a, B. Meirose44, D. Melini170,ai, B.R. Mellado Garcia147c, J.D. Mellenthin57, M. Melo146a, F. Meloni18, A. Melzer23, S.B. Menary87, L. Meng77, X.T. Meng92, A. Mengarelli22a,22b, S. Menke103, E. Meoni40a,40b, S. Mergelmeyer17, C. Merlassino18, P. Mermod52, L. Merola106a,106b, C. Meroni94a, F.S. Merritt33, A. Messina134a,134b, J. Metcalfe6, A.S. Mete166, C. Meyer124, J-P. Meyer138, J. Meyer109, H. Meyer Zu Theenhausen60a, F. Miano151, R.P. Middleton133, S. Miglioranzi53a,53b, L. Mijovi´c49, G. Mikenberg175, M. Mikestikova129, M. Mikuˇz78, M. Milesi91, A. Milic161, D.A. Millar79, D.W. Miller33, C. Mills49, A. Milov175, D.A. Milstead148a,148b, A.A. Minaenko132, Y. Minami157, I.A. Minashvili54b, A.I. Mincer112, B. Mindur41a, M. Mineev68, Y. Minegishi157, Y. Ming176, L.M. Mir13, K.P. Mistry124, T. Mitani174, J. Mitrevski102, V.A. Mitsou170, A. Miucci18, P.S. Miyagawa141, A. Mizukami69, J.U. Mj¨ornmark84, T. Mkrtchyan180, M. Mlynarikova131, T. Moa148a,148b, K. Mochizuki97, P. Mogg51, S. Mohapatra38, S. Molander148a,148b, R. Moles-Valls23, M.C. Mondragon93, K. M¨onig45, J. Monk39, E. Monnier88, A. Montalbano150, J. Montejo Berlingen32, F. Monticelli74, S. Monzani94a,94b, R.W. Moore3, N. Morange119, D. Moreno21, M. Moreno Ll´acer32, P. Morettini53a, S. Morgenstern32, D. Mori144, T. Mori157, M. Morii59, M. Morinaga174, V. Morisbak121, A.K. Morley32, G. Mornacchi32, J.D. Morris79, L. Morvaj150, P. Moschovakos10, M. Mosidze54b, H.J. Moss141, J. Moss145,aj, K. Motohashi159, R. Mount145, E. Mountricha27, E.J.W. Moyse89, S. Muanza88, F. Mueller103, J. Mueller127, R.S.P. Mueller102, D. Muenstermann75, P. Mullen56, G.A. Mullier18, F.J. Munoz Sanchez87, W.J. Murray173,133, H. Musheghyan32, M. Muˇskinja78, A.G. Myagkov132,ak, M. Myska130, B.P. Nachman16, O. Nackenhorst52, K. Nagai122, R. Nagai69,ad, K. Nagano69, Y. Nagasaka61, K. Nagata164, M. Nagel51, E. Nagy88, A.M. Nairz32, Y. Nakahama105, K. Nakamura69, T. Nakamura157, I. Nakano114, R.F. Naranjo Garcia45, R. Narayan11, D.I. Narrias Villar60a, I. Naryshkin125, T. Naumann45, G. Navarro21, R. Nayyar7, H.A. Neal92, P.Yu. Nechaeva98, T.J. Neep138, A. Negri123a,123b, M. Negrini22a, S. Nektarijevic108, C. Nellist119, A. Nelson166, M.E. Nelson122, S. Nemecek129, P. Nemethy112, M. Nessi32,al, M.S. Neubauer169, M. Neumann178, P.R. Newman19, T.Y. Ng62c, T. Nguyen Manh97, R.B. Nickerson122, R. Nicolaidou138, J. Nielsen139, V. Nikolaenko132,ak, I. Nikolic-Audit83, K. Nikolopoulos19, J.K. Nilsen121, P. Nilsson27, Y. Ninomiya157, A. Nisati134a, N. Nishu36c, R. Nisius103, I. Nitsche46, T. Nitta174, T. Nobe157, Y. Noguchi71, M. Nomachi120, I. Nomidis31, M.A. Nomura27, T. Nooney79, M. Nordberg32, N. Norjoharuddeen122, O. Novgorodova47, M. Nozaki69, L. Nozka117, K. Ntekas166, E. Nurse81, F. Nuti91, K. O’connor25, D.C. O’Neil144, A.A. O’Rourke45, V. O’Shea56, F.G. Oakham31,d, H. Oberlack103, T. Obermann23, J. Ocariz83, A. Ochi70, I. Ochoa38, J.P. Ochoa-Ricoux34a, S. Oda73, S. Odaka69, A. Oh87, S.H. Oh48, C.C. Ohm16, H. Ohman168, H. Oide53a,53b, H. Okawa164, Y. Okumura157, T. Okuyama69, A. Olariu28b, L.F. Oleiro Seabra128a, S.A. Olivares Pino34a, D. Oliveira Damazio27, A. Olszewski42, – 39 – JHEP11(2017)195 J. Olszowska42, A. Onofre128a,128e, K. Onogi105, P.U.E. Onyisi11,z, H. Oppen121, M.J. Oreglia33, Y. Oren155, D. Orestano136a,136b, N. Orlando62b, R.S. Orr161, B. Osculati53a,53b,∗, R. Ospanov36a, G. Otero y Garzon29, H. Otono73, M. Ouchrif137d, F. Ould-Saada121, A. Ouraou138, K.P. Oussoren109, Q. Ouyang35a, M. Owen56, R.E. Owen19, V.E. Ozcan20a, N. Ozturk8, K. Pachal144, A. Pacheco Pages13, L. Pacheco Rodriguez138, C. Padilla Aranda13, S. Pagan Griso16, M. Paganini179, F. Paige27, G. Palacino64, S. Palazzo40a,40b, S. Palestini32, M. Palka41b, D. Pallin37, E.St. Panagiotopoulou10, I. Panagoulias10, C.E. Pandini126a,126b, J.G. Panduro Vazquez80, P. Pani32, S. Panitkin27, D. Pantea28b, L. Paolozzi52, Th.D. Papadopoulou10, K. Papageorgiou9,s, A. Paramonov6, D. Paredes Hernandez179, A.J. Parker75, M.A. Parker30, K.A. Parker45, F. Parodi53a,53b, J.A. Parsons38, U. Parzefall51, V.R. Pascuzzi161, J.M. Pasner139, E. Pasqualucci134a, S. Passaggio53a, Fr. Pastore80, S. Pataraia86, J.R. Pater87, T. Pauly32, B. Pearson103, S. Pedraza Lopez170, R. Pedro128a,128b, S.V. Peleganchuk111,c, O. Penc129, C. Peng35a, H. Peng36a, J. Penwell64, B.S. Peralva26b, M.M. Perego138, D.V. Perepelitsa27, F. Peri17, L. Perini94a,94b, H. Pernegger32, S. Perrella106a,106b, R. Peschke45, V.D. Peshekhonov68,∗, K. Peters45, R.F.Y. Peters87, B.A. Petersen32, T.C. Petersen39, E. Petit58, A. Petridis1, C. Petridou156, P. Petroff119, E. Petrolo134a, M. Petrov122, F. Petrucci136a,136b, N.E. Pettersson89, A. Peyaud138, R. Pezoa34b, F.H. Phillips93, P.W. Phillips133, G. Piacquadio150, E. Pianori173, A. Picazio89, E. Piccaro79, M.A. Pickering122, R. Piegaia29, J.E. Pilcher33, A.D. Pilkington87, A.W.J. Pin87, M. Pinamonti135a,135b, J.L. Pinfold3, H. Pirumov45, M. Pitt175, L. Plazak146a, M.-A. Pleier27, V. Pleskot86, E. Plotnikova68, D. Pluth67, P. Podberezko111, R. Poettgen84, R. Poggi123a,123b, L. Poggioli119, I. Pogrebnyak93, D. Pohl23, G. Polesello123a, A. Poley45, A. Policicchio40a,40b, R. Polifka32, A. Polini22a, C.S. Pollard56, V. Polychronakos27, K. Pomm`es32, D. Ponomarenko100, L. Pontecorvo134a, G.A. Popeneciu28d, D.M. Portillo Quintero83, S. Pospisil130, K. Potamianos16, I.N. Potrap68, C.J. Potter30, H. Potti11, T. Poulsen84, J. Poveda32, M.E. Pozo Astigarraga32, P. Pralavorio88, A. Pranko16, S. Prell67, D. Price87, M. Primavera76a, S. Prince90, N. Proklova100, K. Prokofiev62c, F. Prokoshin34b, S. Protopopescu27, J. Proudfoot6, M. Przybycien41a, A. Puri169, P. Puzo119, J. Qian92, G. Qin56, Y. Qin87, A. Quadt57, M. Queitsch-Maitland45, D. Quilty56, S. Raddum121, V. Radeka27, V. Radescu122, S.K. Radhakrishnan150, P. Radloff118, P. Rados91, F. Ragusa94a,94b, G. Rahal181, J.A. Raine87, S. Rajagopalan27, C. Rangel-Smith168, T. Rashid119, S. Raspopov5, M.G. Ratti94a,94b, D.M. Rauch45, F. Rauscher102, S. Rave86, I. Ravinovich175, J.H. Rawling87, M. Raymond32, A.L. Read121, N.P. Readioff58, M. Reale76a,76b, D.M. Rebuzzi123a,123b, A. Redelbach177, G. Redlinger27, R. Reece139, R.G. Reed147c, K. Reeves44, L. Rehnisch17, J. Reichert124, A. Reiss86, C. Rembser32, H. Ren35a, M. Rescigno134a, S. Resconi94a, E.D. Resseguie124, S. Rettie171, E. Reynolds19, O.L. Rezanova111,c, P. Reznicek131, R. Rezvani97, R. Richter103, S. Richter81, E. Richter-Was41b, O. Ricken23, M. Ridel83, P. Rieck103, C.J. Riegel178, J. Rieger57, O. Rifki115, M. Rijssenbeek150, A. Rimoldi123a,123b, M. Rimoldi18, L. Rinaldi22a, G. Ripellino149, B. Risti´c32, E. Ritsch32, I. Riu13, F. Rizatdinova116, E. Rizvi79, C. Rizzi13, R.T. Roberts87, S.H. Robertson90,o, A. Robichaud-Veronneau90, D. Robinson30, J.E.M. Robinson45, A. Robson56, E. Rocco86, C. Roda126a,126b, Y. Rodina88,am, S. Rodriguez Bosca170, A. Rodriguez Perez13, D. Rodriguez Rodriguez170, S. Roe32, C.S. Rogan59, O. Røhne121, J. Roloff59, A. Romaniouk100, M. Romano22a,22b, S.M. Romano Saez37, E. Romero Adam170, N. Rompotis77, M. Ronzani51, L. Roos83, S. Rosati134a, K. Rosbach51, P. Rose139, N.-A. Rosien57, E. Rossi106a,106b, L.P. Rossi53a, J.H.N. Rosten30, R. Rosten140, M. Rotaru28b, J. Rothberg140, D. Rousseau119, A. Rozanov88, Y. Rozen154, X. Ruan147c, F. Rubbo145, F. R¨uhr51, A. Ruiz-Martinez31, Z. Rurikova51, N.A. Rusakovich68, – 40 – JHEP11(2017)195 de Ciˆencias, Universidade de Lisboa, Lisboa; (c)Department of Physics, University of Coimbra, Coimbra; (d)Centro de F´ısica Nuclear da Universidade de Lisboa, Lisboa; (e)Departamento de Fisica, Universidade do Minho, Braga; (f)Departamento de Fisica Teorica y del Cosmos, Universidad de Granada, Granada; (g)Dep Fisica and CEFITEC of Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal 129 Institute of Physics, Academy of Sciences of the Czech Republic, Praha, Czech Republic 130 Czech Technical University in Prague, Praha, Czech Republic 131 Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic 132 State Research Center Institute for High Energy Physics (Protvino), NRC KI, Russia 133 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, United Kingdom 134 (a)INFN Sezione di Roma; (b)Dipartimento di Fisica, Sapienza Universit`a di Roma, Roma, Italy 135 (a)INFN Sezione di Roma Tor Vergata; (b)Dipartimento di Fisica, Universit`a di Roma Tor Vergata, Roma, Italy 136 (a)INFN Sezione di Roma Tre; (b)Dipartimento di Matematica e Fisica, Universit`a Roma Tre, Roma, Italy 137 (a)Facult´e des Sciences Ain Chock, R´eseau Universitaire de Physique des Hautes Energies - Universit´e Hassan II, Casablanca; (b)Centre National de l’Energie des Sciences Techniques Nucleaires, Rabat; (c)Facult´e des Sciences Semlalia, Universit´e Cadi Ayyad, LPHEA-Marrakech; (d)Facult´e des Sciences, Universit´e Mohamed Premier and LPTPM, Oujda; (e)Facult´e des sciences, Universit´e Mohammed V, Rabat, Morocco 138 DSM/IRFU (Institut de Recherches sur les Lois Fondamentales de l’Univers), CEA Saclay (Commissariat `a l’Energie Atomique et aux Energies Alternatives), Gif-sur-Yvette, France 139 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA, United States of America 140 Department of Physics, University of Washington, Seattle WA, United States of America 141 Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom 142 Department of Physics, Shinshu University, Nagano, Japan 143 Department Physik, Universit¨at Siegen, Siegen, Germany 144 Department of Physics, Simon Fraser University, Burnaby BC, Canada 145 SLAC National Accelerator Laboratory, Stanford CA, United States of America 146 (a)Faculty of Mathematics, Physics & Informatics, Comenius University, Bratislava; (b) Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic 147 (a)Department of Physics, University of Cape Town, Cape Town; (b)Department of Physics, University of Johannesburg, Johannesburg; (c)School of Physics, University of the Witwatersrand, Johannesburg, South Africa 148 (a)Department of Physics, Stockholm University; (b)The Oskar Klein Centre, Stockholm, Sweden 149 Physics Department, Royal Institute of Technology, Stockholm, Sweden 150 Departments of Physics & Astronomy and Chemistry, Stony Brook University, Stony Brook NY, United States of America 151 Department of Physics and Astronomy, University of Sussex, Brighton, United Kingdom 152 School of Physics, University of Sydney, Sydney, Australia 153 Institute of Physics, Academia Sinica, Taipei, Taiwan 154 Department of Physics, Technion: Israel Institute of Technology, Haifa, Israel 155 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 156 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 157 International Center for Elementary Particle Physics and Department of Physics, The University of Tokyo, Tokyo, Japan 158 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 159 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 160 Tomsk State University, Tomsk, Russia – 47 – JHEP11(2017)195 161 Department of Physics, University of Toronto, Toronto ON, Canada 162 (a)INFN-TIFPA; (b)University of Trento, Trento, Italy 163 (a)TRIUMF, Vancouver BC; (b)Department of Physics and Astronomy, York University, Toronto ON, Canada 164 Faculty of Pure and Applied Sciences, and Center for Integrated Research in Fundamental Science and Engineering, University of Tsukuba, Tsukuba, Japan 165 Department of Physics and Astronomy, Tufts University, Medford MA, United States of America 166 Department of Physics and Astronomy, University of California Irvine, Irvine CA, United States of America 167 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine; (b)ICTP, Trieste; (c)Dipartimento di Chimica, Fisica e Ambiente, Universit`a di Udine, Udine, Italy 168 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 169 Department of Physics, University of Illinois, Urbana IL, United States of America 170 Instituto de Fisica Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Spain 171 Department of Physics, University of British Columbia, Vancouver BC, Canada 172 Department of Physics and Astronomy, University of Victoria, Victoria BC, Canada 173 Department of Physics, University of Warwick, Coventry, United Kingdom 174 Waseda University, Tokyo, Japan 175 Department of Particle Physics, The Weizmann Institute of Science, Rehovot, Israel 176 Department of Physics, University of Wisconsin, Madison WI, United States of America 177 Fakult¨at f¨ur Physik und Astronomie, Julius-Maximilians-Universit¨at, W¨urzburg, Germany 178 Fakult¨at f¨ur Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universit¨at Wuppertal, Wuppertal, Germany 179 Department of Physics, Yale University, New Haven CT, United States of America 180 Yerevan Physics Institute, Yerevan, Armenia 181 Centre de Calcul de l’Institut National de Physique Nucl´eaire et de Physique des Particules (IN2P3), Villeurbanne, France 182 Academia Sinica Grid Computing, Institute of Physics, Academia Sinica, Taipei, Taiwan aAlso at Department of Physics, King’s College London, London, United Kingdom bAlso at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan cAlso at Novosibirsk State University, Novosibirsk, Russia dAlso at TRIUMF, Vancouver BC, Canada eAlso at Department of Physics & Astronomy, University of Louisville, Louisville, KY, United States of America fAlso at Physics Department, An-Najah National University, Nablus, Palestine gAlso at Department of Physics, California State University, Fresno CA, United States of America hAlso at Department of Physics, University of Fribourg, Fribourg, Switzerland iAlso at II Physikalisches Institut, Georg-August-Universit¨at, G¨ottingen, Germany jAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain kAlso at Departamento de Fisica e Astronomia, Faculdade de Ciencias, Universidade do Porto, Portugal lAlso at Tomsk State University, Tomsk, and Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia mAlso at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China nAlso at Universita di Napoli Parthenope, Napoli, Italy oAlso at Institute of Particle Physics (IPP), Canada pAlso at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania qAlso at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia rAlso at Borough of Manhattan Community College, City University of New York, New York City, United States of America – 48 – JHEP11(2017)195 sAlso at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece tAlso at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa uAlso at Louisiana Tech University, Ruston LA, United States of America vAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain wAlso at Graduate School of Science, Osaka University, Osaka, Japan xAlso at Fakult¨at f¨ur Mathematik und Physik, Albert-Ludwigs-Universit¨at, Freiburg, Germany yAlso at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands zAlso at Department of Physics, The University of Texas at Austin, Austin TX, United States of America aa Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia ab Also at CERN, Geneva, Switzerland ac Also at Georgian Technical University (GTU),Tbilisi, Georgia ad Also at Ochadai Academic Production, Ochanomizu University, Tokyo, Japan ae Also at Manhattan College, New York NY, United States of America af Also at Departamento de F´ısica, Pontificia Universidad Cat´olica de Chile, Santiago, Chile ag Also at Department of Physics, The University of Michigan, Ann Arbor MI, United States of America ah Also at The City College of New York, New York NY, United States of America ai Also at Departamento de Fisica Teorica y del Cosmos, Universidad de Granada, Granada, Portugal aj Also at Department of Physics, California State University, Sacramento CA, United States of America ak Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia al Also at Departement de Physique Nucleaire et Corpusculaire, Universit´e de Gen`eve, Geneva, Switzerland am Also at Institut de F´ısica d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain an Also at School of Physics, Sun Yat-sen University, Guangzhou, China ao Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia, Bulgaria ap Also at Faculty of Physics, M.V.Lomonosov Moscow State University, Moscow, Russia aq Also at National Research Nuclear University MEPhI, Moscow, Russia ar Also at Department of Physics, Stanford University, Stanford CA, United States of America as Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary at Also at Giresun University, Faculty of Engineering, Turkey au Also at CPPM, Aix-Marseille Universit´e and CNRS/IN2P3, Marseille, France av Also at Department of Physics, Nanjing University, Jiangsu, China aw Also at Institute of Physics, Academia Sinica, Taipei, Taiwan ax Also at University of Malaya, Department of Physics, Kuala Lumpur, Malaysia ay Also at LAL, Univ. Paris-Sud, CNRS/IN2P3, Universit´e Paris-Saclay, Orsay, France ∗Deceased – 49 –