Search for top squark pair production in final states with one isolated lepton, jets, and missing transverse momentum in √s = 8 TeV pp collisions with the ATLAS detector

JHEP11(2014)118 Published for SISSA by Springer Received:July 3, 2014 Revised:September 23, 2014 Accepted:October 30, 2014 Published:November 21, 2014 Search for top squark pair production in final states with one isolated lepton, jets, and missing transverse momentum in √s= 8 TeV pp collisions with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: The results of a search for top squark (stop) pair production in final states with one isolated lepton, jets, and missing transverse momentum are reported. The analysis is performed with proton-proton collision data at √s= 8 TeV collected with the ATLAS detector at the LHC in 2012 corresponding to an integrated luminosity of 20 fb−1. The lightest supersymmetric particle (LSP) is taken to be the lightest neutralino which only interacts weakly and is assumed to be stable. The stop decay modes considered are those to a top quark and the LSP as well as to a bottom quark and the lightest chargino, where the chargino decays to the LSP by emitting a Wboson. A wide range of scenarios with different mass splittings between the stop, the lightest neutralino and the lightest chargino are considered, including cases where the Wbosons or the top quarks are off-shell. Decay modes involving the heavier charginos and neutralinos are addressed using a set of phenomenological models of supersymmetry. No significant excess over the Standard Model prediction is observed. A stop with a mass between 210 and 640 GeV decaying directly to a top quark and a massless LSP is excluded at 95% confidence level, and in models where the mass of the lightest chargino is twice that of the LSP, stops are excluded at 95% confidence level up to a mass of 500 GeV for an LSP mass in the range of 100 to 150 GeV. Stringent exclusion limits are also derived for all other stop decay modes considered, and model-independent upper limits are set on the visible cross-section for processes beyond the Standard Model. Keywords: Hadron-Hadron Scattering, proton-proton scattering, supersymmetry, top squark ArXiv ePrint: 1407.0583 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. doi:10.1007/JHEP11(2014)118 JHEP11(2014)118 Contents 1 Introduction 2 2 Analysis strategy 3 3 The ATLAS detector 5 4 Trigger and data collection 6 5 Simulated samples 7 5.1 Background samples 7 5.2 Signal samples 8 6 Physics object reconstruction and discriminating variables 10 6.1 Physics object reconstruction 10 6.2 Tools to discriminate signal from background 14 7 Signal selections 16 7.1 Event preselection 17 7.2 Selections for the ˜ t1→t˜χ0 1decay 18 7.3 Selections for the ˜ t1→b˜χ± 1decay 21 7.4 Selections for the mixed, threeand four-body decays 27 8 Background estimates 28 8.1 Control regions 29 8.2 Validation 32 9 Systematic uncertainties 35 10 Results 38 11 Summary and conclusions 45 A Detailed description of the discriminating variables 53 B Background fit results 56 The ATLAS collaboration 70 – 1 – JHEP11(2014)118 1 Introduction The hierarchy problem [1–4] has gained additional attention with the observation of a new particle consistent with the Standard Model (SM) Higgs boson [5,6] at the LHC [7]. Supersymmetry (SUSY) [8–16], which extends the SM by introducing supersymmetric partners for all SM particles, provides an elegant solution to the hierarchy problem. The partner particles have identical quantum numbers except for a half-unit difference in spin. The superpartners of the leftand right-handed top quarks, ˜ tLand ˜ tR, mix to form the two mass eigenstates ˜ t1and ˜ t2, where ˜ t1(top squark or stop) is the lighter one. If the supersymmetric partners of the top quarks have masses .1 TeV, loop diagrams involving top quarks, which are the dominant contribution to the divergence of the Higgs boson mass, can be largely cancelled [17–24]. Significant mass splitting between ˜ t1and ˜ t2is possible due to the large top Yukawa coupling.1Furthermore, effects of the renormalisation group equations are strong for the third generation squarks, usually driving their masses significantly lower than those of the other generations. These considerations suggest a light stop which, together with the stringent LHC limits excluding other coloured supersymmetric particles up to masses at the TeV level, motivates dedicated stop searches. SUSY models can violate the conservation of baryon number and lepton number, resulting in a proton lifetime shorter than current experimental limits [25]. This is commonly solved by introducing a multiplicative quantum number called R-parity, which is 1 and −1 for all SM and SUSY particles, respectively. A generic R-parity-conserving minimal supersymmetric extension of the SM (MSSM) [17,26–29] predicts pair production of SUSY particles and the existence of a stable lightest supersymmetric particle (LSP). In a large variety of SUSY models, the lightest neutralino2(˜χ0 1) is the LSP, which is also the assumption throughout this paper. Since the ˜χ0 1interacts only weakly it is a candidate for dark matter. The stop can decay into a variety of final states, depending amongst other things on the SUSY particle mass spectrum, in particular on the masses of the stop and the lightest neutralino. Figure 1illustrates the simplest decay modes as a function of the stop and LSP masses. In the rightmost wedge, the stop mass is greater than the sum of the top quark and the LSP masses, hence the decay ˜ t1→t˜χ0 1is kinematically allowed. A lighter stop can undergo a three-body decay ˜ t1→bW ˜χ0 1if the stop mass is still above the b+W+˜χ0 1 mass. For an even lighter stop, the decay proceeds via a four-body process ˜ t1→bff0˜χ0 1, where fand f0are two distinct fermions, or flavour-changing neutral current (FCNC) processes, such as the loop-suppressed ˜ t1→c˜χ0 1. If supersymmetric particles other than the ˜χ0 1are lighter than the stop, then additional decay modes can open up. The stop decay to a bottom quark and the lightest chargino (˜ t1→b˜χ± 1) is an important example, where the ˜χ± 1can decay to the lightest neutralino by emitting an onor off-shell Wboson 1The masses of the ˜ t1and ˜ t2are given by the eigenvalues of the stop mass matrix. The stop mass matrix involves the top-quark Yukawa coupling in the off-diagonal elements, which typically induces a large mass splitting. The stop mass matrix is diagonalised by the stop mixing matrix, which gives the ˜ tLand ˜ tR components of the mass eigenstates ˜ t1and ˜ t2. 2The charginos ˜χ± 1,2and neutralinos ˜χ0 1,2,3,4are the mass eigenstates formed from the linear superposition of the charged and neutral SUSY partners of the Higgs and electroweak gauge bosons (higgsinos, winos and binos). – 2 – JHEP11(2014)118 Figure 1. Illustration of stop decay modes in the plane spanned by the masses of the stop (˜ t1) and the lightest neutralino (˜χ0 1), where the latter is assumed to be the lightest supersymmetric particle. Stop decays to supersymmetric particles other than the lightest supersymmetric particle are not displayed. (˜χ± 1→W(∗)˜χ0 1). The ˜ t1→b˜χ± 1decay is considered for a stop mass above around 100 GeV since the LEP limit on the lightest chargino is m˜χ± 1>103.5 GeV [30]. This article presents a search for direct ˜ t1pair production in final states with exactly one isolated charged lepton (electron or muon,3henceforth referred to simply as ‘leptons’), several jets, and a significant amount of missing transverse momentum, the magnitude of which is referred to as Emiss T. The lepton arises from the decay of either a real or a virtual Wboson, and the potentially large Emiss Tis generated by the two undetected LSPs and neutrino(s). All stop decay modes described above except for the FCNC modes are considered, as illustrated in figure 2. With several decay modes kinematically available, the ˜ t1decay branching ratio is determined by factors including the stop mixing matrix and the field content of the neutralino/chargino sector. Results are mainly based on simplified models that have 100% branching ratio to one or a pair of these specific decay chains. In addition, phenomenological MSSM (pMSSM) [31] models are used to study the sensitivity to realistic scenarios where more complex decay chains are present alongside the simpler ones. Searches for direct ˜ t1pair production have previously been reported by the ATLAS [32– 38] and CMS [39–43] collaborations, as well as by the CDF and DØ collaborations (for example refs. [44,45]) and the LEP collaborations [46]. Indirect searches for stops, mediated by gluino pair production, have been reported by the ATLAS [47–50] and CMS [39,40,51– 55] collaborations. 2 Analysis strategy Searching for ˜ t1pair production in the various decay modes and over a wide range of stop masses requires different analysis approaches. The ˜ t1pair production cross-section falls 3Electrons and muons from τdecays are included. – 3 – JHEP11(2014)118 ˜ t1 ˜ t1 tW tW p p ˜χ0 1 bℓ ν ˜χ0 1 b q q (a) ˜ t1 ˜ t1 W W p p ˜χ0 1 bℓ ν ˜χ0 1 b q q (b) ˜ t1 ˜ t1 p p ˜χ0 1 b ℓ ν ˜χ0 1 b q q (c) ˜ t1 ˜ t1 ˜χ± 1 W(∗) ˜χ∓ 1 W(∗) p p b ˜χ0 1 ℓ ν b ˜χ0 1 q q (d) Figure 2. Diagrams illustrating the considered signal scenarios, which are referred to as (a) ˜ t1→t˜χ0 1, (b) ˜ t1→bW ˜χ0 1(three-body), (c) ˜ t1→bff0˜χ0 1(four-body), (d) ˜ t1→b˜χ± 1. Furthermore, a non-symmetric decay mode where each ˜ t1can decay via either ˜ t1→t˜χ0 1or ˜ t1→b˜χ± 1is considered (not shown). In these diagrams, the charge-conjugate symbols are omitted for simplicity; all scenarios begin with a top squark-antisquark pair. The three-body and four-body decays are assumed to proceed through an off-shell top quark, and an off-shell top quark followed by an off-shell Wboson, respectively. rapidly with increasing stop mass m˜ t1: for the range targeted by this search, m˜ t1∼100– 700 GeV, the cross-section at √s= 8 TeV proton-proton (pp) collisions decreases from 560 pb to 8 fb. While the various ˜ t1decay modes considered all have identical final state objects — one electron or muon accompanied by one neutrino (or more for a leptonic τ decay), two jets originating from bottom quarks (b-jets), two light-flavour jets, and two LSPs — their kinematic properties change significantly for the different decay modes and as a function of the masses of the stop, LSP, and lightest chargino (if present). The search presented in this paper is based on 15 dedicated analyses that target the various scenarios. The identification of b-jets (b-tagging) is utilised in the event selections and for constructing kinematic variables. The search for a heavy stop exploits a specialised technique, which reconstructs several decay products in a single large-radius (large-R) jet. Low-momentum leptons (referred to as soft leptons) are reconstructed and identified to – 4 – JHEP11(2014)118 enhance the sensitivity for ˜ t1→b˜χ± 1decays where the ˜χ0 1and ˜χ± 1states are close in mass. These and other tools and variables to discriminate signal from background, described in section 6, are used to design sets of requirements for the event selection. Each of these sets of requirements is referred to as a signal region (SR), and is optimised to target one or more signal scenarios. Furthermore, two different analysis techniques are employed, which are referred to as ‘cut-and-count’ and ‘shape-fit’. The former is based on counting events in a single region of phase space (bin), while the latter employs several bins. By utilising different signal-to-background ratios in the various bins, shape-fits enhance the search sensitivity in challenging scenarios, where it is particularly difficult to separate signal from background. All SRs are described in section 7. The dominant background in most SRs arises from top quark pair production (t¯ t) where both Wbosons decay leptonically (dileptonic t¯ t) but one of the leptons is not identified, is outside the detector acceptance, or is a hadronically decaying τlepton. The sub-leading background for most SRs stems from W+jets production. As part of each analysis, the t¯ t and W+jets backgrounds are estimated using dedicated control regions (CRs), making the analysis more robust against potential mis-modelling effects in simulated events and reducing the uncertainties on the background estimates. Other small backgrounds are estimated using simulation only. Dedicated samples are used to validate the background predictions. The background estimation including the definition of all CRs is detailed in section 8. The analysis results are based on maximum likelihood fits, which include the CRs to simultaneously normalise the t¯ tand W+jets backgrounds. Systematic uncertainties due to theoretical and experimental effects are considered for all background and signal processes, and are described in section 9. The final results and interpretations, both in terms of model-dependent exclusion limits on the masses of relevant SUSY particles and modelindependent upper limits on the number of beyond-SM events, are presented in section 10. 3 The ATLAS detector The ATLAS experiment [56] is a multi-purpose particle physics detector with nearly 4πsteradian coverage in solid angle. It consists of an inner detector of tracking devices surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer in a toroidal magnetic field. The inner detector, in combination with the 2 T axial field from the solenoid, provides precision tracking and momentum measurement of charged particles up to |η|= 2.5 and allows efficient b-jet identification.4It consists of a silicon pixel detector, a semiconductor microstrip detector and a straw-tube tracker which also provides transition radiation measurements for electron identification. High-granularity liquid-argon (LAr) sampling electromagnetic calorimeters cover the pseudorapidity range |η|<3.2. The hadronic calorimeter system is based on two different tech4ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point in the centre of the detector and the z-axis along the beam pipe. Cylindrical coordinates (r, φ) are used in the transverse plane, φbeing the azimuthal angle around the beam pipe. The pseudorapidity ηis defined in terms of the polar angle θby η=−ln tan(θ/2), and the angular separation ∆Rin the η–φspace is defined as ∆R=p(∆η)2+ (∆φ)2. – 5 – JHEP11(2014)118 nologies, a scintillator-tile sampling calorimeter (|η|<1.7) and a LAr sampling calorimeter (1.5<|η|<3.2). LAr calorimeters in the most forward region (3.1<|η|<4.9) provide electromagnetic and hadronic measurements. The muon spectrometer has separate trigger and high-precision tracking chambers, the former provide trigger coverage up to |η|= 2.4 while the latter provide muon identification and momentum measurements for |η|<2.7. Events are selected by a three-level trigger system [57], the first level (L1) is implemented in customised hardware while the two high-level triggers (HLT) are software-based. 4 Trigger and data collection The data used in this analysis were collected from March to December 2012 with the LHC operating at a pp centre-of-mass energy of √s= 8 TeV. After application of beam, detector and data quality requirements, the total integrated luminosity is 20.3 fb−1with an uncertainty of 2.8%. The uncertainty is derived, following the methodology detailed in ref. [58], from a preliminary calibration of the luminosity scale from beam-separation scans performed in November 2012. The dataset was recorded using three different types of triggers based on requiring either an electron, a muon, or large Emiss T. The single-electron trigger identifies electrons based on the presence of an energy cluster in the electromagnetic calorimeter with a shower shape consistent with that of an electron, low hadronic leakage, and a matching track in the inner detector. The HLT threshold5on the energy deposit transverse to the beam (ET) is 24 GeV. An electron isolation criterion at the HLT requires the scalar sum of the transverse momenta (pT) of tracks within a cone of radius ∆R= 0.2 around the electron (excluding the electron itself) to be less than 10% of the electron ET. The single-muon trigger identifies muons using tracks reconstructed in the muon spectrometer and inner detector. The pTthreshold at the HLT is 24 GeV. An isolation criterion at the HLT requires the scalar sum of the pTof tracks within a cone of radius ∆R= 0.2 around the muon (excluding the muon itself) to be less than 12% of the muon pT. To recover some of the small efficiency loss for high-pTleptons, events were also collected using complementary single-lepton triggers. These triggers have less stringent shower-shape requirements and no hadronic leakage criterion for electrons, and no isolation criteria, but have an increased ET(pT) threshold of 60 GeV (36 GeV) for electrons (muons). Corrections are applied to the simulated samples to account for small differences between data and simulation in the lepton trigger efficiencies. The Emiss Ttrigger is based on the vector sum of the transverse energies deposited in projective calorimeter trigger towers. A more refined calculation based on the vector sum of all calorimeter cells above noise is made at the HLT. The trigger Emiss Tthreshold at the HLT is 80 GeV, and it is fully efficient for offline-calibrated Emiss T>150 GeV in signal-like events. At the beginning of the 2012 data-taking, the Emiss Ttrigger used in this analysis was disabled for the first three bunch crossings of every bunch train, causing a loss of 0.2 fb−1 in integrated luminosity. 5The trigger thresholds refer to lower requirements on the given quantity, and the HLT thresholds are always more stringent than the corresponding L1 thresholds. – 6 – JHEP11(2014)118 Candidate events in the electron (muon) channel were collected using a logical-OR combination of the single-electron (single-muon) and Emiss Ttriggers. Since the singlelepton trigger thresholds are too high for the soft-lepton selection, these candidate events were recorded using only the Emiss Ttrigger. Consequently, the effective dataset for the soft-lepton selections amounts to an integrated luminosity of 20.1 fb−1. All results quote a rounded value of 20 fb−1, while inside the analysis the appropriate integrated luminosity values are used. The efficiency of the Emiss Tand lepton triggers are measured with W→µν and Z→`` data samples, respectively. In all cases the combined trigger efficiency is greater than 98% for simulated signal events satisfying the selection criteria for the analyses described in sections 7–8. 5 Simulated samples Samples of Monte Carlo (MC) simulated events are used for the description of the background and to model the SUSY signals. As detailed below, the samples are generated with either POWHEG-r2129 [59], ACERMC-3.8 [60], MadGraph-5.1.4.8 [61], SHERPA1.4.1 [62] or Herwig++ 2.5.2 [63]. All POWHEG and SHERPA samples use the next-to-leading-order (NLO) parton distribution function (PDF) set CT10 [64], while samples generated with ACERMC,MadGraph or Herwig++ use the CTEQ6L1 [65] PDF set. The ATLAS Underlying Event Tune 2B [66] is used for all MadGraph samples, while the samples generated with POWHEG or ACERMC use the Perugia 2011C [67] tune and samples generated with Herwig++ use UEEE3 [68]. The SHERPA generator has an integrated underlying event tune. The fragmentation and hadronisation for the POWHEG,ACERMC, and MadGraph samples is performed with PYTHIA-6.426 [69], while SHERPA and Herwig++ use their own built-in models. The samples are processed with either a detector simulation [70] using GEANT4 [71] or a fast simulation framework where the showers in the electromagnetic and hadronic calorimeters are simulated using a parameterised description [72] and GEANT4 is used for the rest of the detector. The fast simulation has been validated against full GEANT4 simulation for several signal models and for the main background, t¯ tproduction. All samples are produced with varying numbers of simulated minimum-bias interactions, generated with PYTHIA-8.160 [73], overlaid on the hard-scattering event to account for multiple pp interactions in the same or nearby bunch crossings (pileup). The average number of interactions per bunch crossing is reweighted to match the distribution in data that varies between approximately 10 and 30. 5.1 Background samples A sample of t¯ tevents is generated with POWHEG using a top quark mass mt= 172.5 GeV. The same top quark mass is used when simulating other signal or background processes involving top quarks. To account for discrepancies between data and simulated t¯ tevents, the simulated sample is reweighted as a function of the pTof the t¯ tsystem; the weights are based on the ATLAS measurement of the differential t¯ tcross-section at 7 TeV, following the method described in ref. [74]. Single top quark production in the s-channel and the – 7 – JHEP11(2014)118 Wt mode are also generated with POWHEG, while the t-channel process is generated with ACERMC. In W t production, the interference with t¯ tat NLO in quantum chromodynamics (QCD) is treated by the diagram removal scheme [75]. Associated production of t¯ tand vector bosons (W,Zand WW) as well as single top production in association with aZboson, are generated with MadGraph with up to two additional partons. Samples of W+jets and Z/γ∗+ jets are produced with SHERPA, containing up to four additional partons and the correct treatment of bottom and charm quark masses. The diboson processes (WW,ZZ and WZ) are also generated with SHERPA. The processes are normalised using theoretical inclusive cross-sections, including higher-order QCD corrections where available. The t¯ tproduction cross-section is calculated at next-to-next-to-leading order (NNLO) in QCD including resummation of next-to-nextto-leading logarithmic (NNLL) soft gluon terms with top++2.0 [76–81]. Cross-sections for single top quark production are calculated to approximate NLO+NNLL precision [82–84]. The production of t¯ tin association with vector bosons is calculated at NLO [85,86], while the production of a single top quark in association with a Zboson is normalised to the LO cross-sections from the generator, because NLO calculations are only available for t-channel production [87]. The cross-sections for the production of Wand Zbosons are calculated with DYNNLO [88]. The production cross-sections for electroweak diboson production are calculated at NLO with MCFM [89,90]. The t¯ t, single top, W,Z, and diboson calculations use the MSTW2008 NNLO PDF set [91], while the cross-sections for t¯ tin association with a vector boson use the MSTW2008 NLO (W) or CTEQ6.6M [92] (Z) PDF set. The cross-sections for t¯ tand Wproduction are used for the optimisation of the selections, while for the final results the two processes are normalised to data in control regions. 5.2 Signal samples Signal samples of top squark-antisquark pairs are generated with different stop decay and mass configurations. The first scenario assumes the ˜ t1→t˜χ0 1decay with a branching ratio (BR) of 100%. The samples are generated with Herwig++ in a grid across the plane of ˜ t1and ˜χ0 1masses with a spacing of 50 GeV for most of the plane; the grid is more finely sampled towards the diagonal region where m˜ t1approaches mt+m˜χ0 1. The ˜ t1is chosen to be mostly the partner of the right-handed top quark6and the ˜χ0 1to be almost pure bino. This choice is consistent with a large BR for the given ˜ t1decay. Different hypotheses on the left/right mixing in the stop sector and the nature of the neutralino lead to different acceptance values. The acceptance is affected because the polarisation of the top quark changes as a function of the field content of the supersymmetric particles, which impacts the boost of the lepton in the top quark decay. A subset of models where the ˜ t1is purely ˜ tLare studied to quantify this effect. The second signal scenario assumes the ˜ t1→b˜χ± 1→bW(∗)˜χ0 1decay with a BR of 100%.7The stop pairs are always generated with MadGraph, while for the ˜ t1decay either MadGraph or PYTHIA is employed. For models where the Wboson is on-shell, 6The ˜ tRcomponent is given by the the off-diagonal entry of the stop mixing matrix. Here, this matrix is set with (off-) diagonal entries of approximately (±0.83) 0.55. 7All possible decays of the (possibly virtual) Wboson are considered. – 8 – JHEP11(2014)118 Figure 3. Illustration of the construction of the amT2 (left) and mτ T2 (right) variables, which are used to discriminate against dileptonic t¯ tbackground with one lost lepton (left) or with a hadronically decaying τ(right). The dashed lines indicate the objects that are assumed to be undetected (‘lost’) for the purpose of defining the two variables. decay mode. The mhad−top variable is a three-jet invariant mass, where the jets are selected by minimising a χ2-distribution taking into account the jet momenta and energy resolutions [105,129]. - Dedicated τ-identification criteria are used to reject t¯ tevents which contain a hadronic τdecay. For the construction of the τ-veto, the reconstructed τhad candidates, as previously defined, are subject to further selection requirements (described in appendix A). Three τ-veto working points are defined: loose, tight, and extra-tight. - A track-veto is designed to reject events which contain an isolated track not associated with a baseline lepton. This complements the second-lepton veto, and helps to reject t¯ t events with a one-prong τhad. The selection criteria are detailed in appendix A. Multijet events can pass the event selection if a jet is mis-identified as a lepton or when a real lepton from a heavy-flavour decay satisfies the isolation criteria, and if large Emiss T occurs due to mis-measured jets. The former is suppressed by the lepton isolation criteria, while the following variables are used to reduce the latter effect. - ∆φ(jeti, ~pmiss T), the azimuthal opening angle between jet iand ~pmiss T, is used to suppress multijet events where ~pmiss Tis aligned with a jet. -Emiss T/√HT, where HTis defined as the scalar pTsum of the four leading jets, is an approximation of the Emiss Tsignificance. -Emiss T/meff, where meff =HT+p` T+Emiss T. -Hmiss T,sig is an object-based missing transverse momentum, divided by the per-event resolution of the jets. The object-based missing transverse momentum is the negative sum of the jets and lepton vectors. A detailed description is given in appendix A. – 15 – JHEP11(2014)118 ˜χ± 1 ˜ t1 ˜χ0 1 ( A ( B ( C ∆M ∆M ( D bCa_low, bCa_med bCb_med1, bCb_med2, bCb_high bCc_diag bCd_bulk, bCd_high1, bCd_high2 Signal Regions Figure 4. Schematic diagram of the mass hierarchies for the ˜ t1→b˜χ± 1decay mode, with a subsequent ˜χ± 1→W(∗)˜χ0 1decay. A list of the corresponding signal regions is given above the diagram. 7 Signal selections Signal selections are optimised using simulated samples only. The metric of the optimisation is to maximise the exclusion sensitivity for the various decay modes, and for different regions of SUSY simplified model parameter space. A set of signal benchmark models, selected to cover the various stop scenarios, was used for the optimisation considering all studied discriminating variables and including statistical and systematic uncertainties. The shape-fits employ multiple bins in one or two discriminating variables, which were selected considering the signal and background separation potential, inter-variable correlations, systematic uncertainties, and modelling of the data. Table 2summarises all 15 SRs with a brief description of the targeted signal scenarios, the exclusion analysis techniques, and forward references to the tables which list the event selection details. Four SRs target the ˜ t1→t˜χ0 1decay. The corresponding SR labels begin with tN, which is an acronym for ‘top neutralino’; additional text in the label describes the stop mass region, for example tN diag targets the ‘diagonal’ of the ˜ t1–˜χ0 1mass plane. Nine SRs target the ˜ t1→b˜χ± 1decay, where the SR labels follow the same logic: the first two characters bC stand for ‘bottom chargino’, a third letter (‘a’ to ‘d’) denotes the four different mass hierarchies illustrated in figure 4, and the last piece of text describes the stop mass region. Furthermore, two SRs labelled 3body and tNbC mix are dedicated to the three-body decay mode (˜ t1→bW ˜χ0 1), and the mixed scenario where ˜ t1→t˜χ0 1and ˜ t1→b˜χ± 1decays both occur, respectively. The SRs are not mutually exclusive. All SRs employ selection requirements to suppress the multijet background, and most SRs use the tools described in section 6.2 to reduce the dileptonic t¯ tbackground. Shape-fit techniques are employed to derive model-dependent exclusion limits where useful, while for all model-independent results a simple cut-and-count approach is used. This procedure implies that for SRs using shape-fits one bin is probed at a time to extract the modelindependent results. Only a single bin, or the four bins with highest signal-to-background ratio are included; these are referred to as signal-sensitive bins. The model-dependent – 16 – JHEP11(2014)118 SR Signal scenario Exclusion technique Table tN diag ˜ t1→t˜χ0 1,m˜ t1&mt+m˜χ0 1shape-fit (Emiss Tand mT)4 tN med ˜ t1→t˜χ0 1,m˜ t1∼550 GeV, m˜χ0 1.225 GeV cut-and-count 4 tN high ˜ t1→t˜χ0 1,m˜ t1&600 GeV cut-and-count 4 tN boost ˜ t1→t˜χ0 1,m˜ t1&600 GeV, with a large-Rjet cut-and-count 4 bCa low ˜ t1→b˜χ± 1, ∆M.50 GeV shape-fit (lepton pT)5 ˜ t1→bff0˜χ0 1 bCa med ˜ t1→b˜χ± 1, 50 GeV.∆M.80 GeV shape-fit (lepton pT)5 ˜ t1→bff0˜χ0 1 bCb med1 ˜ t1→b˜χ± 1, ∆m.25 GeV, m˜ t1.500 GeV shape-fit (amT2)5 bCb high ˜ t1→b˜χ± 1, ∆m.25 GeV, m˜ t1&500 GeV shape-fit (amT2)5 bCb med2 ˜ t1→b˜χ± 1, ∆m.80 GeV, m˜ t1.500 GeV shape-fit (amT2 and mT)6 bCc diag ˜ t1→b˜χ± 1,m˜ t1&m˜χ± 1cut-and-count 6 bCd bulk ˜ t1→b˜χ± 1, (∆M, ∆m)&100 GeV, m˜ t1.500 GeV shape-fit (amT2 and mT)6 bCd high1 ˜ t1→b˜χ± 1, (∆M, ∆m)&100 GeV, m˜ t1&500 GeV cut-and-count 6 bCd high2 ˜ t1→b˜χ± 1, ∆M&250 GeV, m˜ t1&500 GeV cut-and-count 6 3body ˜ t1→bW ˜χ0 1,m˜ t1.300 GeV shape-fit (amT2 and mT)7 tNbC mix non-symmetric (˜ t1→t˜χ0 1,˜ t1→b˜χ± 1) cut-and-count 7 Table 2. Overview of all signal regions (SRs) together with the targeted signal scenario, the analysis technique used for model-dependent exclusions, and a reference to the table with the event selection details. For the ˜ t1→b˜χ± 1decay mode, the mass splittings ∆M=m(˜ t1)−m(˜χ0 1) and ∆m=m(˜χ± 1)−m(˜χ0 1) are used to characterise the mass hierarchies, as illustrated in figure 4. The SRs bCa low,bCa med,bCb med1 and bCb high employ selections based on a soft lepton. and model-independent selections are defined in this section, and the corresponding fit configurations are described in section 10. 7.1 Event preselection Common preselection criteria are employed as follows. Events are required to contain a reconstructed primary vertex. Furthermore, a set of quality requirements to avoid badly reconstructed jets, mismeasured-Emiss Tand detector-related problems is imposed on all events. Events with a bad quality muon or with a cosmic-ray muon candidate9are rejected. Exactly one isolated lepton is required with pT>25 GeV except for the soft-lepton selections which employ a pT>6(7) GeV requirement for muons (electrons). The com9Defined as a muon candidate with a transverse or longitudinal impact parameter of |d0|>0.2 mm or |z0|>1 mm. – 17 – JHEP11(2014)118 Preselection Description Trigger logical-OR combination of single-lepton and Emiss Ttriggers; soft lepton: Emiss Ttrigger only. Data quality jet and Emiss Tcleaning, cosmic-ray muon veto, primary vertex. Lepton one isolated electron or muon with pT>25 GeV; soft lepton: the pTthreshold is 6(7) GeV for muons (electrons). 2nd-lepton veto No additional baseline lepton with pT>10 GeV; soft lepton: no further baseline soft muon (electron) with pT>6(7) GeV. Jets The minimum jet multiplicity requirement varies between 2 and 4. Emiss TEmiss T>100 GeV or tighter is required in all selections. Table 3. Preselection criteria common to all signal selections. mon lepton isolation criteria are tightened for the soft-lepton selections while they are relaxed for the analysis exploiting a large-Rjet (cf. section 6). Events containing additional baseline leptons are rejected. A minimum number of jets ranging between 2 and 4, and Emiss T>100 GeV are common requirements amongst all analyses. Table 3summarises the preselection criteria. Figure 5illustrates the separation power for a selection of discriminating variables. For these distributions, events are required to pass the preselection (table 3), to have at least four jets with pT>25 GeV, one of which above 60 GeV, and with at least one of them b-tagged using the 70% working point, and to have Emiss T>100 GeV, mT>60 GeV and Emiss T/√HT>5 GeV1/2. The W+jets background is normalised to match data in a sample selected in the same way, except that a b-veto is imposed. The other processes are normalised to their theoretical cross-sections. Data and background estimation are seen to be in good agreement. 7.2 Selections for the ˜ t1→t˜χ0 1decay Stop pair production with subsequent ˜ t1→t˜χ0 1decays leads to final state objects similar to that of t¯ tproduction augmented by two ˜χ0 1. Four SRs, labelled tN diag,tN med,tN high, and tN boost, target different regions in the ˜ t1−˜χ0 1mass plane and implement different analysis strategies. Table 4details the event selections for these SRs. Criteria based on a subset of the variables outlined in section 6.2, as well as optimised jet thresholds, a more stringent Emiss Trequirement, and a requirement on the angular separation between the highest-pTb-tagged jet and the lepton, ∆R(b-jet, `), are used to suppress t¯ tand W+jets backgrounds as well as to reduce the multijet background to a negligible level. The loosest selection, tN diag, employs a multi-binned shape-fit that targets the challenging parameter space where the stop and its decay products are nearly mass degenerate (m˜ t1&mt+m˜χ0 1), also referred to as the ‘diagonal’. The strategy of exploiting binned – 18 – JHEP11(2014)118 tN diag tN med tN high tN boost Preselection Default preselection criteria, cf. table 3. Lepton = 1 lepton Jets ≥4 with pT>≥4 with pT>≥4 with pT>≥4 with pT> 60,60,40,25 GeV 80,60,40,25 GeV 100,80,40,25 GeV 75,65,40,25 GeV b-tagging ≥1b-tag (70% eff.) amongst four selected jets large-Rjet —≥1, pT>270 GeV and m > 75 GeV ∆φ(jetlarge-R 2, ~pmiss T)—>0.85 Emiss T>100 GeV >200 GeV >320 GeV >315 GeV mT>60 GeV >140 GeV >200 GeV >175 GeV amT2 —>170 GeV >170 GeV >145 GeV mτ T2 — — >120 GeV — topness — — — >7 mhad−top ∈[130, 205] GeV ∈[130, 195] GeV ∈[130, 250] GeV τ-veto tight — — modified, see text. ∆R(b-jet, `)<2.5 — <3<2.6 Emiss T/√HT>5 GeV1/2— Hmiss T,sig —>12.5>10 ∆φ(jeti, ~pmiss T)>0.8(i= 1,2) >0.8(i= 2) —>0.5,0.3(i= 1,2) Model-dependent selection: shape-fit in mTand cut-and-count Emiss T, cf. figure 6. Model-independent selection: test 4 most cut-and-count signal-sensitive bins one-by-one. Table 4. Selection criteria for SRs employed to search for ˜ t1→t˜χ0 1decays. – 19 – JHEP11(2014)118 Events / 20 GeV 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 Data tt W+jets Single top Diboson Z+jets Vtt Total SM ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s 100)×σ)= (550,300,150) GeV ( 0 1 χ, ± 1 χ, 1 t ~ m( 100)×σ)= (500,200) GeV ( 0 1 χ, 1 t ~ m( Preselection [GeV] miss T E 100 150 200 250 300 350 400 450 500 Data / SM 0.5 1 1.5 Events 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 Data tt W+jets Single top Diboson Z+jets Vtt Total SM ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s 100)×σ)= (550,300,150) GeV ( 0 1 χ, ± 1 χ, 1 t ~ m( 100)×σ)= (500,200) GeV ( 0 1 χ, 1 t ~ m( Preselection topness -15 -10 -5 0 5 10 15 Data / SM 0.5 1 1.5 Events / 10 GeV 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 Data tt W+jets Single top Diboson Z+jets Vtt Total SM ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s 100)×σ)= (550,300,150) GeV ( 0 1 χ, ± 1 χ, 1 t ~ m( 100)×σ)= (500,200) GeV ( 0 1 χ, 1 t ~ m( Preselection [GeV] T2 am 100 150 200 250 300 350 400 450 500 Data / SM 0.5 1 1.5 Events / 10 GeV 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 Data tt W+jets Single top Diboson Z+jets Vtt Total SM ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s 100)×σ)= (550,300,150) GeV ( 0 1 χ, ± 1 χ, 1 t ~ m( 100)×σ)= (500,200) GeV ( 0 1 χ, 1 t ~ m( Preselection [GeV] τ T2 m 0 50 100 150 200 250 300 Data / SM 0.5 1 1.5 Figure 5. Comparison of data with estimated backgrounds in the Emiss T(top left), topness (top right), amT2 (bottom left), and mτ T2 (bottom right) distributions for the preselection defined in the text. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflows. Benchmark signal models with cross-sections enhanced by a factor of 100 are overlaid for comparison. shape information significantly improves the sensitivity. The two-dimensional shape-fit in the variables mTand Emiss Tis illustrated in figure 6(left plot). The top 12 bins serve both to probe a signal and to normalise the t¯ tbackground; a subset of the 12 bins has a high purity in t¯ tevents. Three additional bins with a b-tag veto, shown in the bottom part, are used to derive the normalisation of the W+jets background. The bins with Emiss T>150 GeV or mT>140 GeV are defined without upper boundaries. The two SRs tN med and tN high target medium and high stop mass regions, respectively. Both SRs are based on a cut-and-count approach with relatively tight selections. The SR labelled tN boost also targets models with a high stop mass and a nearly massless – 20 – JHEP11(2014)118 100 125 150 60 90 120 140 60 90 Emiss T[GeV] mT[GeV] ≥1b-jetb-jet veto tN diag SM (fit) Observed m(˜ t1,χ 0 1)=(350,150) GeV 1647 1081 1742 3462 2018 2543 1712 768 647 313 117 101 201 163 217 3 2 6 17 11 20 20 15 21 11 11 18 21 22 45 1647 ±41 1081 ±33 1742 ±42 3462 ±59 2018 ±45 2543 ±50 1720 ±161 767 ±80 684 ±79 295 ±50 136 ±22 98 ±13 235 ±34 152 ±20 236 ±29 ATLAS = 8 TeVs -1 L dt = 20 fb ∫ 80 90 100 120 60 90 120 60 90 amT2 [GeV] mT[GeV] ≥1b-jetb-jet veto 3body SM (fit) Observed m(˜ t1,χ 0 1) =(250,100) GeV 812 36 114 314 29 108 12 29 57 160 822 74 281 1 0 0 0 0 1 1 1 2 3 4 10 10 17 22 34 10 ±217 ±335 ±4112 ±10 6±118 ±334 ±493 ±8 17 ±335 ±468 ±9179 ±23 8±229 ±575 ±11 306 ±30 ATLAS = 8 TeVs -1 L dt = 20 fb ∫ Figure 6. Schematic illustration of the tN diag (left) and 3body (right) shape-fit binning. The mTand Emiss T(left) or amT2 (right) variables are used to define a matrix of 4 ×3 bins (left) or 3×4 (right) in the top part, which is sensitive to stop models, while also being enriched with t¯ t background. The bottom bins invert the b-tag requirement into a veto, and serve to normalise the W+jets background. The numbers of observed events together with the estimated background, obtained using the background-only fit described in section 8, are given for each bin. The data and estimated background are in perfect agreement in the six bottom bins for the left plot because the fit is configured to use these six bins together with six free parameters; the fit used for the right plot employs the bottom eight bins and two free parameters. For comparison the expected numbers of events for one signal model are shown. LSP, but takes advantage of the ‘boosted’ topology. The selection assumes that either all decay products of the hadronically decaying top quark, or at least the decay products of the hadronically decaying Wboson, collimate into a jet with a radius of .1.0. Figure 7shows some of the relevant large-Rjet related distributions. The overlaid heavy stop benchmark model illustrates the separation power of the variables. The tN boost selection requires at least one large-Rjet with pT>270 GeV and an invariant mass above 75 GeV. To further discriminate stop decays from the t¯ tbackground, events with a second (ordered by pT) large-Rjet are required to have a minimum azimuthal distance between ~pmiss Tand the second large-Rjet, ∆φ(jetlarge-R 2, ~pmiss T). The extra-tight τ-veto is applied to discard events with τhad candidates well separated from large-Rjets, ∆R(τhad,large-R-jet) >2.6, that satisfy the above pTand mass requirements. 7.3 Selections for the ˜ t1→b˜χ± 1decay Nine SRs target scenarios where both stops decay as ˜ t1→b˜χ± 1followed by subsequent ˜χ± 1→W(∗)˜χ0 1decays. The mass of the lightest chargino m(˜χ± 1) relative to the ˜ t1and ˜χ0 1 masses largely defines the kinematic properties. Figure 4schematically illustrates the four distinct mass hierarchies, whose SRs are described below. – 21 – JHEP11(2014)118 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 Events 500 1000 1500 2000 2500 Data tt W+jets Single top Diboson Z+jets Vtt Total SM 100)× σ)=(700,1) GeV ( 0 1 χ ∼ , 1 t ~ m( ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s Preselection Large-R jet multiplicity -0.5 0 0.5 1 1.5 2 2.5 3 3.5 Data / SM 0.5 1 1.5 50 100 150 200 250 300 Jets / 12 GeV 50 100 150 200 250 Data tt W+jets Single top Diboson Z+jets Vtt Total SM 100)× σ)=(700,1) GeV ( 0 1 χ ∼ , 1 t ~ m( ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s Preselection Large-R jet mass [GeV] 50 100 150 200 250 300 Data / SM 0.5 1 1.5 100 200 300 400 500 600 700 800 900 1000 Jets / 30 GeV -1 10 1 10 2 10 3 10 4 10 5 10 6 10 Data tt W+jets Single top Diboson Z+jets Vtt Total SM 100)× σ)=(700,1) GeV ( 0 1 χ ∼ , 1 t ~ m( ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s Preselection [GeV] T Large-R jet p 100 200 300 400 500 600 700 800 900 1000 Data / SM 0.5 1 1.5 0 0.5 1 1.5 2 2.5 3 Jets / 0.14 20 40 60 80 100 120 140 160 180 200 Data tt W+jets Single top Diboson Z+jets Vtt Total SM 100)× σ)=(700,1) GeV ( 0 1 χ ∼ , 1 t ~ m( ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s Preselection ) miss T p (subleading large-R jet, φ∆ 0 0.5 1 1.5 2 2.5 3 Data / SM 0.5 1 1.5 Figure 7. Comparison of data with background expectations of large-Rjet distributions: multiplicity (top left), invariant mass (top right), transverse momentum (bottom left), and distance in φ space between the second-highest-pTlarge-Rjet and ~pmiss T. Events are required to pass the preselection defined in section 7.1. In addition, the jet thresholds are tightened (pT>75,65,40,25 GeV) and a requirement of mT>120 GeV is imposed. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflows. A benchmark signal model, with cross-section enhanced by a factor of 100, is overlaid for comparison. Selections for mass hierarchy (a): the selection of signal events with a small overall mass splitting, ∆M=m(˜ t1)−m(˜χ0 1), relies on the presence of an initial-state radiation (ISR) jet, against which the stop decay products recoil. Consequently, events with a hard leading jet are selected together with a soft lepton and relatively soft sub-leading jets. The leading jet must not satisfy the b-tagging criteria, while at least one b-tagged jet amongst the sub-leading jets is required. – 22 – JHEP11(2014)118 bCa low bCa med bCb med1 bCb high Preselection soft-lepton preselection, cf. table 3. Lepton = 1 soft lepton = 1 soft lepton with pT<25 GeV Jets ≥2 with ≥3 with ≥2 with pT>180,25 GeV pT>180,25,25 GeV pT>60,60 GeV ∆φ(jeti, ~pmiss T) — >0.4 (i=1,2) Jet veto —HT,2<50 GeV — b-tagging ≥1 sub-leading jet b-tagged (70% eff.) Leading two jets b-tagged (60% eff.) b-veto 1st jet not b-tagged (70% eff.) — mbb —>150 GeV Emiss T>370 GeV >300 GeV >150 GeV >250 GeV Emiss T/meff >0.35 >0.3 — mT>90 GeV >100 GeV — Model-dependent selection: shape-fit 4 bins in lepton pTrange [6(7), 50] GeV 6 bins in amT2 range [0, 500]GeV Model-independent selection: 1 bin with lepton pT<25 GeV amT2 >170 GeV amT2 >200 GeV Table 5. Selection criteria for soft-lepton SRs, employed to search for ˜ t1→b˜χ± 1decays. The two leftmost/rightmost SRs target mass hierarchies (a)/(b), illustrated in figure 4. Two SRs, labelled bCa low and bCa med, are defined to probe scenarios with a mass splitting ∆M.50 GeV, and 50 GeV .∆M.80 GeV, respectively. The SR event selections are listed in table 5. The requirement of ≥3 jets suppresses the W+jets background in bCa med. For bCa low, the jet multiplicity requirement is lowered to ≥2 to avoid large signal acceptance losses, but tighter Emiss Tand Emiss T/meff thresholds are applied to keep the W+jets and multijet backgrounds suppressed. Figure 8compares data with estimated backgrounds in the lepton pTand Emiss T/meff distributions. The overlaid stop benchmark model motivates the selection of low-pTleptons, and the background estimates show the non-negligible contribution from multijet events (with mis-identified leptons). Model-dependent exclusion results are obtained using a shape-fit in the lepton pT variable with four bins of approximately uniform widths in the range [6(7), 50] GeV for muons (electrons). For model-independent results, the cut-and-count approach is used with an additional lepton pT<25 GeV requirement. Selections for mass hierarchy (b): signal scenarios with a moderately large ∆Mbut a small ∆m=m(˜χ± 1)−m(˜χ0 1) feature two high-pTb-jets and low-momentum decay products from the two off-shell Wbosons. – 23 – JHEP11(2014)118 Events / 10 GeV -1 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 ATLAS -1 L dt=20 fb 0 = 8 TeV, s Preselection 100)× m)=(175,165,145) GeV ( 0 1 r ¾ , ± 1 r ¾ , 1 t ~ m( Data tt W+jets Single top Diboson Z+jets Vtt Multijet Total SM [GeV] T Lepton p 20 40 60 80 100 120 140 160 180 200 Data / SM 0.5 1 1.5 Events / 0.05 -1 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 9 10 ATLAS -1 L dt=20 fb 0 = 8 TeV, s Preselection 100)× m)=(175,165,145) GeV ( 0 1 r ¾ , ± 1 r ¾ , 1 t ~ m( Data tt W+jets Single top Diboson Z+jets Vtt Multijet Total SM eff /m miss T E 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Data / SM 0.5 1 1.5 Figure 8. Comparison of data with estimated backgrounds in the lepton pT(left) and Emiss T/meff (right) distributions. Events are required to satisfy the soft-lepton preselection criteria (cf. table 3), have mT>40 GeV, and contain two or more jets (pT>130,25 GeV) of which the leading one must not be b-tagged while the sub-leading one is required to be b-tagged. The t¯ tand W+jets backgrounds are normalised using control regions, the multijet background is estimated directly from data, and all other backgrounds are normalised to their theoretical predictions (as described in section 8). The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflows. A benchmark signal model, with cross-section enhanced by a factor of 100, is overlaid for comparison. Two SRs, labelled bCb med1 and bCb high, employ event selections based on the presence of one soft lepton and two b-tagged jets. They target medium and high stop mass regions, respectively. The complete event selections are listed in table 5. The bCb med1 SR employs an Emiss T>150 GeV requirement, the lowest possible to retain full Emiss Ttrigger efficiency. For models with a heavier ˜ t1, a higher Emiss Tthreshold improves the sensitivity. The dominant background stems from t¯ tproduction and is suppressed by vetoing additional hard jet activity. The variable HT,2is defined like HTbut without including the two leading jets. The bCb high SR omits the jet activity veto to compensate for the loss in signal acceptance associated with the more stringent Emiss Trequirement. Beyond the kinematic amT2 bound, the dominant source of background arises from mis-tagged c-jets in semileptonic t¯ tevents, and the production of a Wboson in association with heavy-flavour jets. To minimise the mis-tagged background, the b-tagging algorithm is operated at the 60% efficiency working point. A minimum requirement on the invariant mass of the two b-tagged jets, mbb, is imposed to reduce the contribution from W+b¯ bevents. Exclusion results are obtained using a shape-fit in the amT2 variable with six bins in the range [0, 500] GeV with a uniform bin width. For all model-independent results, the cut-and-count approach is used but applying an amT2 >170(200) GeV requirement in bCb med1 (bCb high). – 24 – JHEP11(2014)118 Analysis Variable Control regions Validation regions Signal reg. TCR WCR TVR WVR SR All tN * mT[60,90] [60,90] [90,120] [90,120] >[140,200] Nb≥1 = 0 ≥1 = 0 ≥1 tN med amT2 >120 >120 >120 >120 >170 tN high amT2 - - - - >170 mτ T2 - - - - >120 Emiss T>225 >225 >225 >225 >320 Hsig T>8.8>8.8>8.8>8.8>12.5 tN boost amT2 >130 >130 >130 >130 >145 Emiss T>260 >260 >260 >260 >315 topness - - - - >7 All bCa * pT(`)>6(7) >6(7) [6(7),25] [6(7),25] [6(7),25/50] Nb≥1 = 0 ≥1≥1≥1 bCa low Emiss T[200,250] [200,250] [250,370] [250,370] >370 mT[90,120] [90,120] >90 >90 >90 Emiss T/meff - - - >0.35 >0.35 bCa med Emiss T[200,250] [200,250] [250,300] [250,300] >300 mT[100,120] [100,120] >100 >100 >100 Emiss T/meff - - - >0.3>0.3 All bCb * pT(`)>25 >25 [6(7),25] [6(7),25] Nb= 2 = 0 = 2 = 2 mT- [40, 80] - - mbb - - <150 >150 bCb med1 amT2 - - - >170 bCb high amT2 - - - >200 Emiss T>250 >150 >250 >250 All bCc *,bCd * mT[60,90] [60,90] [90,120] [90,120] >120 bCc diag Nb≥1 = 0 ≥1 = 0 = 0 All bCd * Nb≥2 = 0 ≥2 = 0 ≥2 bCd high1 amT2 >120 >200 >120 >200 >200 bCd high2 amT2 >120 >250 >120 >250 >250 tNbC mix mT[60,90] [60,90] [90,120] [90,120] >130 Nb≥1 = 0 ≥1 = 0 ≥1 amT2 >120 >120 >120 >120 >190 Emiss T>170 >170 >170 >170 >270 Emiss T/√HT>5>5>5>5>9 Table 8. Event selections for control regions, validation regions, and the signal regions of the model-independent selection (defined in tables 4–6) associated with cut-and-count or onedimensional shape-fit analyses. The asterisk symbol ‘*’ is used as a wildcard to describe variable requirements common to several regions. Only one validation region is defined for the bCb med1 and bCb high selections. Variables for which the requirements are the same between the regions are not listed. Requirements related to the presence of a b-tagged jet are removed in all selections with a b-tag veto (WCRs and WVRs). All units are in GeV except for unitless quantities and Emiss T/√HTwhich is quoted in GeV1/2. – 31 – JHEP11(2014)118 3body and bCb med2, one t¯ tand one W+jets normalisation parameter is applied across all bins. All shape-fit bins are used to extract model-dependent exclusion limits, while a subset is used for the model-independent results and for the background only fits. This subset includes all bins with mT<90 GeV, acting as CR bins, and in addition for the model-independent results one signal-sensitive bin is included. Top pair and W+jets production accounts for 70–80% of events in the TCRs and WCRs. The signal contamination, for all signal models studied and all CRs, is typically at the percent level and never exceeds 10%. It is explicitly taken into account when setting model-dependent exclusion limits. Table 9shows the background predictions in each SR. The number of t¯ tand W+jets events are estimated using the background-only fit configuration. For the four twodimensional shape-fits, the background predictions are given for the four bins with the highest signal-sensitivity. The quoted uncertainties include all statistical and systematic effects, described in section 9. The numbers of t¯ tevents normalised in the various TCRs are compatible with the predictions entirely based on simulation and the theoretical crosssection, while the W+jets estimates are about 30% lower than, but nonetheless compatible with the predictions from simulation normalised to the theoretical cross-section. Tables showing the estimated and fitted number of background events in the CRs, validation regions, and SRs of all analyses are shown in appendix B. 8.2 Validation The background fit predictions are validated using dedicated event samples. For each cut-and-count and one-dimensional shape-fit analysis one or more dedicated validation regions (VRs) are defined for the t¯ tand W+jets backgrounds. The VRs are designed to be kinematically close to the associated SRs to test the background estimates in regions of phase space as similar as possible to the SRs. For most analyses the associated VRs are defined following a similar strategy as used for the CRs but with a 90 GeV < mT<120 GeV requirement, which leads to a set of events orthogonal to both the associated CRs and the SR. The event selections for the t¯ tand W+jets VRs, TVR and WVR respectively, are given in table 8. Another set of t¯ tvalidation regions, referred to as TVR2 but not shown in the table, is defined where applicable by inverting the SR requirement on amT2 while keeping all other requirements the same as in the SR. For the four two-dimensional shapefits, a subset of the shape-fit bins in the region falling in between that dominated by t¯ tand the region enhanced by a potential signal is used for the t¯ tbackground validation. These signal-depleted shape-fit bins are referred to as validation bins. For the cut-and-count and one-dimensional shape-fit analyses, the VRs are not used in any fit configuration to constrain the fit parameters. The validation bins of the twodimensional shape-fits, on the other hand, are not used in the background-only fit configuration but are included in the model-dependent fit configuration. The number of background events in each VR or validation bin is predicted by the background-only fit (using simulation for the extrapolation) and compared to the data, as shown in the upper panel of figure 11. The lower panel shows the pull for each bin, where the pull is defined as the difference between the predicted background and the observed number of events divided – 32 – JHEP11(2014)118 Signal region Total bkg. t¯ t W+jets Single top Diboson Z+jets t¯ tV Multijet tN med 13.0±2.2 6.5±1.7 2.1±0.5 1.1±0.5 1.4±0.6 0.009 ±0.005 2.0±0.6 – tN high 5.0±1.0 2.0±0.6 0.87 ±0.26 0.54 ±0.19 0.86 ±0.31 0.0030 ±0.0020 0.75 ±0.25 – tN boost 3.3±0.7 1.1±0.4 0.28 ±0.14 0.39 ±0.15 0.72 ±0.25 0.0040 ±0.0020 0.85 ±0.28 – bCa low 6.5±1.4 2.8±0.9 1.1±0.5 0.9±0.4 1.0±0.6 0.018 ±0.017 0.23 ±0.09 0.6+0.8 −0.6 bCa med 17 ±4 10.5±3.1 1.2±0.5 1.7±0.9 1.1±0.6 0.022+0.134 −0.022 0.47 ±0.17 2.5±2.0 bCb med1 32 ±5 15 ±4 9.5±2.8 6.2±1.2 0.060 ±0.018 0.23 ±0.13 0.13 ±0.04 0.7+2.1 −0.7 bCb high 9.8±1.6 3.8±0.8 2.5±0.9 2.9±0.8 0.075 ±0.024 0.10 ±0.06 0.27 ±0.10 0.18+0.52 −0.18 bCc diag 470 ±50 140 ±40 248 ±27 12.1±3.0 59 ±17 5.2±2.6 3.3±1.1 – bCd high1 11.0±1.5 5.2±1.0 1.9±0.5 1.9±0.8 0.38 ±0.18 0.047 ±0.024 1.5±0.5 – bCd high2 4.4±0.8 1.8±0.4 0.71 ±0.33 1.1±0.5 0.25 ±0.19 0.047 ±0.024 0.48 ±0.16 – tNbC mix 7.2±1.0 2.9±0.6 1.30 ±0.30 0.70 ±0.30 0.8±0.4<0.001 1.4±0.4 – tN diag 125 < Emiss T<150 GeV, 120 < mT<140 GeV 136 ±22 123 ±22 6.5±2.5 5.3±2.2 0.29 ±0.18 0.17 ±0.08 1.5±0.5 – 125 < Emiss T<150 GeV, mT>140 GeV 152 ±20 137 ±20 5.8±2.6 5.8±2.1 0.8±0.5 0.24 ±0.12 2.9±0.9 – Emiss T>150 GeV, 120 < mT<140 GeV 98 ±13 85 ±12 4.6±1.5 5.8±2.1 0.34 ±0.32 0.30 ±0.15 2.5±0.8 – Emiss T>150 GeV, mT>140 GeV 236 ±29 202 ±27 10 ±4 9.0±3.5 4.0±1.8 0.53 ±0.26 10.8±3.3 – bCb med2 175 < amT2 <250 GeV, 90 < mT<120 GeV 12.1±2.0 8.5±1.8 1.8±0.8 1.6±0.7 0.018 ±0.007 <0.001 0.26 ±0.10 – 175 < amT2 <250 GeV, mT>120 GeV 7.4±1.4 4.8±1.2 0.47 ±0.19 1.2±0.6 0.01 ±0.10 <0.001 0.85 ±0.27 – amT2 >250 GeV, 90 < mT<120 GeV 21 ±4 12.1±3.1 3.9±1.4 4.5±2.0 0.32 ±0.12 <0.001 0.25 ±0.08 – amT2 >250 GeV, mT>120 GeV 9.1±1.6 4.0±1.1 2.2±0.9 1.8±0.8 0.29 ±0.13 0.047 ±0.024 0.74 ±0.27 – bCd bulk 175 < amT2 <250 GeV, 90 < mT<120 GeV 133 ±22 87 ±16 29 ±7 13 ±5 2.2±1.0 0.019 ±0.010 1.5±0.5 – 175 < amT2 <250 GeV, mT>120 GeV 73 ±8 46 ±7 12.2±3.5 6.9±2.5 3.0±1.4 0.29 ±0.15 4.8±1.4 – amT2 >250 GeV, 90 < mT<120 GeV 66 ±6 33 ±7 20 ±4 11 ±4 1.8±1.0 0.11 ±0.06 0.56 ±0.18 – amT2 >250 GeV, mT>120 GeV 26.5±2.6 10.8±2.5 6.9±1.6 4.7±1.6 1.9±0.9 0.22 ±0.11 2.0±0.6 – 3body 100 < amT2 <120 GeV, 90 < mT<120 GeV 68 ±9 60 ±9 3.9±2.1 3.0±2.8 0.38 ±0.20 0.08 ±0.04 0.28 ±0.12 – 100 < amT2 <120 GeV, mT>120 GeV 75 ±11 67 ±12 4.1±1.8 2.3±1.4 0.55 ±0.18 0.16 ±0.08 0.73 ±0.23 – amT2 >120 GeV, 90 < mT<120 GeV 179 ±23 145 ±21 22 ±6 9 ±5 1.4±0.7 0.20 ±0.10 1.1±0.4 – amT2 >120 GeV, mT>120 GeV 306 ±30 239 ±32 35 ±10 18 ±7 5.8±2.2 1.6±0.8 6.5±2.1 – Table 9. Background estimates in the SRs (model-independent selection) of the 15 analyses obtained from CRs for t¯ tand W+jets, from data for multijet events, and from simulation normalised to theoretical cross-sections for all other backgrounds. The quoted uncertainties include all statistical and systematic effects. The sum in quadrature of the uncertainties of all backgrounds may not add up to the total uncertainty due to correlations. – 33 – JHEP11(2014)118 by the total uncertainty. The latter is given by the full uncertainty of the prediction (described in section 9) added in quadrature with the statistical uncertainty of the observed number of events. No indication of background mis-modelling is found. VRs or validation bins belonging to different analyses can share events, and the systematic uncertainties are correlated across different regions and bins. Several other cross checks are performed to further validate the background estimations. For the SRs requiring more than two jets, dileptonic t¯ tevents can pass the event selection only if they contain additional jets beyond the two b-jets from the leading-order description of the decay. The modelling of these additional jets, which in the simulation arise from radiation or higher-order-corrections, and which is relevant for the background estimation, is validated using a dedicated sample. The event selection is based on requiring one isolated electron and one oppositely-charged, isolated muon, as well as two or more jets of which at least one is b-tagged using the 70% working point. This selects a clean sample of t¯ tevents, which is used in figure 12 (left) to compare the jet multiplicity distributions between data and simulation. Data is modelled sufficiently well within the systematic uncertainties. Further dedicated validation samples are used to test the modelling of the t¯ t background with a τhad or isolated track. These samples are based on the common event preselection and inverting either the trackor τ-veto. The simulation is found to model data well within uncertainties. The W+jets lightvs heavy-flavour composition in the WCR can be different from that in the SR. A dedicated validation is performed by selecting a sample enriched with W+heavy-flavour jets events. The event selection is based on exactly one isolated lepton, and exactly three jets (the fourth jet veto reduces t¯ tevents), of which at least one is b-tagged. Furthermore, events are required to have 60 GeV< mT<90 GeV, Emiss T> 150 GeV, and the two jets with the highest b-tagging weights are required to yield an invariant mass below 80 GeV and to have a limited separation in η–φspace to increase the sensitivity to pair-produced heavy-flavour jets in association with a Wboson. The selected sample of 166 events has a predicted W+heavy-flavour jets component of about 40%; data are found to be in good agreement with simulation, predicting 171 events, when the overall W+jets background is normalised to match data in a b-veto control region.11 Another dedicated validation sample is constructed to test the background prediction for t¯ tproduced with a Zboson that decays to two neutrinos, t¯ tZ(→ν¯ν). This process represents an irreducible background that becomes important for SRs with stringent requirements on kinematic variables, such as tN high or tN boost. The validation strategy is to select t¯ tevents produced in association with a photon, t¯ tγ. This process closely resembles t¯ tZ(→ν¯ν) in terms of Feynman diagrams and kinematic properties when the vector boson pTis well above mZ. The event selection is based on one isolated lepton, four or more jets with at least one b-tag, one high-pTphoton, as well as requirements on modified versions of mTand Emiss Twhere photons are treated as invisible particles. Figure 12 (right) compares data and background predictions, illustrating the accuracy of data modelling. 11The W+jets background is normalised using the WCR associated with bCc diag, which requires three or more jets with a jet pTselection similar to that used in the validation sample. – 34 – JHEP11(2014)118 [90,120] [90,120] [90,120] WVR WVR WVR TVR2 WVR WVR [90,120] [90,120] [90,120] WVR [90,120] [90,120] [90,120] WVR TVR2 WVR TVR2 WVR [60,90] [60,90] [90,120] [90,120] Events 1 10 2 10 3 10 4 10 5 10 ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s Data tt W+jets Other Total SM [90,120] T [100,125], m miss T E [90,120] T [125,150], m miss T E [90,120] T > 150, m miss T E WVR TVR WVR TVR WVR TVR TVR2 TVR WVR TVR WVR VR [90,120] T [80,175], m T,2 am [90,120] T [175,250], m T,2 am [90,120] T > 250, m T,2 am VR WVR TVR [90,120] T [80,175], m T,2 am [90,120] T [175,250], m T,2 am [90,120] T > 250, m T,2 am WVR TVR TVR2 WVR TVR TVR2 WVR TVR [60,90] T [80,90], m T,2 am [60,90] T [90,100], m T,2 am [90,120] T [100,120], m T,2 am [90,120] T > 120, m T,2 am tot σ)/ exp -N obs (N -2 -1 0 1 2 tN_diag tN_med tN_high tN_boost bCa_low bCa_med bCb_med1 bCb_med2 bCb_high bCc_diag bCd_bulk bCd_high1 bCd_high2 tNbC_mix 3body Figure 11. The upper panel compares data with background predictions in the VRs of the cut-and-count and one-dimensional shape-fit analyses as well as the validation bins of the twodimensional shape-fit analyses. The lower panel shows the pull of the same bins. The t¯ tand W+jets background estimates are obtained using the background-only fit to the CRs (described in the text). All statistical and systematic uncertainties are included. The sample of 104 events has a purity in t¯ tγ of more than 70%. The production of t¯ tγ events is estimated using simulation, based on the same generator (MadGraph) as used for the t¯ tZ process, and normalised to the NLO theoretical cross-section [132]. 9 Systematic uncertainties The systematic uncertainties affecting the results can be divided into two classes: uncertainties due to theoretical predictions and modelling, and uncertainties stemming from experimental effects. The impact of both types of uncertainty is evaluated for all back- – 35 – JHEP11(2014)118 Events 1 10 2 10 3 10 4 10 5 10 6 10 Data tt W+jets Other Total SM ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s Selectionµe Jet Multiplicity 2 3 4 5 6 7 8 9 Data / SM 0.5 1 1.5 Events / 50 GeV 0 10 20 30 40 50 60 70 80 Data γtt W+jets tt Total SM ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s Preselectionγtt with photon added [GeV] miss T = E miss T E ~ 150 200 250 300 350 400 450 500 Data / SM 0 0.5 1 1.5 2 Figure 12. Left: jet multiplicity distribution for events with one opposite charged electron-muon pair and at least two jets of which one or more is b-tagged. Other processes include single top quark production, t¯ tproduction in association with a vector boson, Z+jets, and diboson production. Right: missing transverse momentum where photons are treated as invisible particles ( ˜ Emiss T) for an event selection of t¯ t+ photon (described in the text). Both plots: the uncertainty band includes all statistical and experimental uncertainties, and the last bins include overflows. ground and signal samples. Since the yields for the dominant background sources, t¯ tand W+jets, are obtained in dedicated control regions, the modelling uncertainties for these processes affect only the extrapolation from the CRs into the signal regions (and between TCR and WCR), but not the overall normalisation. The systematic uncertainties are included as nuisance parameters and profiled in the likelihood fits. The nuisance parameters are constrained by Gaussian terms with widths corresponding to the sizes of the systematic uncertainties. The same set of nuisance parameters is used across all bins, with the exception of the two shape-fits that have three t¯ tand three W+jets normalisation parameters and hence also have three sets of nuisance parameters. The effects of the sources of uncertainties discussed in this section are quantified in terms of the corresponding relative uncertainty on the estimated number of background events in the various signal regions, this is referred to as the ‘impact on the background estimate’. The dominant experimental uncertainties arise from imperfect knowledge of the jet energy scale (JES) and jet energy resolution (JER) as well as from the modelling of the b-tagging efficiency. The JES uncertainty is derived from a combination of simulation and data samples [105,106] taking into account the dependence on the pT,ηand flavour of the jet as well as the amount of pileup. The impact of JES on the background estimate varies from 1% to 13%. The JER uncertainties are determined with in-situ measurements of the jet response balance in dijet events [129], and the impact on the background estimate is 1%–21%. The JES, JER, and jet mass scale and resolution uncertainties for large-Rjets are derived from a combination of data and simulation samples [107,133], and their combined impact on the background estimate amounts to 3%. The b-tagging uncertainty is estimated – 36 – JHEP11(2014)118 by varying the b-tagging efficiency and mis-tag rate correction factors, obtained from datadriven measurements of these quantities in t¯ tand dijet events [111,134–136], within their uncertainties. The impact of these uncertainties on the background estimate ranges from 1% to 8%, and is dominated by the uncertainty on the b-tagging efficiency. Other sources of experimental uncertainty are the modelling of the average number of pp interactions per bunch crossing, the modelling of the contribution to the Emiss Tfrom energy deposits not associated with any reconstructed objects and from pileup, the modelling of leptonrelated quantities (trigger and identification efficiency, energy and momentum scale and resolution, isolation and τ-veto) as well as imperfect knowledge of the integrated luminosity. The combined impact of these sources on the background estimate is between 1% and 5%. Uncertainties related to theoretical predictions and MC modelling are evaluated for all signal and background processes obtained entirely or partly from simulated events. The sources of uncertainty considered for both the t¯ tand W+jets background processes are the variations of the renormalisation and factorisation scales by factors of 0.5 and 2.0 as well as PDF variations, which are studied following the PDF4LHC recommendations [137] comparing CT10 NLO, MSTW2008 NNLO and NNPDF21 100 [138] PDF error sets. For the t¯ tbackground, the uncertainty on the hadronisation modelling is derived from a comparison between events generated with POWHEG and interfaced with PYTHIA for the shower model and those generated in the same way, but interfaced with Herwig+Jimmy [139]. Furthermore, the effect of the modelling of ISR and final-state radiation (FSR) is studied using samples of t¯ tevents generated with ACERMC with reduced and increased amounts of additional radiation (constrained by the measurement of ref. [140]). The impact of the t¯ tmodelling on the background estimate is 2%–6%. For the W+jets background, the effect of varying the number of partons used in the hard-scatter process is estimated by comparing samples generated with up to four extra partons to samples generated with up to five extra partons. The impact of merging matrix elements and parton showers is studied by varying the SHERPA scales related to the matching scheme. As the W+jets background is normalised in a region with a b-tag veto, additional uncertainties on the flavour composition of the W+jets events in the signal region, based on the uncertainties on the measurement of ref. [141] extrapolated to higher jet multiplicities, are applied in all regions requiring at least one b-tagged jet. The impact of the W+jets modelling on the background estimate is 1%–7%. Background sources other than t¯ tand W+jets are estimated from simulated events and are normalised to the most accurate cross-section predictions available. The cross-section uncertainty for the single-top process is 7% [82–84], while it is 22% for t¯ tV [85,86]. The ZZ and WZ cross-section uncertainties are 5% and 7%, respectively [89,90]. Other sources of systematic uncertainty considered depend on the physics process, but include the choice of renormalisation and factorisation scales, PDF variations, hadronisation modelling, choice of MC generator, modelling of ISR and FSR, variations of the matrix element to parton shower matching scales, the generation of a finite number of partons, and the interference between single-top and t¯ tproduction at NLO. The uncertainty on the interference treatment is estimated using inclusive WWbb samples at LO generated with ACERMC (which includes both the t¯ tand Wt processes). The total impact of the modelling of the smaller backgrounds on the background estimate ranges from 1% to 11%. – 37 – JHEP11(2014)118 The theoretical uncertainties affecting the signal yields originate from the uncertainty on the production cross-section [96], and from the uncertainty on the acceptance. The latter includes PDF variations assessed using the PDF4LHC prescription [137], as well as modelling uncertainties of ISR and FSR and variations of the renormalisation and factorisation scales, evaluated by varying the relevant parameters in MadGraph. The total uncertainty on the production cross-section varies as a function of the stop mass; it amounts to about 15% for m˜ t1= 200 GeV and increases to 18% for m˜ t1= 700 GeV. The impact of the ISR/FSR modelling uncertainty on the signal acceptance ranges from 10% to 20% for signal regions that select events with ISR activity, such as bCc diag, and for signal models of mass hierarchy (c). It is negligible for the other signal regions. The search sensitivity is directly connected to the fitted uncertainty of the signal strength parameter, where the signal strength is a fit parameter that scales the signal yield predicted by the model in question; a signal strength of one corresponds to the nominal signal yield. The impact of the various sources of uncertainty, including the statistical precision, on the signal strength uncertainty is quantified in table 10 for selected signal regions and signal benchmark models. The breakdown of the size of the systematic uncertainties is evaluated by re-running the fit, fixing the relevant nuisance parameter in question to its value from the nominal fit, and taking the difference in quadrature between the signal strength uncertainty of this fit and the nominal fit. The statistical uncertainty is obtained from re-running the fit without any systematic uncertainties, again fixing the nuisance parameters to their values from the nominal fit. The tightest signal regions, such as tN boost, are statistically limited. Systematic uncertainties dominate the looser signal regions. Overall, the largest contributions to the systematic uncertainty on the signal strength come from JER and t¯ tmodelling. The energy scale and energy resolution of large-Rjets is relevant in the tN boost signal region. 10 Results Figures 13 and 14 show comparisons between the observed data and the SM background prediction from the background-only fit with all selections applied except the requirement on the plotted variable. In all SRs, the plots indicate good compatibility between the data and the SM background. The expected distributions from representative signal benchmark models are overlaid. Table 11 shows the number of observed events together with the predicted number of background events in the SRs using the model-independent selection of the 15 analyses. The predicted numbers of background events are obtained using the background-only fits to the number of observed events in the CRs as described in section 8. These fitted background estimates in the CRs are then used to obtain the fitted numbers of background events in the SRs by extrapolations that use transfer factors obtained with simulated events. The observed numbers of events are found to agree well with the fitted numbers of background events in all SRs. To assess the compatibility of the SM background-only hypothesis with the observations in the SRs, a profile likelihood ratio test is performed implementing the methodology – 38 – JHEP11(2014)118 miss T,sig H 8 10 12 14 16 18 20 22 24 Events / 3 0 5 10 15 20 25 30 Data tt W+jets Other Total SM ATLAS -1 L dt = 20 fb 0 = 8 TeV, s )= (500,200) GeV 0 1 r, 1 t ~ m( )= (700,1) GeV 0 1 r, 1 t ~ m( tN_med Large-R jet mass [GeV] 50 100 150 200 250 Jets / 35 GeV 1 2 3 4 5 6Data tt W+jets Other Total SM )=(700,1) GeV 0 1 χ ∼ , 1 t ~ m( )=(650,1) GeV 0 1 χ ∼ , 1 t ~ m( ATLAS -1 L dt = 20 fb ∫ = 8 TeV, s tN_boost b-jet multiplicity 012345 Events 1 10 2 10 3 10 4 10 5 10 Data tt W+jets Other Total SM ATLAS -1 L dt = 20 fb 0 = 8 TeV, s )= (180,174,87) GeV 0 1 r, ± 1 r, 1 t ~ m( bCc_diag [GeV] T2 am 100 150 200 250 300 350 400 Events / 40 GeV 0 5 10 15 20 25 30 35 Data tt W+jets Other Total SM ATLAS -1 L dt = 20 fb 0 = 8 TeV, s )= (550,300,150) GeV 0 1 r, ± 1 r, 1 t ~ m( )= (600,200,100) GeV 0 1 r, ± 1 r, 1 t ~ m( bCd_high1 [GeV] T Leading b-jet p 100 150 200 250 300 350 400 Events / 50 GeV 0 2 4 6 8 10 12 14 Data tt W+jets Other Total SM ATLAS -1 L dt = 20 fb 0 = 8 TeV, s )= (550,300,150) GeV 0 1 r, ± 1 r, 1 t ~ m( )= (600,200,100) GeV 0 1 r, ± 1 r, 1 t ~ m( bCd_high2 [GeV] miss T E 150 200 250 300 350 400 450 Events / 50 GeV 0 2 4 6 8 10 12 14 16 18 Data tt W+jets Other Total SM ATLAS -1 L dt = 20 fb 0 = 8 TeV, s )= (500,300,150) GeV 0 1 r, ± 1 r, 1 t ~ m( tNbC_mix Figure 13. For each signal region one characteristic distribution is shown, with the full event selection of the signal region applied, except for the requirement (indicated by an arrow) on the shown quantity. The uncertainty band includes statistical and all experimental systematic uncertainties. The last bin includes overflows. Benchmark signal models are overlaid for comparison. – 39 – JHEP11(2014)118 Uncertainty on signal strength tN boost tN diag bCc diag bCd bulk Total 0.37 0.19 0.11 0.16 Statistical 0.36 0.05 0.07 0.09 Systematic 0.09 0.18 0.09 0.13 Contribution of systematic uncertainty components Jet energy scale 0.02 0.03 0.02 0.03 Jet energy resolution 0.06 0.11 0.06 0.07 Large-R-jet related 0.03 - - - Emiss T(non-associated energy and pileup) 0.01 0.06 0.01 0.03 Pileup <0.01 0.03 0.01 0.02 b-tagging 0.03 0.01 0.04 0.01 t¯ tmodelling 0.01 0.15 0.04 0.08 W+jets modelling <0.01 0.01 0.02 0.03 Other backgrounds modelling 0.04 0.03 0.02 0.02 Signal acceptance modelling 0.02 0.01 0.02 0.01 Table 10. Breakdown of the size of uncertainties on the signal strength parameter of the likelihood fit. The central values of the signal strength parameters (not shown) are close to zero because the data are compatible with the predicted backgrounds. The uncertainty components are obtained from the difference in quadrature between the signal strength uncertainty of the nominal fit and a fit where the systematic uncertainty in question is disabled by fixing the corresponding nuisance parameter(s) to the value(s) from the nominal fit. Some systematic uncertainty components, such as the jet energy scale or the modelling of backgrounds, are displayed as single entries while the likelihood fit employs a more detailed description. The sum in quadrature of the systematic uncertainty components may not add up to the total systematic uncertainty due to correlations. The following benchmark signal models are used: m˜ t1= 700 GeV and m˜χ0 1= 1 GeV for tN boost,m˜ t1= 350 GeV and m˜χ0 1= 150 GeV for tN diag m˜ t1= 180 GeV, m˜χ± 1= 174 GeV and m˜χ0 1= 87 GeV for bCc diag m˜ t1= 300 GeV, m˜χ± 1= 200 GeV and m˜χ0 1= 100 GeV for bCd bulk. described in ref. [143]. The model-independent selection is used, and the likelihood for a given test includes one SR and all its associated CRs. Each SR, and each signal-sensitive bin in the two-dimensional shape-fits, is probed separately. Table 11 shows the p0values obtained using these fits, indicating that the data in all SRs are compatible with the background-only hypothesis. Good agreement is found when comparing the results obtained using pseudo-experiments to those calculated from asymptotic formulae [142]; the latter is used as the default for all exclusion results presented below. As no significant excess over the expected background from SM processes is observed, the data are used to derive one-sided limits at 95% CL. The results are obtained from a profile likelihood ratio test following the CLsprescription [143]. Model-independent upper limits on beyond-SM contributions are derived separately for each analysis, and in case of the two-dimensional shape-fits for each signal-sensitive bin. The model-independent selection is used, and the likelihood of the fit is configured to include one SR or shape-fit bin and all its associated CRs. A generic signal model, which contributes only to the SR, – 40 – JHEP11(2014)118 [GeV] 1 t ~ m 100 200 300 400 500 600 700 [GeV] 1 0 r ¾ m 50 100 150 200 250 300 1 0 r ¾ = 2 m ± 1 r ¾ , m 1 0 r ¾ + (*) WA 1 ± r ¾ , 1 ± r ¾ b+A 1 t ~ production, 1 t ~ 1 t ~ ATLAS T miss 1-lepton + jets + E =8 TeVs , -1 L dt = 20 fb 0 ) 1 0 r ¾ = 2 m 1 ± r ¾ ( m 1 ± r ¾ +m b < m 1 t ~ m ) exp m1 ±Expected limit ( ) theory SUSY m1 ±Observed limit ( All limits at 95% CL Figure 16. Expected (black dashed) and observed (red solid) 95% CL excluded region in the plane of m˜χ0 1vs. m˜ t1, assuming BR(˜ t1→b˜χ± 1) = 100%, BR(˜χ± 1→W(?)˜χ0 1) = 100% and m˜χ± 1= 2m˜χ0 1. [GeV] 1 t ~ m 200 300 400 500 600 700 [GeV] 1 0 r ¾ m 0 50 100 150 200 250 1 ± r ¾ > m 1 0 r ¾ m = 150 GeV 1 ± r ¾ , m 1 0 r ¾ (*) WA 1 ± r ¾ , 1 ± r ¾ b A 1 t ~ production, 1 t ~ 1 t ~ =8 TeVs , -1 L dt = 20 fb 0 miss T 1-lepton + jets + E ) theory SUSY m1 ±Observed limit ( ) exp m1 ±Expected limit ( All limits at 95% CL ATLAS Figure 17. Expected (black dashed) and observed (red solid) 95% CL excluded region in the plane of m˜χ0 1vs. m˜ t1, assuming BR(˜ t1→b˜χ± 1) = 100%, BR(˜χ± 1→W(?)˜χ0 1) = 100% and m˜χ± 1= 150 GeV. – 47 – JHEP11(2014)118 [GeV] 1 t ~ m 200 300 400 500 600 700 [GeV] 1 0 r ¾ m 0 20 40 60 80 100 120 140 160 1 ± r ¾ > m 1 0 r ¾ m = 106 GeV 1 ± r ¾ , m 1 0 r ¾ (*) WA 1 ± r ¾ , 1 ± r ¾ b A 1 t ~ production, 1 t ~ 1 t ~ =8 TeVs , -1 L dt = 20 fb 0 miss T 1-lepton + jets + E ) theory SUSY m1 ±Observed limit ( ) exp m1 ±Expected limit ( All limits at 95% CL ATLAS Figure 18. Expected (black dashed) and observed (red solid) 95% CL excluded region in the plane of m˜χ0 1vs. m˜ t1, assuming BR(˜ t1→b˜χ± 1) = 100%, BR(˜χ± 1→W(?)˜χ0 1) = 100% and m˜χ± 1= 106 GeV. [GeV] 1 t ~ m 200 300 400 500 600 700 [GeV] 1 0 r ¾ m 100 120 140 160 180 200 220 240 260 280 300 1 ± r ¾ + m b < m 1 t ~ m + 5 GeV 1 0 r ¾ = m 1 ± r ¾ , m 1 0 r ¾ (*) WA 1 ± r ¾ , 1 ± r ¾ b A 1 t ~ production, 1 t ~ 1 t ~ =8 TeVs , -1 L dt = 20 fb 0 miss T 1-lepton + jets + E ) theory SUSY m1 ±Observed limit ( ) exp m1 ±Expected limit ( All limits at 95% CL ATLAS Figure 19. Expected (black dashed) and observed (red solid) 95% CL excluded region in the plane of m˜χ0 1vs. m˜ t1, assuming BR(˜ t1→b˜χ± 1) = 100%, BR(˜χ± 1→W?˜χ0 1) = 100% and m˜χ± 1= m˜χ0 1+ 5 GeV. – 48 – JHEP11(2014)118 [GeV] 1 t ~ m 200 300 400 500 600 700 [GeV] 1 0 r ¾ m 100 150 200 250 300 350 400 450 500 1 ± r ¾ + m b < m 1 t ~ m + 20 GeV 1 0 r ¾ = m 1 ± r ¾ , m 1 0 r ¾ (*) WA 1 ± r ¾ , 1 ± r ¾ b A 1 t ~ production, 1 t ~ 1 t ~ =8 TeVs , -1 L dt = 20 fb 0 miss T 1-lepton + jets + E ) theory SUSY m1 ±Observed limit ( ) exp m1 ±Expected limit ( All limits at 95% CL ATLAS Figure 20. Expected (black dashed) and observed (red solid) 95% CL excluded region in the plane of m˜χ0 1vs. m˜ t1, assuming BR(˜ t1→b˜χ± 1) = 100%, BR(˜χ± 1→W?˜χ0 1) = 100% and m˜χ± 1= m˜χ0 1+ 20 GeV. [GeV] 1 t ~ m 150 200 250 300 350 400 450 500 [GeV] 1 0 r ¾ m 0 50 100 150 200 250 300 350 = 10 GeV 1 ± r ¾ - m 1 t ~ , m 1 0 r ¾ (*) WA 1 ± r ¾ , 1 ± r ¾ b A 1 t ~ production, 1 t ~ 1 t ~ ) exp m1 ±Expected limit ( ) theory SUSY m1 ±Observed limit ( ATLAS All limits at 95% CL T miss 1-lepton + jets + E =8 TeVs , -1 L dt = 20 fb 0 1 + r ¾ > m 1 0 r ¾ m Figure 21. Expected (black dashed) and observed (red solid) 95% CL excluded region in the plane of m˜χ0 1vs. m˜ t1, assuming BR(˜ t1→b˜χ± 1) = 100%, BR(˜χ± 1→W(?)˜χ0 1) = 100% and m˜χ± 1=m˜ t1−10 GeV. – 49 – JHEP11(2014)118 [GeV] 1 ± r ¾ m 100 120 140 160 180 200 220 240 260 280 [GeV] 1 0 r ¾ m 0 50 100 150 200 250 300 350 400 1 ± r ¾ > m 1 0 r ¾ m = 300 GeV 1 t ~ , m 1 0 r ¾ (*) WA 1 ± r ¾ , 1 ± r ¾ b A 1 t ~ production, 1 t ~ 1 t ~ =8 TeVs , -1 L dt = 20 fb 0 miss T 1-lepton + jets + E ) theory SUSY m1 ±Observed limit ( ) exp m1 ±Expected limit ( All limits at 95% CL ATLAS Figure 22. Expected (black dashed) and observed (red solid) 95% CL excluded region in the plane of m˜χ0 1vs. m˜χ± 1, assuming BR(˜ t1→b˜χ± 1) = 100%, BR(˜χ± 1→W(?)˜χ0 1) = 100% and m˜ t1= 300 GeV. [GeV] 1 t ~ m 300 400 500 600 700 [GeV] 1 0 r ¾ m 50 100 150 200 250 300 350 1 0 r ¾ = 2 m ± 1 r ¾ , m 1 0 r ¾ + (*) WA 1 ± r ¾ , 1 ± r ¾ / b 1 0 r ¾ t A 1 t ~ production, 1 t ~ 1 t ~ ) 1 0 r ¾ t A 1 t ~ x = BR( x = 0% x = 25% x = 50% x = 75% x = 100% ) 1 0 r ¾ = 2 m 1 ± r ¾ ( m 1 ± r ¾ +m b < m 1 t ~ m 1 0 r ¾ +m t < m 1 t ~ m ATLAS Expected limits Observed limits All limits at 95% CL T miss 1-lepton + jets + E =8 TeVs , -1 L dt = 20 fb 0 Figure 23. Expected (dashed) and observed (solid) 95% CL excluded region in the plane of m˜χ0 1 vs. m˜ t1, assuming x=BR(˜ t1→t˜χ0 1)=1−BR(˜ t1→b˜χ± 1), and xvarying from 0% to 100%. – 50 – JHEP11(2014)118 [GeV] 1 t ~ m 250 300 350 400 450 500 550 600 650 700 750 [pb]m -2 10 -1 10 1 10 2 10 = 50 GeV 1 0 r ¾ , m 1 0 r ¾ t A 1 t ~ production, 1 t ~ 1 t ~ ATLAS All limits at 95% CL T miss 1-lepton + jets + E pair prod. cross section 1 t ~ Observed limit (mostly stop-right) Expected limit (mostly stop-right) Observed limit (purely stop-left) Expected limit (purely stop-left) =8 TeVs, -1 L dt = 20 fb 0 [GeV] 1 t ~ m 350 400 450 500 550 600 650 700 [pb]m -2 10 -1 10 1 10 = 150 GeV 1 0 r ¾ , m 1 0 r ¾ t A 1 t ~ production, 1 t ~ 1 t ~ ATLAS All limits at 95% CL T miss 1-lepton + jets + E pair prod. cross section 1 t ~ Observed limit (mostly stop-right) Expected limit (mostly stop-right) Observed limit (purely stop-left) Expected limit (purely stop-left) =8 TeVs, -1 L dt = 20 fb 0 Figure 24. Expected and observed cross-section upper limits at 95% CL in the ˜ t1→t˜χ0 1decay mode with the LSP mass fixed to 50 GeV (top) or 150 GeV (bottom) for models with the ˜ t1being a pure stop-left (˜ tL) or mostly a stop-right (˜ tR). The upper and lower blue lines correspond to the nominal signal cross-section scaled up and down by the theoretical uncertainty. – 51 – JHEP11(2014)118 ) 1 0 χ t→ 1 t ~ )+BR( 1 ± χ b→ 1 t ~ BR( 0 0.2 0.4 0.6 0.8 1 significance s Expected CL 0 1 2 3 4 5 6 7 8 (400,50) GeV≈) 0 1 χ ∼ , 1 t ~ m( (550,50) GeV≈) 0 1 χ ∼ , 1 t ~ m( (550,150) GeV≈) 0 1 χ ∼ , 1 t ~ m( ) = (400,50) GeV 0 1 χ ∼ , 1 t ~ m( ) = (550,50) GeV 0 1 χ ∼ , 1 t ~ m( 1.0≤0.8 < x 0.8≤0.6 < x 0.6≤0.4 < x 0.4≤0.2 < x 0.2≤0.0 < x ATLAS ∫-1 L dt = 20 fb = 8 TeVs miss T 1-lepton + jets + E ) 0 1 χ ∼ t→ 1 t ~ x = BR(pMSSM models: Simplified models: ) 1 0 χ t→ 1 t ~ )+BR( 1 ± χ b→ 1 t ~ BR( 0 0.2 0.4 0.6 0.8 1 significance s Observed CL 0 1 2 3 4 5 6 7 8 (400,50) GeV≈) 0 1 χ ∼ , 1 t ~ m( (550,50) GeV≈) 0 1 χ ∼ , 1 t ~ m( (550,150) GeV≈) 0 1 χ ∼ , 1 t ~ m( ) = (400,50) GeV 0 1 χ ∼ , 1 t ~ m( ) = (550,50) GeV 0 1 χ ∼ , 1 t ~ m( 1.0≤0.8 < x 0.8≤0.6 < x 0.6≤0.4 < x 0.4≤0.2 < x 0.2≤0.0 < x ATLAS ∫-1 L dt = 20 fb = 8 TeVs miss T 1-lepton + jets + E ) 0 1 χ ∼ t→ 1 t ~ x = BR(pMSSM models: Simplified models: Figure 25. The expected (top) and observed (bottom) CLssignificance values for the 27 pMSSM models described in section 5.2 and for two simplified models. Models above the dashed line, indicating the CLssignificance corresponding to 95% CL, are excluded. The results of the pMSSM models are displayed using filled markers where the marker symbol corresponds to the m˜ t1,m˜χ0 1 range and the colour represents the branching ratio for the ˜ t1→t˜χ0 1decay, while the simplified models are shown using open markers. The uncertainty on the expected CLssignificance includes all sources except the theoretical cross-section uncertainty, while the uncertainty on the observed CLssignificance includes only the effect of scaling the nominal signal cross-section up and down by the theoretical cross-section uncertainty. – 52 – JHEP11(2014)118 Acknowledgments We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; EPLANET, ERC and NSRF, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT and NSRF, Greece; ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; BRF and RCN, Norway; MNiSW and NCN, Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZˇ S, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.) and in the Tier-2 facilities worldwide. A Detailed description of the discriminating variables This section provides more detailed descriptions of the discriminating variables that are introduced in section 6. - Stransverse Mass, mT2. This variable targets decay topologies with two branches, referred to here as aand b. In each branch, there are some particles with fully measured momenta and some particles with momenta that are not measured directly. The sum of the four vectors of the measured momenta in branch i∈ {a, b}are denoted pi= (Ei, ~pTi, pzi) and the sum of the four vectors of the unmeasured momenta are denoted qi= (Fi, ~qTi, qzi). With m2 pi= E2 i−~p 2 iand m2 qi=F2 i−~q 2 i, the mTof the particles in branch iis given in general by m2 Ti=qp2 Ti+m2 pi+qq2 Ti+m2 qi2 −(~pTi+~qTi)2 which in the case that mqi=mpi= 0 is the same as the one given in section 6.2. A generalisation of mT,mT2, is defined as a minimisation over the allocation of ~pmiss T – 53 – JHEP11(2014)118 between ~qTaand ~qTbof the maximum of the corresponding mTa or mTb: mT2 ≡min ~qTa+~qTb=~p miss T{max(mTa, mTb)}, where one must make an assumption of mqaand mqbin the computation of mT a and mTb. The result of the above minimisation is the minimum parent mass consistent with the observed kinematic distributions under the inputs mqaand mqb. The variants of mT2 described below only differ in the measured particles, (assumed) unmeasured particles, and choices for the input masses, mqaand mqb. - Asymmetric mT2,amT2 - Measured particles: for branch a, this is one of the b-jets and for branch bthis is the second b-jet and the charged lepton. The b-jets are identified based on the highest b-tagging weights. Since there are two ways of assigning the b-tagged jets to branches aand b, both mT2 values are computed and the minimum kept for the final discriminant. - Unmeasured particles: for branch a, this is a Wboson that decays leptonically, with the charged lepton unidentified as such. The unmeasured particle for branch bis the neutrino associated with the measured charged lepton. - Input masses: mqa=mW= 80 GeV and mqb=mν= 0 GeV. - In cases in which the lost lepton is an electron and the corresponding energy deposit enters the Emiss Tcalculation, for instance as a soft calorimeter cluster, amT2 can exceed the top mass boundary in t¯ tevents, but the variable remains powerful at discriminating signal from background. -τ-based mT2,mτ T2 - Measured particles: for branch a, this is the τ-jet, identified as the highest-pTjet excluding the selected two b-tagged jets. The measured particle for branch bis the charged lepton. - Unmeasured particles: for branch a, this includes the two neutrinos associated with the τproduction and hadronic decay. The unmeasured particle for branch bis the neutrino associated with the charged lepton. - Input masses: mqa= 0 GeV and mqb=mν= 0 GeV. - Topness. The topness event value is defined as ln(min ˆ S), where ˆ Sis the minimum of the χ2-type function S: S(pW,x, pW,y, pW,z, pν,z) = m2 W−(p`+pν)22 a4 W +m2 t−(pb1+p`+pν)22 a4 t + +m2 t−(pb2+pW)22 a4 t +4m2 t−(Σp)22 a4 CM . – 54 – JHEP11(2014)118 The first three arguments of Sare the components of the non-reconstructed Wboson 3-momentum (pW,x,pW,y,pW,z). This Wis assumed to decay leptonically, but the lepton is not reconstructed and is thus only noticeable in the missing transverse momentum. The variable pν,z is the longitudinal momentum of the neutrino from the other Wboson decay, for which the lepton was successfully reconstructed. These four numbers are varied to find the minimum of S. The momenta appearing on the right-hand side of the equation above are either 4momenta of the reconstructed objects (one lepton, p`, and two b-jets, pb1and pb2) or 4-momenta assigned by the minimisation procedure (pWand pν). To find all four components, the neutrinos and the Wboson without reconstructed decay products are assumed to be on-shell. Both combinations for b1and b2are evaluated during the minimisation; if only one b-tagged jet is present, it is used together with the leading or subleading jet (that means, a total of four possible jet assignments is evaluated in this case). The minimisation is constrained such that the observed missing transverse momentum is attributed to the unobserved Wboson (decaying into a not-reconstructed lepton and a neutrino) and a neutrino from the other top decay branch. The constants aW,atand aCM are set to the values suggested by the authors of S: aW= 5 GeV, at= 15 GeV, aCM = 1 TeV. - Hadronic top mass, mhad−top. This reconstructed top mass is constructed as mj1,j2,biby minimising χ2=mj1,j2,bi−mtop2 σ2 mj1,j2,bi +mj1,j2−mW2 σ2 mj1,j2 , where i= 1 or 2; b1and b2are the two jets with the highest b-tagging weights; j1,j2 are the highest pTjets from the selected jets in the event excluding b1and b2and σ2 mj1,j2,bi=m2 j1,j2,bi(r2 j1+r2 j2+r2 bi) σ2 mj1,j2=m2 j1,j2(r2 j1+r2 j2), where riis the fractional jet energy uncertainty of the pTfor jet idetermined by dedicated studies [105,129]. -τ-veto. For the construction of the τ-veto, the reconstructed τhad candidates are subject to further selection requirements. Candidates are required to have either one associated track (classified as one-prong τdecay), or two to three tracks (classified as three-prong τ decay, where one track can be missed). The τhad charge for candidates with one or three tracks is required to be ±1 and to be opposite to the charge of the selected electron or muon in the event. For candidates with two tracks, the sign of the τhad charge is required to be opposite to that of the selected lepton only if the τhad charge is ±2. Finally, – 55 – JHEP11(2014)118 three different BDT requirements are imposed on the candidates to define three τ-veto working points: loose, tight, and extra-tight. In simulated t¯ tevents with one W→`ν decay, signaland background-like events are defined by requiring the other Wboson to either decay into quarks (signal) or into a τhad(background). In these samples, the loose (tight) τ-veto retains 99% (97%) of signal events, while for background events 81% (69%) with a one-prong and 75% (63%) with a three-prong τhad decay survive the veto. - Track-veto. Tracks are required to satisfy the following criteria: pT>10 GeV and |η|<2.5, transverse and longitudinal impact parameters |d0|<1 mm and |z0|<2 mm. The track isolation requires that there are no additional tracks associated with the primary vertex with pT>3 GeV in a cone of ∆R= 0.4 around the track. Events with at least one isolated track of opposite charge compared to that of the selected electron or muon in the event are rejected by the track-veto. -Hmiss T,sig is an object-based missing transverse momentum, divided by the per-event resolution of the jets. It is defined by Hmiss T,sig =|~ Hmiss T|−M σ|~ Hmiss T| , where ~ Hmiss Tis the negative sum of the jets and lepton vectors. The denominator is computed from the per-event jet energy uncertainties, while the lepton is assumed to be well-measured. The parameter Mis chosen to be a characteristic ‘scale’ of the background [145], and is fixed at 100 GeV in this analysis based on optimisation studies. 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Citterio90a, M. Ciubancan26a, A. Clark49, P.J. Clark46, R.N. Clarke15, W. Cleland124, J.C. Clemens84, C. Clement147a,147b, Y. Coadou84, M. Cobal165a,165c, A. Coccaro139, J. Cochran63, L. Coffey23, J.G. Cogan144, J. Coggeshall166, B. Cole35, S. Cole107, A.P. Colijn106, J. Collot55, T. Colombo58c, G. Colon85, G. Compostella100, P. Conde Mui˜no125a,125b, E. Coniavitis48, M.C. Conidi12, S.H. Connell146b, I.A. Connelly76, S.M. Consonni90a,90b, V. Consorti48, S. Constantinescu26a, C. Conta120a,120b, G. Conti57, F. Conventi103a,h, M. Cooke15, B.D. Cooper77, A.M. Cooper-Sarkar119, N.J. Cooper-Smith76, K. Copic15, T. Cornelissen176, M. Corradi20a, – 71 – JHEP11(2014)118 F. Corriveau86,i, A. Corso-Radu164, A. Cortes-Gonzalez12, G. Cortiana100, G. Costa90a, M.J. Costa168, D. Costanzo140, D. Cˆot´e8, G. Cottin28, G. Cowan76, B.E. Cox83, K. Cranmer109, G. Cree29, S. Cr´ep´e-Renaudin55, F. Crescioli79, W.A. Cribbs147a,147b, M. Crispin Ortuzar119, M. Cristinziani21, V. Croft105, G. Crosetti37a,37b, C.-M. Cuciuc26a, T. Cuhadar Donszelmann140, J. Cummings177, M. Curatolo47, C. Cuthbert151, H. Czirr142, P. Czodrowski3, Z. Czyczula177, S. D’Auria53, M. D’Onofrio73, M.J. Da Cunha Sargedas De Sousa125a,125b, C. Da Via83, W. Dabrowski38a, A. Dafinca119, T. Dai88, O. Dale14, F. Dallaire94, C. Dallapiccola85, M. Dam36, A.C. Daniells18, M. Dano Hoffmann137, V. Dao48, G. Darbo50a, S. Darmora8, J.A. Dassoulas42, A. Dattagupta60, W. Davey21, C. David170, T. Davidek128, E. Davies119,c, M. Davies154, O. Davignon79, A.R. Davison77, P. Davison77, Y. Davygora58a, E. Dawe143, I. Dawson140, R.K. Daya-Ishmukhametova85, K. De8, R. de Asmundis103a, S. De Castro20a,20b, S. De Cecco79, N. De Groot105, P. de Jong106, H. De la Torre81, F. De Lorenzi63, L. De Nooij106, D. De Pedis133a, A. De Salvo133a, U. De Sanctis165a,165b, A. De Santo150, J.B. De Vivie De Regie116, W.J. Dearnaley71, R. Debbe25, C. Debenedetti138, B. Dechenaux55, D.V. Dedovich64, I. Deigaard106, J. Del Peso81, T. 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Dolezal128, B.A. Dolgoshein97,∗, M. Donadelli24d, S. Donati123a,123b, P. Dondero120a,120b, J. Donini34, J. Dopke130, A. Doria103a, M.T. Dova70, A.T. Doyle53, M. Dris10, J. Dubbert88, S. Dube15, E. Dubreuil34, E. Duchovni173, G. Duckeck99, O.A. Ducu26a, D. Duda176, A. Dudarev30, F. Dudziak63, L. Duflot116, L. Duguid76, M. D¨uhrssen30, M. Dunford58a, H. Duran Yildiz4a, M. D¨uren52, A. Durglishvili51b, M. Dwuznik38a, M. Dyndal38a, J. Ebke99, W. Edson2, N.C. Edwards46, W. Ehrenfeld21, T. Eifert144, G. Eigen14, K. Einsweiler15, T. Ekelof167, M. El Kacimi136c, M. Ellert167, S. Elles5, F. Ellinghaus82, N. Ellis30, J. Elmsheuser99, M. Elsing30, D. Emeliyanov130, Y. Enari156, O.C. Endner82, M. Endo117, R. Engelmann149, J. Erdmann177, A. Ereditato17, D. Eriksson147a, G. Ernis176, J. Ernst2, M. Ernst25, J. Ernwein137, D. Errede166, S. Errede166, E. Ertel82, M. Escalier116, H. Esch43, C. Escobar124, B. Esposito47, A.I. Etienvre137, E. Etzion154, H. Evans60, A. Ezhilov122, L. Fabbri20a,20b, G. Facini31, R.M. Fakhrutdinov129, S. Falciano133a, R.J. Falla77, J. Faltova128, Y. Fang33a, M. Fanti90a,90b, A. Farbin8, A. Farilla135a, T. Farooque12, S. Farrell15, S.M. Farrington171, P. Farthouat30, F. Fassi136e, P. Fassnacht30, D. Fassouliotis9, A. Favareto50a,50b, L. Fayard116, P. Federic145a, O.L. Fedin122,j, W. Fedorko169, M. Fehling-Kaschek48, S. Feigl30, L. Feligioni84, C. Feng33d, E.J. Feng6, H. Feng88, A.B. Fenyuk129, S. Fernandez Perez30, S. Ferrag53, J. Ferrando53, A. Ferrari167, P. Ferrari106, R. Ferrari120a, D.E. Ferreira de Lima53, A. Ferrer168, D. Ferrere49, C. Ferretti88, A. Ferretto Parodi50a,50b, M. Fiascaris31, F. Fiedler82, A. Filipˇciˇc74, M. Filipuzzi42, F. Filthaut105, M. Fincke-Keeler170, K.D. Finelli151, M.C.N. Fiolhais125a,125c, L. Fiorini168, A. Firan40, A. Fischer2, J. Fischer176, W.C. Fisher89, E.A. Fitzgerald23, M. Flechl48, I. Fleck142, P. Fleischmann88, S. Fleischmann176, G.T. Fletcher140, G. Fletcher75, T. Flick176, A. Floderus80, L.R. Flores Castillo174,k, A.C. Florez Bustos160b, M.J. Flowerdew100, A. Formica137, A. Forti83, – 72 – JHEP11(2014)118 S. Tanaka65, A.J. Tanasijczuk143, B.B. Tannenwald110, N. Tannoury21, S. Tapprogge82, S. Tarem153, F. Tarrade29, G.F. Tartarelli90a, P. Tas128, M. Tasevsky126, T. Tashiro67, E. Tassi37a,37b, A. Tavares Delgado125a,125b, Y. Tayalati136d, F.E. Taylor93, G.N. Taylor87, W. Taylor160b, F.A. Teischinger30, M. Teixeira Dias Castanheira75, P. Teixeira-Dias76, K.K. Temming48, H. Ten Kate30, P.K. Teng152, J.J. Teoh117, S. Terada65, K. Terashi156, J. Terron81, S. Terzo100, M. Testa47, R.J. Teuscher159,i, J. Therhaag21, T. Theveneaux-Pelzer34, J.P. Thomas18, J. Thomas-Wilsker76, E.N. Thompson35, P.D. Thompson18, P.D. Thompson159, R.J. Thompson83, A.S. Thompson53, L.A. Thomsen36, E. Thomson121, M. Thomson28, W.M. Thong87, R.P. Thun88,∗, F. Tian35, M.J. Tibbetts15, V.O. Tikhomirov95,ag, Yu.A. Tikhonov108,t, S. Timoshenko97, E. Tiouchichine84, P. Tipton177, S. Tisserant84, T. 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Zwalinski30. 1Department of Physics, University of Adelaide, Adelaide, Australia 2Physics Department, SUNY Albany, Albany NY, United States of America 3Department of Physics, University of Alberta, Edmonton AB, Canada 4 (a)Department of Physics, Ankara University, Ankara; (b)Department of Physics, Gazi University, Ankara; (c)Division of Physics, TOBB University of Economics and Technology, Ankara; (d) Turkish Atomic Energy Authority, Ankara, Turkey 5LAPP, CNRS/IN2P3 and Universit´e de Savoie, Annecy-le-Vieux, France 6High Energy Physics Division, Argonne National Laboratory, Argonne IL, United States of America 7Department of Physics, University of Arizona, Tucson AZ, United States of America 8Department of Physics, The University of Texas at Arlington, Arlington TX, United States of America 9Physics Department, University of Athens, Athens, Greece 10 Physics Department, National Technical University of Athens, Zografou, Greece 11 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 12 Institut de F´ısica d’Altes Energies and Departament de F´ısica de la Universitat Aut`onoma de Barcelona, Barcelona, Spain 13 (a)Institute of Physics, University of Belgrade, Belgrade; (b)Vinca Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia 14 Department for Physics and Technology, University of Bergen, Bergen, Norway 15 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA, United States of America 16 Department of Physics, Humboldt University, Berlin, Germany 17 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 18 School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom – 80 – JHEP11(2014)118 19 (a)Department of Physics, Bogazici University, Istanbul; (b)Department of Physics, Dogus University, Istanbul; (c)Department of Physics Engineering, Gaziantep University, Gaziantep, Turkey 20 (a)INFN Sezione di Bologna; (b)Dipartimento di Fisica e Astronomia, Universit`a di Bologna, Bologna, Italy 21 Physikalisches Institut, University of Bonn, Bonn, Germany 22 Department of Physics, Boston University, Boston MA, United States of America 23 Department of Physics, Brandeis University, Waltham MA, United States of America 24 (a)Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro; (b)Federal University of Juiz de Fora (UFJF), Juiz de Fora; (c)Federal University of Sao Joao del Rei (UFSJ), Sao Joao del Rei; (d)Instituto de Fisica, Universidade de Sao Paulo, Sao Paulo, Brazil 25 Physics Department, Brookhaven National Laboratory, Upton NY, United States of America 26 (a)National Institute of Physics and Nuclear Engineering, Bucharest; (b)National Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj Napoca; (c)University Politehnica Bucharest, Bucharest; (d)West University in Timisoara, Timisoara, Romania 27 Departamento de F´ısica, Universidad de Buenos Aires, Buenos Aires, Argentina 28 Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom 29 Department of Physics, Carleton University, Ottawa ON, Canada 30 CERN, Geneva, Switzerland 31 Enrico Fermi Institute, University of Chicago, Chicago IL, United States of America 32 (a)Departamento de F´ısica, Pontificia Universidad Cat´olica de Chile, Santiago; (b)Departamento de F´ısica, Universidad T´ecnica Federico Santa Mar´ıa, Valpara´ıso, Chile 33 (a)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing; (b)Department of Modern Physics, University of Science and Technology of China, Anhui; (c)Department of Physics, Nanjing University, Jiangsu; (d)School of Physics, Shandong University, Shandong; (e)Physics Department, Shanghai Jiao Tong University, Shanghai, China 34 Laboratoire de Physique Corpusculaire, Clermont Universit´e and Universit´e Blaise Pascal and CNRS/IN2P3, Clermont-Ferrand, France 35 Nevis Laboratory, Columbia University, Irvington NY, United States of America 36 Niels Bohr Institute, University of Copenhagen, Kobenhavn, Denmark 37 (a)INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati; (b)Dipartimento di Fisica, Universit`a della Calabria, Rende, Italy 38 (a)AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow; (b)Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow, Poland 39 The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Krakow, Poland 40 Physics Department, Southern Methodist University, Dallas TX, United States of America 41 Physics Department, University of Texas at Dallas, Richardson TX, United States of America 42 DESY, Hamburg and Zeuthen, Germany 43 Institut f¨ur Experimentelle Physik IV, Technische Universit¨at Dortmund, Dortmund, Germany 44 Institut f¨ur Kernund Teilchenphysik, Technische Universit¨at Dresden, Dresden, Germany 45 Department of Physics, Duke University, Durham NC, United States of America 46 SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom 47 INFN Laboratori Nazionali di Frascati, Frascati, Italy 48 Fakult¨at f¨ur Mathematik und Physik, Albert-Ludwigs-Universit¨at, Freiburg, Germany 49 Section de Physique, Universit´e de Gen`eve, Geneva, Switzerland 50 (a)INFN Sezione di Genova; (b)Dipartimento di Fisica, Universit`a di Genova, Genova, Italy 51 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi; (b) High Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia 52 II Physikalisches Institut, Justus-Liebig-Universit¨at Giessen, Giessen, Germany 53 SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom – 81 – JHEP11(2014)118 54 II Physikalisches Institut, Georg-August-Universit¨at, G¨ottingen, Germany 55 Laboratoire de Physique Subatomique et de Cosmologie, Universit´e Grenoble-Alpes, CNRS/IN2P3, Grenoble, France 56 Department of Physics, Hampton University, Hampton VA, United States of America 57 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge MA, United States of America 58 (a)Kirchhoff-Institut f¨ur Physik, Ruprecht-Karls-Universit¨at Heidelberg, Heidelberg; (b) Physikalisches Institut, Ruprecht-Karls-Universit¨at Heidelberg, Heidelberg; (c)ZITI Institut f¨ur technische Informatik, Ruprecht-Karls-Universit¨at Heidelberg, Mannheim, Germany 59 Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan 60 Department of Physics, Indiana University, Bloomington IN, United States of America 61 Institut f¨ur Astround Teilchenphysik, Leopold-Franzens-Universit¨at, Innsbruck, Austria 62 University of Iowa, Iowa City IA, United States of America 63 Department of Physics and Astronomy, Iowa State University, Ames IA, United States of America 64 Joint Institute for Nuclear Research, JINR Dubna, Dubna, Russia 65 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 66 Graduate School of Science, Kobe University, Kobe, Japan 67 Faculty of Science, Kyoto University, Kyoto, Japan 68 Kyoto University of Education, Kyoto, Japan 69 Department of Physics, Kyushu University, Fukuoka, Japan 70 Instituto de F´ısica La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 71 Physics Department, Lancaster University, Lancaster, United Kingdom 72 (a)INFN Sezione di Lecce; (b)Dipartimento di Matematica e Fisica, Universit`a del Salento, Lecce, Italy 73 Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom 74 Department of Physics, Joˇzef Stefan Institute and University of Ljubljana, Ljubljana, Slovenia 75 School of Physics and Astronomy, Queen Mary University of London, London, United Kingdom 76 Department of Physics, Royal Holloway University of London, Surrey, United Kingdom 77 Department of Physics and Astronomy, University College London, London, United Kingdom 78 Louisiana Tech University, Ruston LA, United States of America 79 Laboratoire de Physique Nucl´eaire et de Hautes Energies, UPMC and Universit´e Paris-Diderot and CNRS/IN2P3, Paris, France 80 Fysiska institutionen, Lunds universitet, Lund, Sweden 81 Departamento de Fisica Teorica C-15, Universidad Autonoma de Madrid, Madrid, Spain 82 Institut f¨ur Physik, Universit¨at Mainz, Mainz, Germany 83 School of Physics and Astronomy, University of Manchester, Manchester, United Kingdom 84 CPPM, Aix-Marseille Universit´e and CNRS/IN2P3, Marseille, France 85 Department of Physics, University of Massachusetts, Amherst MA, United States of America 86 Department of Physics, McGill University, Montreal QC, Canada 87 School of Physics, University of Melbourne, Victoria, Australia 88 Department of Physics, The University of Michigan, Ann Arbor MI, United States of America 89 Department of Physics and Astronomy, Michigan State University, East Lansing MI, United States of America 90 (a)INFN Sezione di Milano; (b)Dipartimento di Fisica, Universit`a di Milano, Milano, Italy 91 B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Republic of Belarus 92 National Scientific and Educational Centre for Particle and High Energy Physics, Minsk, Republic of Belarus 93 Department of Physics, Massachusetts Institute of Technology, Cambridge MA, United States of America 94 Group of Particle Physics, University of Montreal, Montreal QC, Canada 95 P.N. Lebedev Institute of Physics, Academy of Sciences, Moscow, Russia – 82 – JHEP11(2014)118 96 Institute for Theoretical and Experimental Physics (ITEP), Moscow, Russia 97 Moscow Engineering and Physics Institute (MEPhI), Moscow, Russia 98 D.V.Skobeltsyn Institute of Nuclear Physics, M.V.Lomonosov Moscow State University, Moscow, Russia 99 Fakult¨at f¨ur Physik, Ludwig-Maximilians-Universit¨at M¨unchen, M¨unchen, Germany 100 Max-Planck-Institut f¨ur Physik (Werner-Heisenberg-Institut), M¨unchen, Germany 101 Nagasaki Institute of Applied Science, Nagasaki, Japan 102 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan 103 (a)INFN Sezione di Napoli; (b)Dipartimento di Fisica, Universit`a di Napoli, Napoli, Italy 104 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM, United States of America 105 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands 106 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, Netherlands 107 Department of Physics, Northern Illinois University, DeKalb IL, United States of America 108 Budker Institute of Nuclear Physics, SB RAS, Novosibirsk, Russia 109 Department of Physics, New York University, New York NY, United States of America 110 Ohio State University, Columbus OH, United States of America 111 Faculty of Science, Okayama University, Okayama, Japan 112 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK, United States of America 113 Department of Physics, Oklahoma State University, Stillwater OK, United States of America 114 Palack´y University, RCPTM, Olomouc, Czech Republic 115 Center for High Energy Physics, University of Oregon, Eugene OR, United States of America 116 LAL, Universit´e Paris-Sud and CNRS/IN2P3, Orsay, France 117 Graduate School of Science, Osaka University, Osaka, Japan 118 Department of Physics, University of Oslo, Oslo, Norway 119 Department of Physics, Oxford University, Oxford, United Kingdom 120 (a)INFN Sezione di Pavia; (b)Dipartimento di Fisica, Universit`a di Pavia, Pavia, Italy 121 Department of Physics, University of Pennsylvania, Philadelphia PA, United States of America 122 Petersburg Nuclear Physics Institute, Gatchina, Russia 123 (a)INFN Sezione di Pisa; (b)Dipartimento di Fisica E. Fermi, Universit`a di Pisa, Pisa, Italy 124 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA, United States of America 125 (a)Laboratorio de Instrumentacao e Fisica Experimental de Particulas - LIP, Lisboa; (b)Faculdade 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 and CAFPE, Universidad de Granada, Granada (Spain); (g)Dep Fisica and CEFITEC of Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal 126 Institute of Physics, Academy of Sciences of the Czech Republic, Praha, Czech Republic 127 Czech Technical University in Prague, Praha, Czech Republic 128 Faculty of Mathematics and Physics, Charles University in Prague, Praha, Czech Republic 129 State Research Center Institute for High Energy Physics, Protvino, Russia 130 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, United Kingdom 131 Physics Department, University of Regina, Regina SK, Canada 132 Ritsumeikan University, Kusatsu, Shiga, Japan 133 (a)INFN Sezione di Roma; (b)Dipartimento di Fisica, Sapienza Universit`a di Roma, Roma, Italy 134 (a)INFN Sezione di Roma Tor Vergata; (b)Dipartimento di Fisica, Universit`a di Roma Tor Vergata, Roma, Italy 135 (a)INFN Sezione di Roma Tre; (b)Dipartimento di Matematica e Fisica, Universit`a Roma Tre, – 83 – JHEP11(2014)118 Roma, Italy 136 (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-Agdal, Rabat, Morocco 137 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 138 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA, United States of America 139 Department of Physics, University of Washington, Seattle WA, United States of America 140 Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom 141 Department of Physics, Shinshu University, Nagano, Japan 142 Fachbereich Physik, Universit¨at Siegen, Siegen, Germany 143 Department of Physics, Simon Fraser University, Burnaby BC, Canada 144 SLAC National Accelerator Laboratory, Stanford CA, United States of America 145 (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 146 (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 147 (a)Department of Physics, Stockholm University; (b)The Oskar Klein Centre, Stockholm, Sweden 148 Physics Department, Royal Institute of Technology, Stockholm, Sweden 149 Departments of Physics & Astronomy and Chemistry, Stony Brook University, Stony Brook NY, United States of America 150 Department of Physics and Astronomy, University of Sussex, Brighton, United Kingdom 151 School of Physics, University of Sydney, Sydney, Australia 152 Institute of Physics, Academia Sinica, Taipei, Taiwan 153 Department of Physics, Technion: Israel Institute of Technology, Haifa, Israel 154 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 155 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 156 International Center for Elementary Particle Physics and Department of Physics, The University of Tokyo, Tokyo, Japan 157 Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 158 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 159 Department of Physics, University of Toronto, Toronto ON, Canada 160 (a)TRIUMF, Vancouver BC; (b)Department of Physics and Astronomy, York University, Toronto ON, Canada 161 Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan 162 Department of Physics and Astronomy, Tufts University, Medford MA, United States of America 163 Centro de Investigaciones, Universidad Antonio Narino, Bogota, Colombia 164 Department of Physics and Astronomy, University of California Irvine, Irvine CA, United States of America 165 (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 166 Department of Physics, University of Illinois, Urbana IL, United States of America 167 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 168 Instituto de F´ısica Corpuscular (IFIC) and Departamento de F´ısica At´omica, Molecular y Nuclear and Departamento de Ingenier´ıa Electr´onica and Instituto de Microelectr´onica de Barcelona (IMB-CNM), University of Valencia and CSIC, Valencia, Spain – 84 – JHEP11(2014)118 169 Department of Physics, University of British Columbia, Vancouver BC, Canada 170 Department of Physics and Astronomy, University of Victoria, Victoria BC, Canada 171 Department of Physics, University of Warwick, Coventry, United Kingdom 172 Waseda University, Tokyo, Japan 173 Department of Particle Physics, The Weizmann Institute of Science, Rehovot, Israel 174 Department of Physics, University of Wisconsin, Madison WI, United States of America 175 Fakult¨at f¨ur Physik und Astronomie, Julius-Maximilians-Universit¨at, W¨urzburg, Germany 176 Fachbereich C Physik, Bergische Universit¨at Wuppertal, Wuppertal, Germany 177 Department of Physics, Yale University, New Haven CT, United States of America 178 Yerevan Physics Institute, Yerevan, Armenia 179 Centre de Calcul de l’Institut National de Physique Nucl´eaire et de Physique des Particules (IN2P3), Villeurbanne, France 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 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, United Kingdom dAlso at TRIUMF, Vancouver BC, Canada eAlso at Department of Physics, California State University, Fresno CA, United States of America fAlso at Tomsk State University, Tomsk, Russia gAlso at CPPM, Aix-Marseille Universit´e and CNRS/IN2P3, Marseille, France hAlso at Universit`a di Napoli Parthenope, Napoli, Italy iAlso at Institute of Particle Physics (IPP), Canada jAlso at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia kAlso at Chinese University of Hong Kong, China lAlso at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece mAlso at Louisiana Tech University, Ruston LA, United States of America nAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain oAlso at Department of Physics, The University of Texas at Austin, Austin TX, United States of America pAlso at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia qAlso at CERN, Geneva, Switzerland rAlso at Ochadai Academic Production, Ochanomizu University, Tokyo, Japan sAlso at Manhattan College, New York NY, United States of America tAlso at Novosibirsk State University, Novosibirsk, Russia uAlso at Institute of Physics, Academia Sinica, Taipei, Taiwan vAlso at LAL, Universit´e Paris-Sud and CNRS/IN2P3, Orsay, France wAlso at Academia Sinica Grid Computing, Institute of Physics, Academia Sinica, Taipei, Taiwan xAlso at Laboratoire de Physique Nucl´eaire et de Hautes Energies, UPMC and Universit´e Paris-Diderot and CNRS/IN2P3, Paris, France yAlso at School of Physical Sciences, National Institute of Science Education and Research, Bhubaneswar, India zAlso at Dipartimento di Fisica, Sapienza Universit`a di Roma, Roma, Italy aa Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia ab Also at section de Physique, Universit´e de Gen`eve, Geneva, Switzerland ac Also at International School for Advanced Studies (SISSA), Trieste, Italy ad Also at Department of Physics and Astronomy, University of South Carolina, Columbia SC, United States of America ae Also at School of Physics and Engineering, Sun Yat-sen University, Guangzhou, China af Also at Faculty of Physics, M.V.Lomonosov Moscow State University, Moscow, Russia ag Also at Moscow Engineering and Physics Institute (MEPhI), Moscow, Russia ah Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, – 85 – JHEP11(2014)118 Hungary ai Also at Department of Physics, Oxford University, Oxford, United Kingdom aj Also at Department of Physics, Nanjing University, Jiangsu, China ak Also at Institut f¨ur Experimentalphysik, Universit¨at Hamburg, Hamburg, Germany al Also at Department of Physics, The University of Michigan, Ann Arbor MI, United States of America am Also at Discipline of Physics, University of KwaZulu-Natal, Durban, South Africa an Also at University of Malaya, Department of Physics, Kuala Lumpur, Malaysia ∗Deceased – 86 –