Search for long-lived charginos based on a disappearing-track signature in pp collisions at √s = 13TeV with the ATLAS detector

JHEP06(2018)022 Published for SISSA by Springer Received:December 7, 2017 Revised:March 15, 2018 Accepted:May 11, 2018 Published:June 5, 2018 Search for long-lived charginos based on a disappearing-track signature in pp collisions at √s=13 TeV with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: This paper presents a search for direct electroweak gaugino or gluino pair production with a chargino nearly mass-degenerate with a stable neutralino. It is based on an integrated luminosity of 36.1 fb−1of pp collisions at √s= 13 TeV collected by the ATLAS experiment at the LHC. The final state of interest is a disappearing track accompanied by at least one jet with high transverse momentum from initial-state radiation or by four jets from the gluino decay chain. The use of short track segments reconstructed from the innermost tracking layers significantly improves the sensitivity to short chargino lifetimes. The results are found to be consistent with Standard Model predictions. Exclusion limits are set at 95% confidence level on the mass of charginos and gluinos for different chargino lifetimes. For a pure wino with a lifetime of about 0.2 ns, chargino masses up to 460 GeV are excluded. For the strong production channel, gluino masses up to 1.65 TeV are excluded assuming a chargino mass of 460 GeV and lifetime of 0.2 ns. Keywords: Hadron-Hadron scattering (experiments) ArXiv ePrint: 1712.02118 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP06(2018)022 JHEP06(2018)022 Contents 1 Introduction 2 2 ATLAS detector 3 3 Analysis overview 4 3.1 Signal processes 4 3.2 Background sources 5 3.3 Analysis method 6 4 Data and simulated event samples 6 5 Reconstruction and event selection 8 5.1 Event reconstruction 8 5.2 Event selection 11 5.3 Signal acceptance and efficiency 11 6 Signal and background estimation 14 6.1 Background templates 14 6.1.1 Smearing function 14 6.1.2 Hadron background 15 6.1.3 Charged-lepton background 16 6.1.4 Templates for scattered particles 17 6.1.5 Fake tracklets 17 6.2 Signal templates 17 6.3 Fit to the pTspectrum 18 7 Systematic uncertainties 19 7.1 Background uncertainties 19 7.2 Signal uncertainties 19 8 Results and interpretation 21 9 Conclusions 25 A Likelihood function 26 The ATLAS collaboration 31 – 1 – JHEP06(2018)022 1 Introduction Supersymmetry (SUSY) [1–6] is a space-time symmetry that relates fermions and bosons. It predicts new particles that differ from their Standard Model (SM) partners by a half unit of spin. If R-parity is conserved [7], SUSY particles are produced in pairs and decay such that their final products consist only of SM particles and the stable lightest supersymmetric particle (LSP). In many supersymmetric models, the supersymmetric partners of the SM Wboson fields, the wino fermions, are the lightest gaugino states. In this case, the lightest of the charged mass eigenstates, a chargino, and the lightest of the neutral mass eigenstates, a neutralino, are both almost pure wino and nearly mass-degenerate. As a result, the lightest chargino can have a lifetime long enough that it can reach the ATLAS detector before decaying. For example, anomaly-mediated supersymmetry breaking (AMSB) scenarios [8,9] naturally predict a pure wino LSP, which is a dark-matter candidate. The mass-splitting between the charged and neutral wino (∆m˜χ1) in such models is suppressed at tree level by the approximate custodial symmetry; it has been calculated at the two-loop level to be around 160 MeV [10], corresponding to a chargino lifetime of about 0.2 ns [11]. This prediction for the value of the lifetime is actually a general feature of models with a wino LSP: within the generated models of the ATLAS phenomenological Minimal Supersymmetric Standard Model (pMSSM) scan [12] that have a wino-like LSP, about 70% have a charged-wino lifetime between 0.15 ns and 0.25 ns. Most of the models in the other 30% have a larger mass-splitting (and therefore the charged wino has a shorter lifetime) due to a non-decoupled higgsino mass. The search presented here is sensitive to a wide range of lifetimes, from 10 ps to 10 ns, and reaches maximum sensitivity for lifetimes around 1 ns. The decay products of SUSY particles that are strongly mass-degenerate with the lightest neutralino leave little visible energy in the detector. Thus, the corresponding searches represent a significant challenge for the LHC experiments. If a charged SUSY particle produced in a high-energy collider had a relatively long lifetime, it would leave multiple hits1 in the traversed tracking layers before decaying, and could then be reconstructed as a track segment in the innermost part of the detector [13–15]. In the models considered in this paper, a long-lived chargino decays into a pion and the LSP, a neutralino. The pion emitted in the transition from the lightest chargino (˜χ± 1) to the lightest neutralino (˜χ0 1) typically has very low momentum and it is not reconstructed in the detector. The neutralino is assumed to pass through the detector without interacting. A track arising from a long-lived chargino can therefore disappear, i.e., leave hits only in the innermost layers and no hits in the portions of the detector at higher radii. Figure 1shows an example of a simulated signal event in which a long-lived chargino decays into a neutralino and a low-momentum pion in the ATLAS detector. Searches for long-lived, massive, charged particles using measurements of ionization energy loss and timing information are also sensitive to long-lived charginos [16–18], with a lower efficiency for selecting signals with lifetimes around 0.2 ns, 1A hit is a space-time point which represents interactions between a particle and material in an active region of a particle detector. – 2 – JHEP06(2018)022 ATLAS Simulation π+ χ0 1 ~χ+ 1 ~ Figure 1. Illustration of a pp →˜χ+ 1˜χ− 1+ jet event, with long-lived charginos. Particles produced in pile-up pp interactions are not shown. The ˜χ+ 1decays into a low-momentum pion and a ˜χ0 1after leaving hits in the four pixel layers (indicated by red makers). relative to the disappearing-track signature. The disappearing-track signature provides the most sensitive search to date for SUSY models with charginos with O(ns) lifetimes. Previous searches for a disappearing-track signature were performed by the ATLAS [19] and CMS [20] collaborations using the full dataset of the LHC pp run at a centre-ofmass energy of √s= 8 TeV. These searches excluded chargino masses below 270 GeV and 260 GeV respectively, with a chargino proper lifetime (τ˜χ± 1) of 0.2 ns. In the previous ATLAS analysis, a special tracking algorithm was used to reconstruct short tracks, and the search was sensitive to charginos decaying at radii larger than about 30 cm. A crucial improvement in the analysis described here is the use of even shorter tracks, called tracklets, which allows the reconstruction of charginos decaying at radii from about 12 cm to 30 cm. The use of these tracklets is possible thanks to the new innermost tracking layer [21,22] installed during the LHC long shutdown between Run 1 and Run 2. The use of shorter tracklets significantly extends the sensitivity to smaller chargino lifetimes. This paper is organised as follows. A brief overview of the ATLAS detector is given in section 2. In section 3, the signal processes and backgrounds are described and an overview of the analysis method is given. The data samples used in this analysis and the simulation model of the signal processes are described in section 4. The reconstruction algorithms and event selection are presented in section 5. The analysis method is discussed in section 6. The systematic uncertainties are described in section 7. The results are presented in section 8. Section 9is devoted to conclusions. 2 ATLAS detector ATLAS [23] is a multipurpose detector with a forward-backward symmetric cylindrical geometry, covering nearly the entire solid angle around an interaction point of the LHC.2 2ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point in the centre of the detector. The positive x-axis is defined by the direction from the interaction point to the centre of the LHC ring, with the positive y-axis pointing upwards, while the beam direction defines the z-axis. Cylindrical coordinates (r, φ) are used in the transverse plane, φbeing the azimuthal angle around the – 3 – JHEP06(2018)022 The inner tracking detector (ID) consists of pixel and micro-strip silicon detectors covering the pseudorapidity region of |η|<2.5, surrounded by a transition radiation tracker (TRT), which improves the momentum measurement and enhances electron identification capabilities. The pixel detector spans the radius range from 3 cm to 12 cm, the strip semiconductor tracker (SCT) from 30 cm to 52 cm, and the TRT from 56 cm to 108 cm. The pixel detector has four barrel layers, and three disks in each of the forward and backward regions. The barrel layers surround the beam pipe at radii of 33.3, 50.5, 88.5, and 122.5 mm, covering |η|<1.9. These layers are equipped with pixels which have a width of 50 µm in the transverse direction. The pixel sizes in the longitudinal direction are 250 µm for the first layer and 400 µfor the other layers. The innermost layer, the insertable B-layer [21,22], was added during the long shutdown between Run 1 and Run 2 and improves the reconstruction of tracklets by adding an additional measurement point close to the interaction point. The ID is surrounded by a thin superconducting solenoid providing an axial 2 T magnetic field and by a fine-granularity lead/liquid-argon (LAr) electromagnetic calorimeter covering |η|<3.2. The calorimeters in the region of 3.1<|η|<4.9 are made of LAr active layers with either copper or tungsten as the absorber material. A steel/scintillator-tile calorimeter provides coverage for hadronic showers in the central pseudorapidity range of |η|<1.7. LAr hadronic end-cap calorimeters, which use lead as absorber, cover the forward region of 1.5<|η|<3.2. The muon spectrometer with an air-core toroid magnet system surrounds the calorimeters. The ATLAS trigger system [24] consists of a hardware-based level-1 trigger followed by a software-based high-level trigger. 3 Analysis overview 3.1 Signal processes If the gluino mass is too large to yield a sizeable production cross-section, electroweakgaugino direct pair production could be the only gaugino production mode within reach at LHC energies. If the gluino mass is relatively light, however, gluino pair production becomes the dominant process, and charginos can be produced in cascade decays of the gluino. For large mass separations between the gluino and the chargino, the relatively large transverse momentum (pT) transferred to the chargino typically leads to higher kinematic selection efficiencies and larger chargino decay radii relative to charginos from gaugino pair production. Two complementary searches are described here: one targets direct electroweak-gaugino pair production and the other targets gluino pair production in which at least one long-lived chargino is produced in the subsequent decay of the gluinos. In both searches, events are selected with a trigger based on the magnitude of the missing transverse momentum in the event (Emiss T). A candidate event is required to have at least one “pixel tracklet”, which is a tracklet with no associated SCT hits. Candidate pixel tracklets are required to have pT>5 GeV. z-axis. The pseudorapidity ηis defined in terms of the polar angle θby η=−ln tan(θ/2) and the rapidity is defined as y= (1/2) ln[(E+pz)/(E−pz)] where Eis the energy and pzthe longitudinal momentum of the object of interest. – 4 – JHEP06(2018)022 ˜χ± 1 p p ˜χ0 1 ˜χ0 1 π± j (a) pp →˜χ± 1˜χ0 1j. ˜g ˜g ˜χ± 1 p p ˜χ0 1 q q ˜χ0 1 π± q q (b) pp →˜χ± 1˜χ0 1qqqq. Figure 2. Example diagrams of the benchmark signal processes used in this analysis. In the case of direct chargino/neutralino production (a), the signal signature consists of a long-lived chargino, missing transverse momentum and initial-state radiation. In the case of the strong channel (b), each gluino decays into two quarks and a chargino or neutralino. A long-lived chargino, missing transverse momentum and multiple quarks, which are observed as jets, are the signatures of this signal. Electroweak production. This search targets the production processes pp →˜χ± 1˜χ0 1j and pp →˜χ+ 1˜χ− 1j, where jdenotes an energetic jet from initial-state radiation (ISR). The presence of the ISR jet is required to ensure significant Emiss Tand hence high trigger efficiency. An example diagram for the pp →˜χ± 1˜χ0 1jprocess is presented in figure 2a. The resulting signal topology is characterised by a high-pTjet, large Emiss T, and at least one high-pTpixel tracklet. Strong production. This search targets gluino pair production with a long-lived chargino in the decay chains pp →˜g˜g→qqqq˜χ± 1˜χ0 1and pp →˜g˜g→qqqq˜χ± 1˜χ± 1. These are typical decay modes in AMSB models. An example diagram is shown in figure 2b. The signal topology is characterised by four high-pTjets, large Emiss T, and at least one high-pT pixel tracklet. 3.2 Background sources The main SM background processes for the two analysis channels are top-quark pair production (tt) and Wboson production associated with hadron jets (W+jets) with subsequent decay W→eν, τν. Hadrons or leptons in these events can be reconstructed as a pixel tracklet if they interact with the detector material and any hits in the tracking detectors after the pixel detector are not assigned to the reconstructed tracklet. Interactions that contribute to this background include severe multiple-scattering, hadronic interactions or, in the case of leptons, bremsstrahlung, as shown in figure 3a and 3b. The other main category of background is from “fake” tracklets, which originate from random combinations of hits from two or more particles, as shown in figure 3c. – 5 – JHEP06(2018)022 Pixel SCT (a) Pixel SCT (b) Pixel SCT (c) Figure 3. Sketch of the different background components in the search with pixel tracklets. Thin solid and dotted red lines show trajectories of charged and neutral particles respectively. Thick blue lines show reconstructed pixel tracklets. (a) A hadron undergoing a hard scattering can yield track segments in the pixel and SCT detector that are not recognised as belonging to the same track, thus faking a pixel tracklet. (b) A lepton emitting hard photon radiation could be identified as a pixel tracklet through a similar mechanism. (c) A pixel tracklet can arise from a random combination of hits created by different particles in close proximity. 3.3 Analysis method Candidate events are required to have large Emiss T, at least one high-pTjet, and at least one isolated pixel tracklet. A lepton-veto is used to suppress background events from W/Z + jets and top-pair production processes. Kinematic requirements, optimised for each channel, are applied to enhance the signal purity in the event samples. After selection, the search is performed by looking for an excess of candidate events in the pTdistribution of pixel tracklets. The shapes of the pTspectrum for the background from hadrons, muons, electrons, and fake tracklets are derived from data using dedicated techniques for each background process. A fit to the observed pTdistribution to extract the normalisation of the total background component and the signal strength is performed simultaneously in a low-Emiss Tcontrol region, two fake-tracklet control regions, and a high-Emiss Tsignal region. The regions are defined by the requirements described in section 5and in section 6.3. The expected signal spectrum and yield are estimated from simulation and the measured detector performance. Further details are given in section 6. 4 Data and simulated event samples The data used in this analysis were recorded by the ATLAS detector in 2015 and 2016. The pp centre-of-mass energy was 13 TeV and the bunch spacing was 25 ns. The mean number of pp interactions per bunch crossing in the dataset was 14 in 2015 and 24 in 2016. Events were selected by Emiss Ttriggers [25] with trigger thresholds varying from 70 GeV to 110 GeV depending on the data-taking period. Data samples used to estimate the background contribution and to measure tracking performance were selected using triggers – 6 – JHEP06(2018)022 requiring at least one isolated electron (pT>24–26 GeV) or muon (pT>20–26 GeV). After applying basic data-quality requirements, the data sample corresponds to an integrated luminosity of 36.1 fb−1. The uncertainty in the combined 2015+2016 integrated luminosity is 3.2%. It is derived, following a methodology similar to that detailed in ref. [26], from a preliminary calibration of the luminosity scale using x–ybeam-separation scans performed in August 2015 and May 2016. The simulated signal samples were generated assuming the minimal AMSB model [8,9] with tan β= 5, the sign of the higgsino mass term set to be positive, and the universal scalar mass set to m0= 5 TeV. The proper lifetime and the mass of the chargino were scanned in the range from 10 ps to 10 ns and from 100 GeV to 700 GeV respectively. For the strong production, samples were generated for gluino masses (m˜g) varying from 700 GeV to 2200 GeV with LSP mass from 200 GeV to m˜g−100 GeV. The SUSY mass spectrum, the branching ratios and decay widths were calculated using ISASUSY 7.80 [27]. The signal samples were generated with up to two extra partons in the matrix element using MG5 aMC@NLO 2.3.3 [28] at leading order (LO) interfaced to Pythia 8.212 [29] for parton showering, hadronisation and SUSY particle decay. The NNPDF2.3LO [30] parton distribution function (PDF) set was used. Renormalisation and factorisation scales were determined by the default dynamic scale choice of MG5 aMC@NLO. The CKKW-L merging scheme [31] was applied to combine tree-level matrix elements containing multiple partons with parton showers. The scale parameter for merging was set to a quarter of the mass of the wino for wino-pair production or a quarter of the gluino mass for the strong production channel. The A14 [32] set of tuned parameters with simultaneously optimised multiparton interaction and parton shower parameters was used for the underlying event together with the NNPDF2.3LO PDF set. Charginos were assumed to be stable in the event-generation step. The cross-sections for the electroweak production are calculated at next-to-leading order (NLO) in the strong coupling constant using Prospino2 [33]. The cross-sections for the strong production are calculated in the same way as in the electroweak channel, adding the resummation of soft gluon emission at next-to-leading-logarithm accuracy (NLO + NLL) [34]. In both channels, an envelope of cross-section predictions is defined using the 68% confidence level (CL) ranges of the CTEQ6.6 PDF set [35], including the αS uncertainty, and MSTW2008 PDF set [36], together with variations of the factorisation and renormalisation scales by factors of two or one half. The nominal cross-section value is taken to be the midpoint of the envelope and the uncertainty assigned is half of the full width of the envelope, following the PDF4LHC recommendations [37]. For the strong production mode, the branching ratio of the gluino decay is assumed to be 1/3 for each of the following decays: ˜g→qq ˜χ0, ˜g→qq ˜χ−and ˜g→qq ˜χ+. Only firstand second-generation quarks (d, u, s, c) are considered. Direct electroweak-gaugino production is not considered in the strong channel. The cross-section for the electroweak production, including at least one chargino, varies from 47 pb to 13 fb as the wino mass increases from 100 GeV to 700 GeV with the uncertainty in the cross-section ranging from 8.6% to 7.3%. The cross-section for gluino production varies from 3.5 pb to 0.36 fb as the gluino mass increases from 700 GeV to 2200 GeV with the uncertainty increasing from 14% to 36%. – 7 – JHEP06(2018)022 The response of the detector to particles was modelled with the full ATLAS detector simulation [38] based on Geant4 [39]. All simulated events were overlaid with additional pp interactions in the same and neighbouring bunch crossings (pile-up) simulated with the soft QCD processes of Pythia 8.186 using the A2 set of tuned parameters [40] and the MSTW2008LO PDF set. Charginos were forced to decay into a pion and a neutralino in Geant4. The simulated events are reconstructed in the same way as the data, and are reweighted so that the distribution of the average number of collisions per bunch crossing matches the one observed in the data. The Emiss Ttrigger efficiency is measured as a function of the offline Emiss Tusing a data control sample consisting of events selected by the muon triggers and an additional offline selection designed to extract nearly pure W→µν events. For Emiss T>200 GeV, the trigger efficiency is almost 100%. The measured trigger efficiency is used to directly estimate the probability for signal events to pass the trigger. The trigger efficiency for the direct electroweak production signal is about 20%, depending on the assumed SUSY particle masses. In the strong production search, the trigger efficiency is over 90% when the mass difference between the gluino and the LSP is above 300 GeV, and it decreases to about 55% for a mass difference of 100 GeV. 5 Reconstruction and event selection 5.1 Event reconstruction Primary vertices are reconstructed from two or more tracks with pT>400 MeV. When two or more vertices are reconstructed, the one with the largest sum of p2 Tof the associated tracks is used. Events are required to have at least one reconstructed primary vertex. Jets are reconstructed from noise-suppressed energy clusters [41] of calorimeter cells using an anti-ktalgorithm [42] with a radius parameter of 0.4 as implemented in the FastJet package [43]. An area-based correction is applied to account for energy from additional pp collisions based on an estimate of the pile-up activity in a given event [44]. Further corrections derived from the average jet response in simulation and data are used to calibrate the jet energies to the scale of their constituent particles [45]. Jets are required to have pT>20 GeV and |η|<2.8. Additional selection criteria are applied to the tracks associated with jets [46] with pT<60 GeV and |η|<2.4 to reduce the number of jets originating from pile-up interactions. Muon candidates are reconstructed by combining a track reconstructed by the muon spectrometer (MS track) with one recorded by the ID. They are required to satisfy ‘Medium’ quality requirements described in ref. [47] and to have pT>10 GeV and |η|<2.7. Electron candidates are reconstructed from energy clusters in the electromagnetic calorimeter with a matching track in the ID. They are required to satisfy the ‘Loose’ likelihood-based identification criteria described in ref. [48]. They are further required to have transverse energy ET>10 GeV and |η|<2.47. After the requirements described above, ambiguities between candidate jets and leptons are resolved as follows. First, any jet candidate which is within a distance ∆R≡p(∆η)2+ (∆φ)2= 0.2 of an electron candidate is discarded. Second, if an – 8 – JHEP06(2018)022 100−50−0 50 100 ] -1 ) [TeV T p/q (∆ 5− 10 4− 10 3− 10 2− 10 1− 10 -1 Fraction of tracklets / 4 TeV ATLAS -1 =13TeV, 2.0 fbs Data Fit (a) Track(let)s / GeV 2− 10 1− 10 1 10 2 10 3 10 Standard tracks Pixel tracklets Smeared tracks ATLAS -1 =13TeV, 2.0 fbs candidatesµµ →Z [GeV] T p 100 1000 10000 (Pixel tracklets) (Smeared tracks) / 0 0.5 1 1.5 2 (b) Figure 5. (a) Distribution of the difference between q/pTof a pixel tracklet and a track in Z→µµ events in data. The solid curve shows the smearing function (eq. (6.1)) used to construct the background pTtemplate, which is described in section 6.1. The parameter values of the curve are α=1.67, β=−1.72 TeV−1and σ=13.2 TeV−1. The red band indicates a 1σvariation of the systematic uncertainty (see section 7). The data are normalised to unit area. (b) Validation of the smearing procedure in Z→µµ events in data. The green and red points show the pTdistributions of tracks and pixel tracklets respectively. The blue point shows the pTspectrum obtained by convolving the track pTdistribution with the smearing function. The lower plot shows the ratio of the smeared spectrum to the distribution of the pixel tracklets. 6.1.2 Hadron background Assuming that the pTspectrum of hadrons scattered in the ID is the same as that of non-scattered hadrons, the pTspectra of scattered hadrons can be extracted from tracks in control samples of non-scattered hadrons. This assumption was verified with simulation. The control samples are obtained by applying the same kinematic requirements as in the signal regions and then selecting samples of tracks which satisfy the following requirements: •The number of associated hits in the TRT must be larger than 15, and the number of associated hits in the SCT must be larger than 6. •There must be associated energy deposits in the calorimeter: the transverse energy deposited in the calorimeter in a cone of ∆R= 0.2 around the track, excluding the energy cluster associated with the track, (Econe20 T) must satisfy Econe20 T>3 GeV, and the sum of cluster energies in a cone of ∆R= 0.4 around the track (P∆R<0.4Eclus T) divided by the pTof the track must satisfy P∆R<0.4Eclus T/pT>0.5. The first requirement selects good-quality tracks which have not undergone scattering in the silicon layers. The second requirement removes electron and muon tracks from the control region. The pTspectra of the control samples are convolved with the smearing function to take into account the resolution of the pixel tracklets. Separate pTspectra are prepared for the high-Emiss Tregion and the low-Emiss Tregion in each channel. – 15 – JHEP06(2018)022 6.1.3 Charged-lepton background In order to obtain the pTspectra of background tracklets originating from leptons, events containing a lepton without significant scattering due to hard bremsstrahlung are used. The lepton pTspectra obtained from these events are scaled to take into account the probability of significant scattering and are smeared to take into account the poor pTresolution of pixel tracklets. Events containing exactly one lepton which satisfy the same kinematic requirements as for the signal regions, excluding the lepton-veto, are used. The lepton is required to have an associated inner detector track with pT>16 GeV which satisfies the same quality selection as for tracklets, except for the SCT veto and the isolation from candidate electrons and muons. The pTdistribution of background tracklets from leptons is obtained by multiplying the pTdistribution of the lepton control sample by a transfer factor, which rescales the number of identified leptons to that of pixel tracklets. The transfer factor is found to be pT-dependent for electrons, and ηand φ-dependent for muons, as described below. The transfer factor is extracted with a tag-and-probe method using Z→`` events in data which are selected by a single-lepton trigger. Tag and probe leptons are selected by applying requirements discussed below. The tag-probe pair is further required to have an invariant mass within 10 GeV of the Zboson pole mass. A tag electron is required to fully satisfy track-based isolation criteria and likelihoodbased ‘Tight’ electron identification criteria, to match the electron which triggered the event and to have pT>30 GeV. Probe electrons are identified as clusters of energy in the calorimeter with an associated track satisfying the quality, isolation, high-pTand geometrical acceptance requirements defined in section 5.1 for signal tracks and tracklets. The probe track has to satisfy either the full pixel tracklet selection, including the disappearance condition, or the tight electron selection. The transfer factor is defined as the ratio of the number of probe electrons which satisfy the full tracklet selection to the number of probe electrons which satisfy the tight electron selection, as a function of electron pT. The transfer factor is O(10−2)–O(10−4), depending on electron pT, and is below 10−5for electrons with pT>50 GeV. A muon used as a tag must satisfy track-based isolation criteria and cut-based ‘Tight’ identification criteria. The transfer factor for muons is the product of two components: the probability for a muon ID track to be classified as disappearing and the probability for a muon ID track not to have an associated MS track. As the pixel tracklets in the signal region are required to be isolated from MS tracks, the second component of the transfer factor allows an estimation of the expected normalisation as well as the pTdistribution of the muon background. The first component of the muon transfer factor is estimated with a method similar to that used for the electron transfer factor. The same selection criteria as the electron case are applied to the tag and probe muons, replacing the electron identification criteria with those for the muon. The first component of the muon transfer factor is found to be 4.5×10−4. The second component is necessary because an MS track is used as a probe to measure the first component. The second component is evaluated with a similar tag-and-probe method, where the probe is taken from a sample of well-measured – 16 – JHEP06(2018)022 tracks which pass through the full ID, selected by requiring more than 15 TRT hits on the track. The probability for an MS track to be geometrically matched to an ID track is calculated from this sample. The transfer factor is measured as a function of ηand φto fully take into account the detector geometry. The second component of the transfer factor for muons is found to be of the order of 10−2to 10−1. The pTspectra of the lepton control samples are scaled by the transfer factors, then convolved with the smearing function. Two different pTspectra are prepared, one for the high-Emiss Tregion and one for the lowEmiss Tregion, while keeping the same requirements as in the signal region. The expected numbers of muon background events in the low-Emiss Tregion and in the high-Emiss Tregion are estimated by scaling the number of events in the muon control samples by the transfer factor. 6.1.4 Templates for scattered particles The control samples for hadron and electron components are found to have similar pT distributions, which is due in part to the fact that the isolation requirement for tracklets to be separated from jets affects both the electron and hadron background similarly. The two components are therefore combined in the fitting. The muon component is treated separately as the muon control samples are found to have a different pTdistribution. 6.1.5 Fake tracklets Fake tracklets are formed from a random combination of hits. The d0distribution of fake tracklets is broad, whereas the high-pTchargino tracklets have good impact parameter resolution and therefore have values of d0which cluster around zero. The fake-tracklet control region is defined by requiring |d0|/σ(d0)>10, and by removing the Emiss Trequirement. This region is dominated by fake tracklets. The pTspectra of fake tracklets are modelled with the following empirical functional form: f(pT) = exp −p0·log(pT)−p1·(log(pT))2,(6.2) where p0and p1are fit parameters. Figure 6shows the pTdistribution of pixel tracklets in the fake-tracklet control region along with a histogram filled from the result of the fit. The pTspectrum shape is confirmed to be independent of Emiss Tby comparing it in three Emiss Tregions: Emiss T<90 GeV, 90 GeV < Emiss T<140 GeV and Emiss T>140 GeV. A small dependence of the fit parameters on |d0|/σ(d0) is observed by comparing the parameters obtained in three regions: 10 <|d0|/σ(d0)<20, 20 <|d0|/σ(d0)<30 and 30 <|d0|/σ(d0)<100. The size of the dependence on |d0|/σ(d0) is added as an uncertainty in the pTtemplate shape. 6.2 Signal templates The signal pTspectrum is estimated by smearing the generator-level pTdistribution of charginos in the signal simulation for each signal parameter point. The smearing function parameters are determined from muons in data, but shifted by the differences between the parameter values found for charginos and muons in simulation due to the difference between – 17 – JHEP06(2018)022 2− 10 1− 10 1 10 2 10 3 10 Tracklets Fit Data ATLAS -1 =13TeV, 36.1 fbs EW production Fake control region 100 1000 10000 [GeV] T pTracklet 0 0.5 1 1.5 2 Data / Fit (a) 1− 10 1 10 2 10 3 10 Tracklets Fit Data ATLAS -1 =13TeV, 36.1 fbs Strong production Fake control region 100 1000 10000 [GeV] T pTracklet 0 0.5 1 1.5 2 Data / Fit (b) Figure 6. Fit on the fake-tracklet control sample for (a) the electroweak production channel and (b) the strong production channel. The black markers show data. The blue line and the band show the histogram made from the fit function and its uncertainty. The bottom plot shows the ratio of the observed data to the fit histogram. The chi-square per degrees of freedom of the fit are 5.4/14 and 8.5/19 for the electroweak and strong production channels respectively. Red arrows in the Data/Fit ratio indicate bins where the corresponding entry falls outside the plotted range. their masses. This smearing is performed because the tracklet pTresolution measured in reconstructed simulated samples is narrower than the resolution measured in data. 6.3 Fit to the pTspectrum The extended likelihood function, described in detail in appendix A, consists of signal and background components. The background components represent tracklets from muons, fakes, and the sum of hadron and electron contributions. The fit parameters are the signal strength, the normalisations of the sum of the hadron and electron, muon, and fake-tracklet backgrounds, the fake-tracklet pTdistribution’s fit parameters, and nuisance parameters. The nuisance parameters are allowed to float in the fit with Gaussian constraints to include systematic uncertainties, discussed in section 7. The number of signal events and the sum of hadron and electron events are fit without a Gaussian-constraint term. The number of muon events and the sum of hadron and electron events are fit with independent individual normalisation factors in the low-Emiss Tand high-Emiss Tregions. The number of muon events is constrained by a Gaussian term which represents the expectation described in section 6.1.3. The statistical uncertainty in the transfer factors for muons is propagated to the final template. The fake-tracklet control region is divided into two parts, a low-Emiss T and a high-Emiss Tfake-tracklet control region, by applying the same Emiss Trequirement as in the signal region. The signal regions and the two parts in the fake-tracklet control region are fit simultaneously and the ratio of the number of fake tracklets in the high-Emiss T – 18 – JHEP06(2018)022 signal region to that in the low-Emiss Tregion is constrained to the same value as in the fake-tracklet control region. 7 Systematic uncertainties 7.1 Background uncertainties An uncertainty in the shape of the hadron and electron pTtemplate was estimated as the maximum difference between the hadron and electron individual templates and found to be negligible. As a combined template is used for hadrons and electrons, the difference in tracklet q/pTresolutions between hadrons and electrons in simulation is added to the systematic uncertainty in the smearing function for the combined template. The red band in figure 5shows the uncertainty in the smearing function. Possible differences between the signal and the fake-tracklet control region leading to systematic uncertainties in the shape of the pTspectrum of the fake-tracklet background are taken into account. The uncertainty is estimated from the d0significance dependence of the parameters of the fake-tracklet pTspectrum function defined in eq. (6.2) for the fake-tracklet control region. A conservative uncertainty of 100% is assigned to the ratio of the number of fake tracklets in the low-Emiss Tcontrol region to the number in the high-Emiss T control region. 7.2 Signal uncertainties A breakdown of the systematic uncertainties in the expected number of signal events passing the signal region requirements is shown in table 3. In addition, an uncertainty in the pTspectrum shape, due the uncertainty in the pTresolution, is taken into account. High-pTjets originating from ISR and final state radiation (FSR) alter the signal acceptance. Uncertainties in the modelling of ISR and FSR are estimated by varying the renormalisation, factorisation and merging scales from 0.5 to 2 times their nominal values, and by comparing samples with one and two additional partons in the matrix element with MG5 aMC@NLO+Pythia8. For the strong channel, the ISR/FSR uncertainty is small when the mass difference between the gluino and chargino is large; however, the uncertainty grows to about 10% when the mass difference is smaller than 200 GeV, as signal events start to be rejected by the requirement on the jet pT. The uncertainties in the jet energy scale and resolution are estimated by the techniques in refs. [53–57]. The uncertainty in the trigger efficiency is small because it is measured from data, as described in section 4. For the signal pTresolution, a conservative uncertainty, corresponding to 100% of the effect of multiple scattering, is added to the uncertainty in the values of parameters in the q/pTsmearing function. The pile-up modelling uncertainty is estimated by varying the number of collisions per bunch crossing in simulation by its uncertainty of 10% of the nominal value. The signal reconstruction efficiency decreases as the number of pile-up interactions increases because it becomes more likely for pixel-detector hits originating from charginos to be used by tracks from other particles. – 19 – JHEP06(2018)022 Relative uncertainties [%] Electroweak channel Strong channel MC statistical uncertainty 6.6 6.5 ISR/FSR 7.6 0.2 Jet energy scale and resolution 2.0 0.7 Trigger efficiency 0.2<0.1 Pile-up modelling 11 Tracklet efficiency 6.9 Luminosity 3.2 Sub-total 17 15 Cross-section 6.4 28 Total 18 32 Table 3. Systematic uncertainties in the signal event yields at m˜χ± 1= 400 GeV for the electroweak channel and at m˜g= 1600 GeV, m˜χ± 1= 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. The uncertainty in the cross-section of the strong production is large due to the large effect from the PDF uncertainty. The uncertainty in the chargino reconstruction efficiency (tracklet efficiency) can be split into four components: (1) the uncertainty in the probability for a chargino to produce a set of pixel-detector hits which can satisfy the tracklet quality selection, (2) the uncertainty in the efficiency to reconstruct a tracklet when a chargino leaves a set of good hits which satisfies the tracklet quality selection, (3) the uncertainty in the track reconstruction efficiency, which depends on the number of pile-up interactions, (4) the uncertainty in the d0significance selection. The first two components are estimated using Z→µµ events, which are selected with the same requirements as for the data sample used to estimate the smearing function. The tracklet data-quality selection requirements are applied to the muon tracks in the sample. The first component is estimated from the difference in the efficiency of these requirements between data and simulation. The second component is estimated by re-fitting the muon tracks using only the pixel hits, and comparing the tracklet reconstruction efficiencies in data and simulation. The third component is included in the uncertainty in the pile-up modelling described already. The fourth component is estimated by shifting the measured |d0|/σ(d0) distribution by its uncertainty; the change in the |d0|/σ(d0) selection efficiency is added to the systematic uncertainty. Theoretical uncertainties in the signal cross-section are estimated by computing the changes in the cross-section when the renormalisation and factorisation scales, the choice of PDFs and the strong coupling constant, αS, are varied. Renormalisation and factorisation scales are varied by factors of 0.5 and 2 from their nominal value. The PDF uncertainty is estimated as the maximum of the uncertainty from the CTEQ6.6 [58] uncertainty band at 68% confidence level and the difference between CTEQ6.6 and MSTW2008 NLO PDF sets. Each uncertainty is varied independently and their effects are added in quadrature. – 20 – JHEP06(2018)022 Electroweak channel Strong channel Number of observed events with pT>100 GeV in high-Emiss Tregions 9 2 Number of expected events with pT>100 GeV in high-Emiss Tregions Hadron+electron background 6.1±0.6 1.78 ±0.32 Muon background 0.15 ±0.09 0.05 ±0.08 Fake background 5.5±3.3 0.1±0.4 Total background 11.8±3.1 1.9±0.4 p00.50 0.47 Observed σ95% vis [fb] 0.22 0.12 Expected σ95% vis [fb] 0.28+0.11 −0.08 0.12+0.07 −0.04 Number of expected signal events with pT>100 GeV in high-Emiss Tregions 13.5±2.1 5.6±0.8 Table 4. Observed events, expected background for null signal, and expected signal yields for two benchmark models: electroweak channel with (m˜χ± 1, τ˜χ± 1) = (400 GeV,0.2 ns) and strong channel with (m˜g, m˜χ± 1, τ˜χ± 1) = (1600 GeV,500 GeV,0.2 ns) in the high-Emiss Tregion. Also shown are the probability of a background-only experiment being more signal-like than observed (p0) and the upper limit on the model-independent visible cross-section at 95% CL. The uncertainty in the total background yield is different from the sum of uncertainties in quadrature due to anti-correlation between different backgrounds. 8 Results and interpretation The tracklet pTspectra are shown in figure 7, along with the results of the fit to the background-only hypothesis. The observed pTdistributions are well described by the background predictions in the low-Emiss Tregions. When fitting to the background+signal hypothesis, no significant excess above the expected SM processes is found at high tracklet pT in high-Emiss Tregions. Model-dependent upper limits on the signal strength are computed using the profile-likelihood ratio [59] as a test statistic and using the asymptotic formula in ref. [59], fitting the pTspectrum in the full range. The confidence levels are computed by following the CLsprescription [60]. Upper limits on the number of signal events are converted into limits on the visible cross-section (σ95% vis ) of signal processes by dividing by the integrated luminosity of the data. Model-independent limits are calculated from the expected and observed event yields in the region where the tracklet pTis above 100 GeV. Table 4lists the observed event yields, expected backgrounds, expected signal yields and model-independent upper limits on the visible signal cross-section in the high-Emiss Tregion. Figure 8shows the model-dependent exclusion limits in the (m˜χ± 1, τ˜χ± 1) plane for the electroweak channel, where τ˜χ± 1is the lifetime of the chargino. A large region is excluded by this analysis while the 8 TeV result [19] has higher sensitivity for long lifetimes due to – 21 – JHEP06(2018)022 5− 10 4− 10 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 Tracklets Fake tracklet Muon HadronElectron Signal Total Background Data ATLAS -1 =13TeV, 36.1 fbs EW production region miss T ELow ) = (400 GeV, 0.20 ns) ± 1 χ ∼ τ, ± 1 χ ∼ m ( 100 1000 10000 [GeV] T pTracklet 0 0.5 1 1.5 2 Data / BG (a) Electroweak channel low-Emiss Tregion. 5− 10 4− 10 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 Tracklets Fake tracklet Muon HadronElectron Signal Total Background Data ATLAS -1 =13TeV, 36.1 fbs Strong production region miss T ELow ) = (1600 GeV, 500 GeV, 0.20 ns) ± 1 χ ∼ τ, ± 1 χ ∼ m, g ~ m( 100 1000 10000 [GeV] T pTracklet 0 0.5 1 1.5 2 Data / BG (b) Strong channel low-Emiss Tregion. 4− 10 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 Tracklets Fake tracklet Muon HadronElectron Signal Total Background Data ATLAS -1 =13TeV, 36.1 fbs EW production region miss T EHigh ) = (400 GeV, 0.20 ns) ± 1 χ ∼ τ, ± 1 χ ∼ m ( 100 1000 10000 [GeV] T pTracklet 0 0.5 1 1.5 2 Data / BG (c) Electroweak channel high-Emiss Tregion. 4− 10 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 Tracklets Fake tracklet Muon HadronElectron Signal Total Background Data ATLAS -1 =13TeV, 36.1 fbs Strong production region miss T EHigh ) = (1600 GeV, 500 GeV, 0.20 ns) ± 1 χ ∼ τ, ± 1 χ ∼ m, g ~ m( 100 1000 10000 [GeV] T pTracklet 0 0.5 1 1.5 2 Data / BG (d) Strong channel high-Emiss Tregion. Figure 7. Pixel-tracklet pTspectrum in various regions: (a) electroweak channel in the lowEmiss Tregion, (b) strong channel in the low-Emiss Tregion, (c) electroweak channel in the high-Emiss T region, and (d) strong channel in the high-Emiss Tregion. Observed data are shown with markers and the background components for the background-only fit are shown with lines. In the strong channel, total background lines overlap hadron and electron background lines. An example of the expected signal spectrum at τ˜χ± 1= 0.2 ns and m˜χ± 1= 400 GeV for the electroweak channel and m˜g= 1600 GeV, m˜χ± 1= 500 GeV for the strong channel is overlaid for comparison. The bottom panels show the ratio of the data to the background predictions. The error band shows the uncertainty in the background prediction including both the statistical and systematic uncertainties. Red arrows in the Data/BG ratio indicate bins where the corresponding entry falls outside the plotted range. the use of longer tracklets. For τ˜χ± 1∼0.2 ns, which corresponds to ∆m˜χ1∼160 MeV in the pure wino LSP model, winos with a mass up to 460 GeV are excluded at 95% CL. Figure 9 – 22 – JHEP06(2018)022 100 200 300 400 500 600 700 [GeV] ± 1 χ ∼ m 0.01 0.02 0.03 0.04 0.1 0.2 0.3 0.4 1 2 3 4 10 [ns] ± 1 χ ∼ τ ) theory σ1 ±Observed 95% CL limit ( ) exp σ1 ±Expected 95% CL limit ( , EW prod. Obs.) -1 ATLAS (8 TeV, 20.3 fb Theory (Phys. Lett. B721 (2013) 252) ALEPH (Phys. Lett. B533 (2002) 223) ATLAS -1 =13TeV, 36.1 fbs > 0µ = 5, βtan production ± 1 χ ∼ ± 1 χ ∼ , 0 1 χ ∼ ± 1 χ ∼ Figure 8. Exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime (τ˜χ± 1) and mass (m˜χ± 1). The yellow band shows the 1σregion of the distribution of the expected limits. The median of the expected limits is shown by a dashed line. The red line shows the observed limit and the orange dotted lines around it show the impact on the observed limit of the variation of the nominal signal cross-section by ±1σof its theoretical uncertainties. Results are compared with the observed limits obtained by the previous ATLAS search with disappearing tracks and tracklets [19] and an example of the limit obtained at LEP2 by the ALEPH experiment [61]. The chargino lifetime as a function of the chargino mass is shown in the almost pure wino LSP scenario at the two-loop level [62]. shows the model-dependent exclusion limits in the m˜g–m˜χ± 1plane for the strong channel. For a chargino lifetime of 0.2 ns, gluino masses up to 1.65 TeV are excluded assuming a chargino mass of 460 GeV, and chargino masses up to 1.05 TeV are excluded assuming very compressed spectra with a mass difference between the gluino and the chargino of less than 200 GeV. Charginos are assumed to decay into a pion and a neutralino in the considered models. However, the results do not depend on this decay mode since the decay products of charginos cannot be detected due to their low momentum. The effects of systematic uncertainties are estimated using the exclusion significance, which is defined as the number of standard deviations corresponding to the signal confidence CLs. Relative changes in the exclusion significance, when nuisance parameters are shifted – 23 – JHEP06(2018)022 800 1000 1200 1400 1600 1800 2000 2200 [GeV] g ~ m 200 400 600 800 1000 1200 1400 1600 [GeV] ± 1 χ ∼ m ) theory σ1 ±Observed 95% CL limit ( ) exp σ1 ±Expected 95% CL limit ( , EW prod. Obs.) -1 ATLAS ( 13TeV, 36.1 fb ± 1 χ ∼ =m g ~ m ATLAS -1 =13TeV, 36.1 fbs = 0.2 ns ± 1 χ ∼ τ )=33% 1 0 χ ∼ qq→g ~ )=67%, B( ± 1 χ ∼ qq→g ~ production, B(g ~ g ~ (a) 800 1000 1200 1400 1600 1800 2000 2200 [GeV] g ~ m 200 400 600 800 1000 1200 1400 1600 1800 [GeV] ± 1 χ ∼ m ) theory σ1 ±Observed 95% CL limit ( ) exp σ1 ±Expected 95% CL limit ( , EW prod. Obs.) -1 ATLAS ( 13TeV, 36.1 fb ± 1 χ ∼ =m g ~ m ATLAS -1 =13TeV, 36.1 fbs = 1.0 ns ± 1 χ ∼ τ )=33% 1 0 χ ∼ qq→g ~ )=67%, B( ± 1 χ ∼ qq→g ~ production, B(g ~ g ~ (b) Figure 9. Exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime of (a) 0.2 ns and (b) 1.0 ns. The yellow band shows the 1σregion of the distribution of the expected limits. The median of the expected limits is shown by a dashed line. The red line shows the observed limit and the orange dotted lines around it show the impact on the observed limit of the variation of the nominal signal cross-section by ±1σof its theoretical uncertainties. Observed limits in the electroweak production search are shown as a green shaded region. Parameter Electroweak channel [%] Strong channel [%] Expected signal events 11 13 αin signal pTresolution function 0.8 1.5 σin signal pTresolution function 5.3 7.2 log rABCD 15 <0.1 αin background pTresolution function 5.0 1.2 σin background pTresolution function 2.2 5.0 p0parameter of the fake-BG pTfunction 2.5<0.1 p1parameter of the fake-BG pTfunction 8.5 0.1 Expected number of muon events 0.5 0.9 Table 5. Effects of systematic uncertainties on the signal exclusion significance at m˜χ± 1= 400 GeV for the electroweak channel and at m˜g= 1600 GeV, m˜χ± 1= 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. Effects of uncertainties on the fake-tracklet background is smaller in the strong channel analysis because the estimated number of the fake-tracklet background events is small. by one standard deviation from their nominal values, are summarised in table 5. When shifting a parameter, the other nuisance parameters are fixed at their nominal values. – 24 – JHEP06(2018)022 The ATLAS collaboration M. Aaboud137d, G. Aad88, B. Abbott115, O. Abdinov12,∗, B. Abeloos119, S.H. Abidi161, O.S. AbouZeid139, N.L. Abraham151, H. Abramowicz155, H. Abreu154, R. Abreu118, Y. Abulaiti148a,148b, B.S. Acharya167a,167b,a, S. Adachi157, L. Adamczyk41a, J. Adelman110, M. Adersberger102, T. Adye133, A.A. Affolder139, T. Agatonovic-Jovin14, C. Agheorghiesei28c, J.A. Aguilar-Saavedra128a,128f, S.P. Ahlen24, F. Ahmadov68,b, G. Aielli135a,135b, S. Akatsuka71, H. Akerstedt148a,148b, T.P.A. ˚ Akesson84, E. Akilli52, A.V. Akimov98, G.L. Alberghi22a,22b, J. Albert172, P. Albicocco50, M.J. Alconada Verzini74, S.C. Alderweireldt108, M. Aleksa32, I.N. Aleksandrov68, C. Alexa28b, G. Alexander155, T. Alexopoulos10, M. Alhroob115, B. Ali130, M. Aliev76a,76b, G. Alimonti94a, J. Alison33, S.P. Alkire38, B.M.M. Allbrooke151, B.W. Allen118, P.P. Allport19, A. Aloisio106a,106b, A. Alonso39, F. Alonso74, C. Alpigiani140, A.A. Alshehri56, M.I. Alstaty88, B. Alvarez Gonzalez32, D. ´ Alvarez Piqueras170, M.G. Alviggi106a,106b, B.T. Amadio16, Y. Amaral Coutinho26a, C. Amelung25, D. Amidei92, S.P. Amor Dos Santos128a,128c, A. Amorim128a,128b, S. Amoroso32, G. Amundsen25, C. Anastopoulos141, L.S. Ancu52, N. Andari19, T. Andeen11, C.F. Anders60b, J.K. Anders77, K.J. Anderson33, A. Andreazza94a,94b, V. Andrei60a, S. Angelidakis9, I. Angelozzi109, A. Angerami38, A.V. Anisenkov111,c, N. Anjos13, A. Annovi126a,126b, C. Antel60a, M. Antonelli50, A. Antonov100,∗, D.J. Antrim166, F. Anulli134a, M. Aoki69, L. Aperio Bella32, G. Arabidze93, Y. Arai69, J.P. Araque128a, V. Araujo Ferraz26a, A.T.H. Arce48, R.E. Ardell80, F.A. Arduh74, J-F. Arguin97, S. Argyropoulos66, M. Arik20a, A.J. Armbruster32, L.J. Armitage79, O. Arnaez161, H. Arnold51, M. Arratia30, O. Arslan23, A. Artamonov99, G. Artoni122, S. Artz86, S. Asai157, N. Asbah45, A. Ashkenazi155, L. Asquith151, K. Assamagan27, R. Astalos146a, M. Atkinson169, N.B. Atlay143, K. Augsten130, G. Avolio32, B. Axen16, M.K. Ayoub119, G. Azuelos97,d, A.E. Baas60a, M.J. Baca19, H. Bachacou138, K. Bachas76a,76b, M. Backes122, M. Backhaus32, P. Bagnaia134a,134b, M. Bahmani42, H. Bahrasemani144, J.T. Baines133, M. Bajic39, O.K. Baker179, E.M. Baldin111,c, P. Balek175, F. Balli138, W.K. Balunas124, E. Banas42, A. Bandyopadhyay23, Sw. Banerjee176,e, A.A.E. Bannoura178, L. Barak32, E.L. Barberio91, D. Barberis53a,53b, M. Barbero88, T. Barillari103, M-S Barisits32, J.T. Barkeloo118, T. Barklow145, N. Barlow30, S.L. Barnes36c, B.M. Barnett133, R.M. Barnett16, Z. Barnovska-Blenessy36a, A. Baroncelli136a, G. Barone25, A.J. Barr122, L. Barranco Navarro170, F. Barreiro85, J. Barreiro Guimar˜aes da Costa35a, R. Bartoldus145, A.E. Barton75, P. Bartos146a, A. Basalaev125, A. Bassalat119,f , R.L. Bates56, S.J. Batista161, J.R. Batley30, M. Battaglia139, M. Bauce134a,134b, F. Bauer138, H.S. Bawa145,g, J.B. Beacham113, M.D. Beattie75, T. Beau83, P.H. Beauchemin165, P. Bechtle23, H.P. Beck18,h, H.C. Beck57, K. Becker122, M. Becker86, M. Beckingham173, C. Becot112, A.J. Beddall20e, A. Beddall20b, V.A. Bednyakov68, M. Bedognetti109, C.P. Bee150, T.A. Beermann32, M. Begalli26a, M. Begel27, J.K. Behr45, A.S. Bell81, G. Bella155, L. Bellagamba22a, A. Bellerive31, M. Bellomo154, K. Belotskiy100, O. Beltramello32, N.L. Belyaev100, O. Benary155,∗, D. Benchekroun137a, M. Bender102, K. Bendtz148a,148b, N. Benekos10, Y. Benhammou155, E. Benhar Noccioli179, J. Benitez66, D.P. Benjamin48, M. Benoit52, J.R. Bensinger25, S. Bentvelsen109, L. Beresford122, M. Beretta50, D. Berge109, E. Bergeaas Kuutmann168, N. Berger5, J. Beringer16, S. Berlendis58, N.R. Bernard89, G. Bernardi83, C. Bernius145, F.U. Bernlochner23, T. Berry80, P. Berta131, C. Bertella35a, G. Bertoli148a,148b, F. Bertolucci126a,126b, I.A. Bertram75, C. Bertsche45, D. Bertsche115, G.J. Besjes39, O. Bessidskaia Bylund148a,148b, M. Bessner45, N. Besson138, C. Betancourt51, A. Bethani87, S. Bethke103, A.J. Bevan79, J. Beyer103, R.M. Bianchi127, O. Biebel102, D. Biedermann17, R. Bielski87, K. Bierwagen86, N.V. Biesuz126a,126b, M. Biglietti136a, T.R.V. Billoud97, H. Bilokon50, M. Bindi57, A. Bingul20b, C. Bini134a,134b, – 31 – JHEP06(2018)022 S. Biondi22a,22b, T. Bisanz57, C. Bittrich47, D.M. Bjergaard48, C.W. Black152, J.E. Black145, K.M. Black24, R.E. Blair6, T. Blazek146a, I. Bloch45, C. Blocker25, A. Blue56, W. Blum86,∗, U. Blumenschein79, S. Blunier34a, G.J. Bobbink109, V.S. Bobrovnikov111,c, S.S. Bocchetta84, A. Bocci48, C. Bock102, M. Boehler51, D. Boerner178, D. Bogavac102, A.G. Bogdanchikov111, C. Bohm148a, V. Boisvert80, P. Bokan168,i, T. Bold41a, A.S. Boldyrev101, A.E. Bolz60b, M. Bomben83, M. Bona79, M. Boonekamp138, A. Borisov132, G. Borissov75, J. Bortfeldt32, D. Bortoletto122, V. Bortolotto62a, D. Boscherini22a, M. Bosman13, J.D. Bossio Sola29, J. Boudreau127, J. Bouffard2, E.V. Bouhova-Thacker75, D. Boumediene37, C. Bourdarios119, S.K. Boutle56, A. Boveia113, J. Boyd32, I.R. Boyko68, J. Bracinik19, A. Brandt8, G. Brandt57, O. Brandt60a, U. Bratzler158, B. Brau89, J.E. Brau118, W.D. Breaden Madden56, K. Brendlinger45, A.J. Brennan91, L. Brenner109, R. Brenner168, S. Bressler175, D.L. Briglin19, T.M. Bristow49, D. Britton56, D. Britzger45, F.M. Brochu30, I. Brock23, R. Brock93, G. Brooijmans38, T. Brooks80, W.K. Brooks34b, J. Brosamer16, E. Brost110, J.H Broughton19, P.A. Bruckman de Renstrom42, D. Bruncko146b, A. Bruni22a, G. Bruni22a, L.S. Bruni109, BH Brunt30, M. Bruschi22a, N. Bruscino23, P. Bryant33, L. Bryngemark45, T. Buanes15, Q. Buat144, P. Buchholz143, A.G. Buckley56, I.A. Budagov68, F. Buehrer51, M.K. Bugge121, O. Bulekov100, D. Bullock8, T.J. Burch110, S. Burdin77, C.D. Burgard51, A.M. Burger5, B. Burghgrave110, K. Burka42, S. Burke133, I. Burmeister46, J.T.P. Burr122, E. Busato37, D. B¨uscher51, V. B¨uscher86, P. Bussey56, J.M. Butler24, C.M. Buttar56, J.M. Butterworth81, P. Butti32, W. Buttinger27, A. Buzatu35c, A.R. Buzykaev111,c, S. Cabrera Urb´an170, D. Caforio130, V.M. Cairo40a,40b, O. Cakir4a, N. Calace52, P. Calafiura16, A. Calandri88, G. Calderini83, P. Calfayan64, G. Callea40a,40b, L.P. Caloba26a, S. Calvente Lopez85, D. Calvet37, S. Calvet37, T.P. Calvet88, R. Camacho Toro33, S. Camarda32, P. Camarri135a,135b, D. Cameron121, R. Caminal Armadans169, C. Camincher58, S. Campana32, M. Campanelli81, A. Camplani94a,94b, A. Campoverde143, V. Canale106a,106b, M. Cano Bret36c, J. Cantero116, T. Cao155, M.D.M. Capeans Garrido32, I. Caprini28b, M. Caprini28b, M. Capua40a,40b, R.M. Carbone38, R. Cardarelli135a, F. Cardillo51, I. Carli131, T. Carli32, G. Carlino106a, B.T. Carlson127, L. Carminati94a,94b, R.M.D. Carney148a,148b, S. Caron108, E. Carquin34b, S. Carr´a94a,94b, G.D. Carrillo-Montoya32, J. Carvalho128a,128c, D. Casadei19, M.P. Casado13,j, M. Casolino13, D.W. Casper166, R. Castelijn109, V. Castillo Gimenez170, N.F. Castro128a,k, A. Catinaccio32, J.R. Catmore121, A. Cattai32, J. Caudron23, V. Cavaliere169, E. Cavallaro13, D. Cavalli94a, M. Cavalli-Sforza13, V. Cavasinni126a,126b, E. Celebi20d, F. Ceradini136a,136b, L. Cerda Alberich170, A.S. Cerqueira26b, A. Cerri151, L. Cerrito135a,135b, F. Cerutti16, A. Cervelli18, S.A. Cetin20d, A. Chafaq137a, D. Chakraborty110, S.K. Chan59, W.S. Chan109, Y.L. Chan62a, P. Chang169, J.D. Chapman30, D.G. Charlton19, C.C. Chau161, C.A. Chavez Barajas151, S. Che113, S. Cheatham167a,167c, A. Chegwidden93, S. Chekanov6, S.V. Chekulaev163a, G.A. Chelkov68,l, M.A. Chelstowska32, C. Chen67, H. Chen27, J. Chen36a, S. Chen35b, S. Chen157, X. Chen35c,m, Y. Chen70, H.C. Cheng92, H.J. Cheng35a,35d, A. Cheplakov68, E. Cheremushkina132, R. Cherkaoui El Moursli137e, E. Cheu7, K. Cheung63, L. Chevalier138, V. Chiarella50, G. Chiarelli126a,126b, G. Chiodini76a, A.S. Chisholm32, A. Chitan28b, Y.H. Chiu172, M.V. Chizhov68, K. Choi64, A.R. Chomont37, S. Chouridou156, V. Christodoulou81, D. Chromek-Burckhart32, M.C. Chu62a, J. Chudoba129, A.J. Chuinard90, J.J. Chwastowski42, L. Chytka117, A.K. Ciftci4a, D. Cinca46, V. Cindro78, I.A. Cioara23, C. Ciocca22a,22b, A. Ciocio16, F. Cirotto106a,106b, Z.H. Citron175, M. Citterio94a, M. Ciubancan28b, A. Clark52, B.L. Clark59, M.R. Clark38, P.J. Clark49, R.N. Clarke16, C. Clement148a,148b, Y. Coadou88, M. Cobal167a,167c, A. Coccaro52, J. Cochran67, L. Colasurdo108, B. Cole38, A.P. Colijn109, J. Collot58, T. Colombo166, P. Conde Mui˜no128a,128b, E. Coniavitis51, S.H. Connell147b, I.A. Connelly87, S. Constantinescu28b, G. Conti32, – 32 – JHEP06(2018)022 F. Conventi106a,n, M. Cooke16, A.M. Cooper-Sarkar122, F. Cormier171, K.J.R. Cormier161, M. Corradi134a,134b, F. Corriveau90,o, A. Cortes-Gonzalez32, G. Cortiana103, G. Costa94a, M.J. Costa170, D. Costanzo141, G. Cottin30, G. Cowan80, B.E. Cox87, K. Cranmer112, S.J. Crawley56, R.A. Creager124, G. Cree31, S. Cr´ep´e-Renaudin58, F. Crescioli83, W.A. Cribbs148a,148b, M. Cristinziani23, V. Croft108, G. Crosetti40a,40b, A. Cueto85, T. Cuhadar Donszelmann141, A.R. Cukierman145, J. Cummings179, M. Curatolo50, J. C´uth86, P. Czodrowski32, G. D’amen22a,22b, S. D’Auria56, L. D’eramo83, M. D’Onofrio77, M.J. Da Cunha Sargedas De Sousa128a,128b, C. Da Via87, W. Dabrowski41a, T. Dado146a, T. Dai92, O. Dale15, F. Dallaire97, C. Dallapiccola89, M. Dam39, J.R. Dandoy124, M.F. Daneri29, N.P. Dang176, A.C. Daniells19, N.S. Dann87, M. Danninger171, M. Dano Hoffmann138, V. Dao150, G. Darbo53a, S. Darmora8, J. Dassoulas3, A. Dattagupta118, T. Daubney45, W. Davey23, C. David45, T. Davidek131, D.R. Davis48, P. Davison81, E. Dawe91, I. Dawson141, K. De8, R. de Asmundis106a, A. De Benedetti115, S. De Castro22a,22b, S. De Cecco83, N. De Groot108, P. de Jong109, H. De la Torre93, F. De Lorenzi67, A. De Maria57, D. De Pedis134a, A. De Salvo134a, U. De Sanctis135a,135b, A. De Santo151, K. De Vasconcelos Corga88, J.B. De Vivie De Regie119, W.J. Dearnaley75, R. Debbe27, C. Debenedetti139, D.V. Dedovich68, N. Dehghanian3, I. Deigaard109, M. Del Gaudio40a,40b, J. Del Peso85, D. Delgove119, F. Deliot138, C.M. Delitzsch52, A. Dell’Acqua32, L. Dell’Asta24, M. Dell’Orso126a,126b, M. Della Pietra106a,106b, D. della Volpe52, M. Delmastro5, C. Delporte119, P.A. Delsart58, D.A. DeMarco161, S. Demers179, M. Demichev68, A. Demilly83, S.P. Denisov132, D. Denysiuk138, D. Derendarz42, J.E. Derkaoui137d, F. Derue83, P. Dervan77, K. Desch23, C. Deterre45, K. Dette46, M.R. Devesa29, P.O. Deviveiros32, A. Dewhurst133, S. Dhaliwal25, F.A. Di Bello52, A. Di Ciaccio135a,135b, L. Di Ciaccio5, W.K. Di Clemente124, C. Di Donato106a,106b, A. Di Girolamo32, B. Di Girolamo32, B. Di Micco136a,136b, R. Di Nardo32, K.F. Di Petrillo59, A. Di Simone51, R. Di Sipio161, D. Di Valentino31, C. Diaconu88, M. Diamond161, F.A. Dias39, M.A. Diaz34a, E.B. Diehl92, J. Dietrich17, S. D´ıez Cornell45, A. Dimitrievska14, J. Dingfelder23, P. Dita28b, S. Dita28b, F. Dittus32, F. Djama88, T. Djobava54b, J.I. Djuvsland60a, M.A.B. do Vale26c, D. Dobos32, M. Dobre28b, C. Doglioni84, J. Dolejsi131, Z. Dolezal131, M. Donadelli26d, S. Donati126a,126b, P. Dondero123a,123b, J. Donini37, J. Dopke133, A. Doria106a, M.T. Dova74, A.T. Doyle56, E. Drechsler57, M. Dris10, Y. Du36b, J. Duarte-Campderros155, A. Dubreuil52, E. Duchovni175, G. Duckeck102, A. Ducourthial83, O.A. Ducu97,p, D. Duda109, A. Dudarev32, A.Chr. Dudder86, E.M. Duffield16, L. Duflot119, M. D¨uhrssen32, M. Dumancic175, A.E. Dumitriu28b, A.K. Duncan56, M. Dunford60a, H. Duran Yildiz4a, M. D¨uren55, A. Durglishvili54b, D. Duschinger47, B. Dutta45, D. Duvnjak1, M. Dyndal45, B.S. Dziedzic42, C. Eckardt45, K.M. Ecker103, R.C. Edgar92, T. Eifert32, G. Eigen15, K. Einsweiler16, T. Ekelof168, M. El Kacimi137c, R. El Kosseifi88, V. Ellajosyula88, M. Ellert168, S. Elles5, F. Ellinghaus178, A.A. Elliot172, N. Ellis32, J. Elmsheuser27, M. Elsing32, D. Emeliyanov133, Y. Enari157, O.C. Endner86, J.S. Ennis173, J. Erdmann46, A. Ereditato18, M. Ernst27, S. Errede169, M. Escalier119, C. Escobar170, B. Esposito50, O. Estrada Pastor170, A.I. Etienvre138, E. Etzion155, H. Evans64, A. Ezhilov125, M. Ezzi137e, F. Fabbri22a,22b, L. Fabbri22a,22b, V. Fabiani108, G. Facini81, R.M. Fakhrutdinov132, S. Falciano134a, R.J. Falla81, J. Faltova32, Y. Fang35a, M. Fanti94a,94b, A. Farbin8, A. Farilla136a, C. Farina127, E.M. Farina123a,123b, T. Farooque93, S. Farrell16, S.M. Farrington173, P. Farthouat32, F. Fassi137e, P. Fassnacht32, D. Fassouliotis9, M. Faucci Giannelli80, A. Favareto53a,53b, W.J. Fawcett122, L. Fayard119, O.L. Fedin125,q, W. Fedorko171, S. Feigl121, L. Feligioni88, C. Feng36b, E.J. Feng32, H. Feng92, M.J. Fenton56, A.B. Fenyuk132, L. Feremenga8, P. Fernandez Martinez170, S. Fernandez Perez13, J. Ferrando45, A. Ferrari168, P. Ferrari109, R. Ferrari123a, D.E. Ferreira de Lima60b, A. Ferrer170, D. Ferrere52, C. Ferretti92, F. Fiedler86, – 33 – JHEP06(2018)022 A. Filipˇciˇc78, M. Filipuzzi45, F. Filthaut108, M. Fincke-Keeler172, K.D. Finelli152, M.C.N. Fiolhais128a,128c,r, L. Fiorini170, A. Fischer2, C. Fischer13, J. Fischer178, W.C. Fisher93, N. Flaschel45, I. Fleck143, P. Fleischmann92, R.R.M. Fletcher124, T. Flick178, B.M. Flierl102, L.R. Flores Castillo62a, M.J. Flowerdew103, G.T. Forcolin87, A. Formica138, F.A. F¨orster13, A. Forti87, A.G. Foster19, D. Fournier119, H. Fox75, S. Fracchia141, P. Francavilla83, M. Franchini22a,22b, S. Franchino60a, D. Francis32, L. Franconi121, M. Franklin59, M. Frate166, M. Fraternali123a,123b, D. Freeborn81, S.M. Fressard-Batraneanu32, B. Freund97, D. Froidevaux32, J.A. Frost122, C. Fukunaga158, T. Fusayasu104, J. Fuster170, C. Gabaldon58, O. Gabizon154, A. Gabrielli22a,22b, A. Gabrielli16, G.P. Gach41a, S. Gadatsch32, S. Gadomski80, G. Gagliardi53a,53b, L.G. Gagnon97, C. Galea108, B. Galhardo128a,128c, E.J. Gallas122, B.J. Gallop133, P. Gallus130, G. Galster39, K.K. Gan113, S. Ganguly37, Y. Gao77, Y.S. Gao145,g, F.M. Garay Walls49, C. Garc´ıa170, J.E. Garc´ıa Navarro170, J.A. Garc´ıa Pascual35a, M. Garcia-Sciveres16, R.W. Gardner33, N. Garelli145, V. Garonne121, A. Gascon Bravo45, K. Gasnikova45, C. Gatti50, A. Gaudiello53a,53b, G. Gaudio123a, I.L. Gavrilenko98, C. Gay171, G. Gaycken23, E.N. Gazis10, C.N.P. Gee133, J. Geisen57, M. Geisen86, M.P. Geisler60a, K. Gellerstedt148a,148b, C. Gemme53a, M.H. Genest58, C. Geng92, S. Gentile134a,134b, C. Gentsos156, S. George80, D. Gerbaudo13, A. Gershon155, G. Geßner46, S. Ghasemi143, M. Ghneimat23, B. Giacobbe22a, S. Giagu134a,134b, N. Giangiacomi22a,22b, P. Giannetti126a,126b, S.M. Gibson80, M. Gignac171, M. Gilchriese16, D. Gillberg31, G. Gilles178, D.M. Gingrich3,d, N. Giokaris9,∗, M.P. Giordani167a,167c, F.M. Giorgi22a, P.F. Giraud138, P. Giromini59, D. Giugni94a, F. Giuli122, C. Giuliani103, M. Giulini60b, B.K. Gjelsten121, S. Gkaitatzis156, I. Gkialas9,s, E.L. Gkougkousis139, P. Gkountoumis10, L.K. Gladilin101, C. Glasman85, J. Glatzer13, P.C.F. Glaysher45, A. Glazov45, M. Goblirsch-Kolb25, J. Godlewski42, S. Goldfarb91, T. Golling52, D. Golubkov132, A. Gomes128a,128b,128d, R. Gon¸calo128a, R. Goncalves Gama26a, J. Goncalves Pinto Firmino Da Costa138, G. Gonella51, L. Gonella19, A. Gongadze68, S. Gonz´alez de la Hoz170, S. Gonzalez-Sevilla52, L. Goossens32, P.A. Gorbounov99, H.A. Gordon27, I. Gorelov107, B. Gorini32, E. Gorini76a,76b, A. Goriˇsek78, A.T. Goshaw48, C. G¨ossling46, M.I. Gostkin68, C.A. Gottardo23, C.R. Goudet119, D. Goujdami137c, A.G. Goussiou140, N. Govender147b,t, E. Gozani154, L. Graber57, I. Grabowska-Bold41a, P.O.J. Gradin168, J. Gramling166, E. Gramstad121, S. Grancagnolo17, V. Gratchev125, P.M. Gravila28f, C. Gray56, H.M. Gray16, Z.D. Greenwood82,u, C. Grefe23, K. Gregersen81, I.M. Gregor45, P. Grenier145, K. Grevtsov5, J. Griffiths8, A.A. Grillo139, K. Grimm75, S. Grinstein13,v, Ph. Gris37, J.-F. Grivaz119, S. Groh86, E. Gross175, J. Grosse-Knetter57, G.C. Grossi82, Z.J. Grout81, A. Grummer107, L. Guan92, W. Guan176, J. Guenther65, F. Guescini163a, D. Guest166, O. Gueta155, B. Gui113, E. Guido53a,53b, T. Guillemin5, S. Guindon2, U. Gul56, C. Gumpert32, J. Guo36c, W. Guo92, Y. Guo36a,w, R. Gupta43, S. Gupta122, G. Gustavino134a,134b, P. Gutierrez115, N.G. Gutierrez Ortiz81, C. Gutschow81, C. Guyot138, M.P. Guzik41a, C. Gwenlan122, C.B. Gwilliam77, A. Haas112, C. Haber16, H.K. Hadavand8, N. Haddad137e, A. Hadef88, S. Hageb¨ock23, M. Hagihara164, H. Hakobyan180,∗, M. Haleem45, J. Haley116, G. Halladjian93, G.D. Hallewell88, K. Hamacher178, P. Hamal117, K. Hamano172, A. Hamilton147a, G.N. Hamity141, P.G. Hamnett45, L. Han36a, S. Han35a,35d, K. Hanagaki69,x, K. Hanawa157, M. Hance139, B. Haney124, P. Hanke60a, J.B. Hansen39, J.D. Hansen39, M.C. Hansen23, P.H. Hansen39, K. Hara164, A.S. Hard176, T. Harenberg178, F. Hariri119, S. Harkusha95, R.D. Harrington49, P.F. Harrison173, N.M. Hartmann102, M. Hasegawa70, Y. Hasegawa142, A. Hasib49, S. Hassani138, S. Haug18, R. Hauser93, L. Hauswald47, L.B. Havener38, M. Havranek130, C.M. Hawkes19, R.J. Hawkings32, D. Hayakawa159, D. Hayden93, C.P. Hays122, J.M. Hays79, H.S. Hayward77, S.J. Haywood133, S.J. Head19, T. Heck86, V. Hedberg84, L. Heelan8, S. Heer23, K.K. Heidegger51, S. Heim45, – 34 – JHEP06(2018)022 T. Heim16, B. Heinemann45,y, J.J. Heinrich102, L. Heinrich112, C. Heinz55, J. Hejbal129, L. Helary32, A. Held171, S. Hellman148a,148b, C. Helsens32, R.C.W. Henderson75, Y. Heng176, S. Henkelmann171, A.M. Henriques Correia32, S. Henrot-Versille119, G.H. Herbert17, H. Herde25, V. Herget177, Y. Hern´andez Jim´enez147c, H. Herr86, G. Herten51, R. Hertenberger102, L. Hervas32, T.C. Herwig124, G.G. Hesketh81, N.P. Hessey163a, J.W. Hetherly43, S. Higashino69, E. Hig´on-Rodriguez170, K. Hildebrand33, E. Hill172, J.C. Hill30, K.H. Hiller45, S.J. Hillier19, M. Hils47, I. Hinchliffe16, M. Hirose51, D. Hirschbuehl178, B. Hiti78, O. Hladik129, X. Hoad49, J. Hobbs150, N. Hod163a, M.C. Hodgkinson141, P. Hodgson141, A. Hoecker32, M.R. Hoeferkamp107, F. Hoenig102, D. Hohn23, T.R. Holmes33, M. Homann46, S. Honda164, T. Honda69, T.M. Hong127, B.H. Hooberman169, W.H. Hopkins118, Y. Horii105, A.J. Horton144, J-Y. Hostachy58, S. Hou153, A. Hoummada137a, J. Howarth87, J. Hoya74, M. Hrabovsky117, J. Hrdinka32, I. Hristova17, J. Hrivnac119, T. Hryn’ova5, A. Hrynevich96, P.J. Hsu63, S.-C. Hsu140, Q. Hu36a, S. Hu36c, Y. Huang35a, Z. Hubacek130, F. Hubaut88, F. Huegging23, T.B. Huffman122, E.W. Hughes38, G. Hughes75, M. Huhtinen32, P. Huo150, N. Huseynov68,b, J. Huston93, J. Huth59, G. Iacobucci52, G. Iakovidis27, I. Ibragimov143, L. Iconomidou-Fayard119, Z. Idrissi137e, P. Iengo32, O. Igonkina109,z, T. Iizawa174, Y. Ikegami69, M. Ikeno69, Y. Ilchenko11,aa, D. Iliadis156, N. Ilic145, G. Introzzi123a,123b, P. Ioannou9,∗, M. Iodice136a, K. Iordanidou38, V. Ippolito59, M.F. Isacson168, N. Ishijima120, M. Ishino157, M. Ishitsuka159, C. Issever122, S. Istin20a, F. Ito164, J.M. Iturbe Ponce62a, R. Iuppa162a,162b, H. Iwasaki69, J.M. Izen44, V. Izzo106a, S. Jabbar3, P. Jackson1, R.M. Jacobs23, V. Jain2, K.B. Jakobi86, K. Jakobs51, S. Jakobsen65, T. Jakoubek129, D.O. Jamin116, D.K. Jana82, R. Jansky52, J. Janssen23, M. Janus57, P.A. Janus41a, G. Jarlskog84, N. Javadov68,b, T. Jav˚urek51, M. Javurkova51, F. Jeanneau138, L. Jeanty16, J. Jejelava54a,ab, A. Jelinskas173, P. Jenni51,ac, C. Jeske173, S. J´ez´equel5, H. Ji176, J. Jia150, H. Jiang67, Y. Jiang36a, Z. Jiang145, S. Jiggins81, J. Jimenez Pena170, S. Jin35a, A. Jinaru28b, O. Jinnouchi159, H. Jivan147c, P. Johansson141, K.A. Johns7, C.A. Johnson64, W.J. Johnson140, K. Jon-And148a,148b, R.W.L. Jones75, S.D. Jones151, S. Jones7, T.J. Jones77, J. Jongmanns60a, P.M. Jorge128a,128b, J. Jovicevic163a, X. Ju176, A. Juste Rozas13,v, M.K. K¨ohler175, A. Kaczmarska42, M. Kado119, H. Kagan113, M. Kagan145, S.J. Kahn88, T. Kaji174, E. Kajomovitz48, C.W. Kalderon84, A. Kaluza86, S. Kama43, A. Kamenshchikov132, N. Kanaya157, L. Kanjir78, V.A. Kantserov100, J. Kanzaki69, B. Kaplan112, L.S. Kaplan176, D. Kar147c, K. Karakostas10, N. Karastathis10, M.J. Kareem57, E. Karentzos10, S.N. Karpov68, Z.M. Karpova68, K. Karthik112, V. Kartvelishvili75, A.N. Karyukhin132, K. Kasahara164, L. Kashif176, R.D. Kass113, A. Kastanas149, Y. Kataoka157, C. Kato157, A. Katre52, J. Katzy45, K. Kawade70, K. Kawagoe73, T. Kawamoto157, G. Kawamura57, E.F. Kay77, V.F. Kazanin111,c, R. Keeler172, R. Kehoe43, J.S. Keller31, J.J. Kempster80, J Kendrick19, H. Keoshkerian161, O. Kepka129, B.P. Kerˇsevan78, S. Kersten178, R.A. Keyes90, M. Khader169, F. Khalil-zada12, A. Khanov116, A.G. Kharlamov111,c, T. Kharlamova111,c, A. Khodinov160, T.J. Khoo52, V. Khovanskiy99,∗, E. Khramov68, J. Khubua54b,ad, S. Kido70, C.R. Kilby80, H.Y. Kim8, S.H. Kim164, Y.K. Kim33, N. Kimura156, O.M. Kind17, B.T. King77, D. Kirchmeier47, J. Kirk133, A.E. Kiryunin103, T. Kishimoto157, D. Kisielewska41a, V. Kitali45, K. Kiuchi164, O. Kivernyk5, E. Kladiva146b, T. Klapdor-Kleingrothaus51, M.H. Klein92, M. Klein77, U. Klein77, K. Kleinknecht86, P. Klimek110, A. Klimentov27, R. Klingenberg46, T. Klingl23, T. Klioutchnikova32, E.-E. Kluge60a, P. Kluit109, S. Kluth103, E. Kneringer65, E.B.F.G. Knoops88, A. Knue103, A. Kobayashi157, D. Kobayashi159, T. Kobayashi157, M. Kobel47, M. Kocian145, P. Kodys131, T. Koffas31, E. Koffeman109, N.M. K¨ohler103, T. Koi145, M. Kolb60b, I. Koletsou5, A.A. Komar98,∗, Y. Komori157, T. Kondo69, N. Kondrashova36c, K. K¨oneke51, A.C. K¨onig108, T. Kono69,ae, R. Konoplich112,af , N. Konstantinidis81, R. Kopeliansky64, S. Koperny41a, – 35 – JHEP06(2018)022 A.K. Kopp51, K. Korcyl42, K. Kordas156, A. Korn81, A.A. Korol111,c, I. Korolkov13, E.V. Korolkova141, O. Kortner103, S. Kortner103, T. Kosek131, V.V. Kostyukhin23, A. Kotwal48, A. Koulouris10, A. Kourkoumeli-Charalampidi123a,123b, C. Kourkoumelis9, E. Kourlitis141, V. Kouskoura27, A.B. Kowalewska42, R. Kowalewski172, T.Z. Kowalski41a, C. Kozakai157, W. Kozanecki138, A.S. Kozhin132, V.A. Kramarenko101, G. Kramberger78, D. Krasnopevtsev100, M.W. Krasny83, A. Krasznahorkay32, D. Krauss103, J.A. Kremer41a, J. Kretzschmar77, K. Kreutzfeldt55, P. Krieger161, K. Krizka33, K. Kroeninger46, H. Kroha103, J. Kroll129, J. Kroll124, J. Kroseberg23, J. Krstic14, U. Kruchonak68, H. Kr¨uger23, N. Krumnack67, M.C. Kruse48, T. Kubota91, H. Kucuk81, S. Kuday4b, J.T. Kuechler178, S. Kuehn32, A. Kugel60a, F. Kuger177, T. Kuhl45, V. Kukhtin68, R. Kukla88, Y. Kulchitsky95, S. Kuleshov34b, Y.P. Kulinich169, M. Kuna134a,134b, T. Kunigo71, A. Kupco129, T. Kupfer46, O. Kuprash155, H. Kurashige70, L.L. Kurchaninov163a, Y.A. Kurochkin95, M.G. Kurth35a,35d, V. Kus129, E.S. Kuwertz172, M. Kuze159, J. Kvita117, T. Kwan172, D. Kyriazopoulos141, A. La Rosa103, J.L. La Rosa Navarro26d, L. La Rotonda40a,40b, F. La Ruffa40a,40b, C. Lacasta170, F. Lacava134a,134b, J. Lacey45, H. Lacker17, D. Lacour83, E. Ladygin68, R. Lafaye5, B. Laforge83, T. Lagouri179, S. Lai57, S. Lammers64, W. Lampl7, E. Lan¸con27, U. Landgraf51, M.P.J. Landon79, M.C. Lanfermann52, V.S. Lang60a, J.C. Lange13, R.J. Langenberg32, A.J. Lankford166, F. Lanni27, K. Lantzsch23, A. Lanza123a, A. Lapertosa53a,53b, S. Laplace83, J.F. Laporte138, T. Lari94a, F. Lasagni Manghi22a,22b, M. Lassnig32, P. Laurelli50, W. Lavrijsen16, A.T. Law139, P. Laycock77, T. Lazovich59, M. Lazzaroni94a,94b, B. Le91, O. Le Dortz83, E. Le Guirriec88, E.P. Le Quilleuc138, M. LeBlanc172, T. LeCompte6, F. Ledroit-Guillon58, C.A. Lee27, G.R. Lee133,ag, S.C. Lee153, L. Lee59, B. Lefebvre90, G. Lefebvre83, M. Lefebvre172, F. Legger102, C. Leggett16, G. Lehmann Miotto32, X. Lei7, W.A. Leight45, M.A.L. Leite26d, R. Leitner131, D. Lellouch175, B. Lemmer57, K.J.C. Leney81, T. Lenz23, B. Lenzi32, R. Leone7, S. Leone126a,126b, C. Leonidopoulos49, G. Lerner151, C. Leroy97, A.A.J. Lesage138, C.G. Lester30, M. Levchenko125, J. Levˆeque5, D. Levin92, L.J. Levinson175, M. Levy19, D. Lewis79, B. Li36a,w, C.-Q. Li36a, H. Li150, L. Li36c, Q. Li35a,35d, S. Li48, X. Li36c, Y. Li143, Z. Liang35a, B. Liberti135a, A. Liblong161, K. Lie62c, J. Liebal23, W. Liebig15, A. Limosani152, S.C. Lin182, T.H. Lin86, R.A. Linck64, B.E. Lindquist150, A.E. Lionti52, E. Lipeles124, A. Lipniacka15, M. Lisovyi60b, T.M. Liss169,ah, A. Lister171, A.M. Litke139, B. Liu153,ai, H. Liu92, H. Liu27, J.K.K. Liu122, J. Liu36b, J.B. Liu36a, K. Liu88, L. Liu169, M. Liu36a, Y.L. Liu36a, Y. Liu36a, M. Livan123a,123b, A. Lleres58, J. Llorente Merino35a, S.L. Lloyd79, C.Y. Lo62b, F. Lo Sterzo153, E.M. Lobodzinska45, P. Loch7, F.K. Loebinger87, A. Loesle51, K.M. Loew25, A. Loginov179,∗, T. Lohse17, K. Lohwasser141, M. Lokajicek129, B.A. Long24, J.D. Long169, R.E. Long75, L. Longo76a,76b, K.A. Looper113, J.A. Lopez34b, D. Lopez Mateos59, I. Lopez Paz13, A. Lopez Solis83, J. Lorenz102, N. Lorenzo Martinez5, M. Losada21, P.J. L¨osel102, X. Lou35a, A. Lounis119, J. Love6, P.A. Love75, H. Lu62a, N. Lu92, Y.J. Lu63, H.J. Lubatti140, C. Luci134a,134b, A. Lucotte58, C. Luedtke51, F. Luehring64, W. Lukas65, L. Luminari134a, O. Lundberg148a,148b, B. Lund-Jensen149, M.S. Lutz89, P.M. Luzi83, D. Lynn27, R. Lysak129, E. Lytken84, F. Lyu35a, V. Lyubushkin68, H. Ma27, L.L. Ma36b, Y. Ma36b, G. Maccarrone50, A. Macchiolo103, C.M. Macdonald141, B. Maˇcek78, J. Machado Miguens124,128b, D. Madaffari170, R. Madar37, W.F. Mader47, A. Madsen45, J. Maeda70, S. Maeland15, T. Maeno27, A.S. Maevskiy101, V. Magerl51, J. Mahlstedt109, C. Maiani119, C. Maidantchik26a, A.A. Maier103, T. Maier102, A. Maio128a,128b,128d, O. Majersky146a, S. Majewski118, Y. Makida69, N. Makovec119, B. Malaescu83, Pa. Malecki42, V.P. Maleev125, F. Malek58, U. Mallik66, D. Malon6, C. Malone30, S. Maltezos10, S. Malyukov32, J. Mamuzic170, G. Mancini50, I. Mandi´c78, J. Maneira128a,128b, L. Manhaes de Andrade Filho26b, J. Manjarres Ramos47, K.H. Mankinen84, A. Mann102, A. Manousos32, B. Mansoulie138, J.D. Mansour35a, R. Mantifel90, – 36 – JHEP06(2018)022 M. Mantoani57, S. Manzoni94a,94b, L. Mapelli32, G. Marceca29, L. March52, L. Marchese122, G. Marchiori83, M. Marcisovsky129, M. Marjanovic37, D.E. Marley92, F. Marroquim26a, S.P. Marsden87, Z. Marshall16, M.U.F Martensson168, S. Marti-Garcia170, C.B. Martin113, T.A. Martin173, V.J. Martin49, B. Martin dit Latour15, M. Martinez13,v, V.I. Martinez Outschoorn169, S. Martin-Haugh133, V.S. Martoiu28b, A.C. Martyniuk81, A. Marzin32, L. Masetti86, T. Mashimo157, R. Mashinistov98, J. Masik87, A.L. Maslennikov111,c, L. Massa135a,135b, P. Mastrandrea5, A. Mastroberardino40a,40b, T. Masubuchi157, P. M¨attig178, J. Maurer28b, S.J. Maxfield77, D.A. Maximov111,c, R. Mazini153, I. Maznas156, S.M. Mazza94a,94b, N.C. Mc Fadden107, G. Mc Goldrick161, S.P. Mc Kee92, A. McCarn92, R.L. McCarthy150, T.G. McCarthy103, L.I. McClymont81, E.F. McDonald91, J.A. Mcfayden81, G. Mchedlidze57, S.J. McMahon133, P.C. McNamara91, R.A. McPherson172,o, S. Meehan140, T.J. Megy51, S. Mehlhase102, A. Mehta77, T. Meideck58, K. Meier60a, B. Meirose44, D. Melini170,aj, B.R. Mellado Garcia147c, J.D. Mellenthin57, M. Melo146a, F. Meloni18, A. Melzer23, S.B. Menary87, L. Meng77, X.T. Meng92, A. Mengarelli22a,22b, S. Menke103, E. Meoni40a,40b, S. Mergelmeyer17, P. Mermod52, L. Merola106a,106b, C. Meroni94a, F.S. Merritt33, A. Messina134a,134b, J. Metcalfe6, A.S. Mete166, C. Meyer124, J-P. Meyer138, J. Meyer109, H. Meyer Zu Theenhausen60a, F. Miano151, R.P. Middleton133, S. Miglioranzi53a,53b, L. Mijovi´c49, G. Mikenberg175, M. Mikestikova129, M. Mikuˇz78, M. Milesi91, A. Milic161, D.W. Miller33, C. Mills49, A. Milov175, D.A. Milstead148a,148b, A.A. Minaenko132, Y. Minami157, I.A. Minashvili54b, A.I. Mincer112, B. Mindur41a, M. Mineev68, Y. Minegishi157, Y. Ming176, L.M. Mir13, K.P. Mistry124, T. Mitani174, J. Mitrevski102, V.A. Mitsou170, A. Miucci18, P.S. Miyagawa141, A. Mizukami69, J.U. Mj¨ornmark84, T. Mkrtchyan180, M. Mlynarikova131, T. Moa148a,148b, K. Mochizuki97, P. Mogg51, S. Mohapatra38, S. Molander148a,148b, R. Moles-Valls23, R. Monden71, M.C. Mondragon93, K. M¨onig45, J. Monk39, E. Monnier88, A. Montalbano150, J. Montejo Berlingen32, F. Monticelli74, S. Monzani94a,94b, R.W. Moore3, N. Morange119, D. Moreno21, M. Moreno Ll´acer32, P. Morettini53a, S. Morgenstern32, D. Mori144, T. Mori157, M. Morii59, M. Morinaga157, V. Morisbak121, A.K. Morley32, G. Mornacchi32, J.D. Morris79, L. Morvaj150, P. Moschovakos10, M. Mosidze54b, H.J. Moss141, J. Moss145,ak, K. Motohashi159, R. Mount145, E. Mountricha27, E.J.W. Moyse89, S. Muanza88, F. Mueller103, J. Mueller127, R.S.P. Mueller102, D. Muenstermann75, P. Mullen56, G.A. Mullier18, F.J. Munoz Sanchez87, W.J. Murray173,133, H. Musheghyan32, M. Muˇskinja78, A.G. Myagkov132,al, M. Myska130, B.P. Nachman16, O. Nackenhorst52, K. Nagai122, R. Nagai69,ae, K. Nagano69, Y. Nagasaka61, K. Nagata164, M. Nagel51, E. Nagy88, A.M. Nairz32, Y. Nakahama105, K. Nakamura69, T. Nakamura157, I. Nakano114, R.F. Naranjo Garcia45, R. Narayan11, D.I. Narrias Villar60a, I. Naryshkin125, T. Naumann45, G. Navarro21, R. Nayyar7, H.A. Neal92, P.Yu. Nechaeva98, T.J. Neep138, A. Negri123a,123b, M. Negrini22a, S. Nektarijevic108, C. Nellist119, A. Nelson166, M.E. Nelson122, S. Nemecek129, P. Nemethy112, M. Nessi32,am, M.S. Neubauer169, M. Neumann178, P.R. Newman19, T.Y. Ng62c, T. Nguyen Manh97, R.B. Nickerson122, R. Nicolaidou138, J. Nielsen139, V. Nikolaenko132,al, I. Nikolic-Audit83, K. Nikolopoulos19, J.K. Nilsen121, P. Nilsson27, Y. Ninomiya157, A. Nisati134a, N. Nishu35c, R. Nisius103, I. Nitsche46, T. Nitta174, T. Nobe157, Y. Noguchi71, M. Nomachi120, I. Nomidis31, M.A. Nomura27, T. Nooney79, M. Nordberg32, N. Norjoharuddeen122, O. Novgorodova47, M. Nozaki69, L. Nozka117, K. Ntekas166, E. Nurse81, F. Nuti91, K. O’connor25, D.C. O’Neil144, A.A. O’Rourke45, V. O’Shea56, F.G. Oakham31,d, H. Oberlack103, T. Obermann23, J. Ocariz83, A. Ochi70, I. Ochoa38, J.P. Ochoa-Ricoux34a, S. Oda73, S. Odaka69, A. Oh87, S.H. Oh48, C.C. Ohm16, H. Ohman168, H. Oide53a,53b, H. Okawa164, Y. Okumura157, T. Okuyama69, A. Olariu28b, L.F. Oleiro Seabra128a, S.A. Olivares Pino49, D. Oliveira Damazio27, A. Olszewski42, J. Olszowska42, A. Onofre128a,128e, K. Onogi105, P.U.E. Onyisi11,aa, – 37 – JHEP06(2018)022 H. Oppen121, M.J. Oreglia33, Y. Oren155, D. Orestano136a,136b, N. Orlando62b, R.S. Orr161, B. Osculati53a,53b,∗, R. Ospanov36a, G. Otero y Garzon29, H. Otono73, M. Ouchrif137d, F. Ould-Saada121, A. Ouraou138, K.P. Oussoren109, Q. Ouyang35a, M. Owen56, R.E. Owen19, V.E. Ozcan20a, N. Ozturk8, K. Pachal144, A. Pacheco Pages13, L. Pacheco Rodriguez138, C. Padilla Aranda13, S. Pagan Griso16, M. Paganini179, F. Paige27, G. Palacino64, S. Palazzo40a,40b, S. Palestini32, M. Palka41b, D. Pallin37, E.St. Panagiotopoulou10, I. Panagoulias10, C.E. Pandini83, J.G. Panduro Vazquez80, P. Pani32, S. Panitkin27, D. Pantea28b, L. Paolozzi52, Th.D. Papadopoulou10, K. Papageorgiou9,s, A. Paramonov6, D. Paredes Hernandez179, A.J. Parker75, M.A. Parker30, K.A. Parker45, F. Parodi53a,53b, J.A. Parsons38, U. Parzefall51, V.R. Pascuzzi161, J.M. Pasner139, E. Pasqualucci134a, S. Passaggio53a, Fr. Pastore80, S. Pataraia86, J.R. Pater87, T. Pauly32, B. Pearson103, S. Pedraza Lopez170, R. Pedro128a,128b, S.V. Peleganchuk111,c, O. Penc129, C. Peng35a,35d, H. Peng36a, J. Penwell64, B.S. Peralva26b, M.M. Perego138, D.V. Perepelitsa27, F. Peri17, L. Perini94a,94b, H. Pernegger32, S. Perrella106a,106b, R. Peschke45, V.D. Peshekhonov68,∗, K. Peters45, R.F.Y. Peters87, B.A. Petersen32, T.C. Petersen39, E. Petit58, A. Petridis1, C. Petridou156, P. Petroff119, E. Petrolo134a, M. Petrov122, F. Petrucci136a,136b, N.E. Pettersson89, A. Peyaud138, R. Pezoa34b, F.H. Phillips93, P.W. Phillips133, G. Piacquadio150, E. Pianori173, A. Picazio89, E. Piccaro79, M.A. Pickering122, R. Piegaia29, J.E. Pilcher33, A.D. Pilkington87, A.W.J. Pin87, M. Pinamonti135a,135b, J.L. Pinfold3, H. Pirumov45, M. Pitt175, L. Plazak146a, M.-A. Pleier27, V. Pleskot86, E. Plotnikova68, D. Pluth67, P. Podberezko111, R. Poettgen148a,148b, R. Poggi123a,123b, L. Poggioli119, D. Pohl23, G. Polesello123a, A. Poley45, A. Policicchio40a,40b, R. Polifka32, A. Polini22a, C.S. Pollard56, V. Polychronakos27, K. Pomm`es32, D. Ponomarenko100, L. Pontecorvo134a, G.A. Popeneciu28d, A. Poppleton32, S. Pospisil130, K. Potamianos16, I.N. Potrap68, C.J. Potter30, G. Poulard32, T. Poulsen84, J. Poveda32, M.E. Pozo Astigarraga32, P. Pralavorio88, A. Pranko16, S. Prell67, D. Price87, M. Primavera76a, S. Prince90, N. Proklova100, K. Prokofiev62c, F. Prokoshin34b, S. Protopopescu27, J. Proudfoot6, M. Przybycien41a, A. Puri169, P. Puzo119, J. Qian92, G. Qin56, Y. Qin87, A. Quadt57, M. Queitsch-Maitland45, D. Quilty56, S. Raddum121, V. Radeka27, V. Radescu122, S.K. Radhakrishnan150, P. Radloff118, P. Rados91, F. Ragusa94a,94b, G. Rahal181, J.A. Raine87, S. Rajagopalan27, C. Rangel-Smith168, T. Rashid119, S. Raspopov5, M.G. Ratti94a,94b, D.M. Rauch45, F. Rauscher102, S. Rave86, I. Ravinovich175, J.H. Rawling87, M. Raymond32, A.L. Read121, N.P. Readioff58, M. Reale76a,76b, D.M. Rebuzzi123a,123b, A. Redelbach177, G. Redlinger27, R. Reece139, R.G. Reed147c, K. Reeves44, L. Rehnisch17, J. Reichert124, A. Reiss86, C. Rembser32, H. Ren35a,35d, M. Rescigno134a, S. Resconi94a, E.D. Resseguie124, S. Rettie171, E. Reynolds19, O.L. Rezanova111,c, P. Reznicek131, R. Rezvani97, R. Richter103, S. Richter81, E. Richter-Was41b, O. Ricken23, M. Ridel83, P. Rieck103, C.J. Riegel178, J. Rieger57, O. Rifki115, M. Rijssenbeek150, A. Rimoldi123a,123b, M. Rimoldi18, L. Rinaldi22a, G. Ripellino149, B. Risti´c32, E. Ritsch32, I. Riu13, F. Rizatdinova116, E. Rizvi79, C. Rizzi13, R.T. Roberts87, S.H. Robertson90,o, A. Robichaud-Veronneau90, D. Robinson30, J.E.M. Robinson45, A. Robson56, E. Rocco86, C. Roda126a,126b, Y. Rodina88,an, S. Rodriguez Bosca170, A. Rodriguez Perez13, D. Rodriguez Rodriguez170, S. Roe32, C.S. Rogan59, O. Røhne121, J. Roloff59, A. Romaniouk100, M. Romano22a,22b, S.M. Romano Saez37, E. Romero Adam170, N. Rompotis77, M. Ronzani51, L. Roos83, S. Rosati134a, K. Rosbach51, P. Rose139, N.-A. Rosien57, E. Rossi106a,106b, L.P. Rossi53a, J.H.N. Rosten30, R. Rosten140, M. Rotaru28b, J. Rothberg140, D. Rousseau119, A. Rozanov88, Y. Rozen154, X. Ruan147c, F. Rubbo145, F. R¨uhr51, A. Ruiz-Martinez31, Z. Rurikova51, N.A. Rusakovich68, H.L. Russell90, J.P. Rutherfoord7, N. Ruthmann32, Y.F. Ryabov125, M. Rybar169, G. Rybkin119, S. Ryu6, A. Ryzhov132, G.F. Rzehorz57, A.F. Saavedra152, – 38 – JHEP06(2018)022 G. Sabato109, S. Sacerdoti29, H.F-W. Sadrozinski139, R. Sadykov68, F. Safai Tehrani134a, P. Saha110, M. Sahinsoy60a, M. Saimpert45, M. Saito157, T. Saito157, H. Sakamoto157, Y. Sakurai174, G. Salamanna136a,136b, J.E. Salazar Loyola34b, D. Salek109, P.H. Sales De Bruin168, D. Salihagic103, A. Salnikov145, J. Salt170, D. Salvatore40a,40b, F. Salvatore151, A. Salvucci62a,62b,62c, A. Salzburger32, D. Sammel51, D. Sampsonidis156, D. Sampsonidou156, J. S´anchez170, V. Sanchez Martinez170, A. Sanchez Pineda167a,167c, H. Sandaker121, R.L. Sandbach79, C.O. Sander45, M. Sandhoff178, C. Sandoval21, D.P.C. Sankey133, M. Sannino53a,53b, Y. Sano105, A. Sansoni50, C. Santoni37, H. Santos128a, I. Santoyo Castillo151, A. Sapronov68, J.G. Saraiva128a,128d, B. Sarrazin23, O. Sasaki69, K. Sato164, E. Sauvan5, G. Savage80, P. Savard161,d, N. Savic103, R. Sawada157, C. Sawyer133, L. Sawyer82,u, J. Saxon33, C. Sbarra22a, A. Sbrizzi22a,22b, T. Scanlon81, D.A. Scannicchio166, M. Scarcella152, J. Schaarschmidt140, P. Schacht103, B.M. Schachtner102, D. Schaefer32, L. Schaefer124, R. Schaefer45, J. Schaeffer86, S. Schaepe23, S. Schaetzel60b, U. Sch¨afer86, A.C. Schaffer119, D. Schaile102, R.D. Schamberger150, V.A. Schegelsky125, D. Scheirich131, M. Schernau166, C. Schiavi53a,53b, S. Schier139, L.K. Schildgen23, C. Schillo51, M. Schioppa40a,40b, S. Schlenker32, K.R. Schmidt-Sommerfeld103, K. Schmieden32, C. Schmitt86, S. Schmitt45, S. Schmitz86, U. Schnoor51, L. Schoeffel138, A. Schoening60b, B.D. Schoenrock93, E. Schopf23, M. Schott86, J.F.P. Schouwenberg108, J. Schovancova32, S. Schramm52, N. Schuh86, A. Schulte86, M.J. Schultens23, H.-C. Schultz-Coulon60a, H. Schulz17, M. Schumacher51, B.A. Schumm139, Ph. Schune138, A. Schwartzman145, T.A. Schwarz92, H. Schweiger87, Ph. Schwemling138, R. Schwienhorst93, J. Schwindling138, A. Sciandra23, G. Sciolla25, M. Scornajenghi40a,40b, F. Scuri126a,126b, F. Scutti91, J. Searcy92, P. Seema23, S.C. Seidel107, A. Seiden139, J.M. Seixas26a, G. Sekhniaidze106a, K. Sekhon92, S.J. Sekula43, N. Semprini-Cesari22a,22b, S. Senkin37, C. Serfon121, L. Serin119, L. Serkin167a,167b, M. Sessa136a,136b, R. Seuster172, H. Severini115, T. Sfiligoj78, F. Sforza32, A. Sfyrla52, E. Shabalina57, N.W. Shaikh148a,148b, L.Y. Shan35a, R. Shang169, J.T. Shank24, M. Shapiro16, P.B. Shatalov99, K. Shaw167a,167b, S.M. Shaw87, A. Shcherbakova148a,148b, C.Y. Shehu151, Y. Shen115, N. Sherafati31, P. Sherwood81, L. Shi153,ao, S. Shimizu70, C.O. Shimmin179, M. Shimojima104, I.P.J. Shipsey122, S. Shirabe73, M. Shiyakova68,ap, J. Shlomi175, A. Shmeleva98, D. Shoaleh Saadi97, M.J. Shochet33, S. Shojaii94a, D.R. Shope115, S. Shrestha113, E. Shulga100, M.A. Shupe7, P. Sicho129, A.M. Sickles169, P.E. Sidebo149, E. Sideras Haddad147c, O. Sidiropoulou177, A. Sidoti22a,22b, F. Siegert47, Dj. Sijacki14, J. Silva128a,128d, S.B. Silverstein148a, V. Simak130, L. Simic14, S. Simion119, E. Simioni86, B. Simmons81, M. Simon86, P. Sinervo161, N.B. Sinev118, M. Sioli22a,22b, G. Siragusa177, I. Siral92, S.Yu. Sivoklokov101, J. Sj¨olin148a,148b, M.B. Skinner75, P. Skubic115, M. Slater19, T. Slavicek130, M. Slawinska42, K. Sliwa165, R. Slovak131, V. Smakhtin175, B.H. Smart5, J. Smiesko146a, N. Smirnov100, S.Yu. Smirnov100, Y. Smirnov100, L.N. Smirnova101,aq, O. Smirnova84, J.W. Smith57, M.N.K. Smith38, R.W. Smith38, M. Smizanska75, K. Smolek130, A.A. Snesarev98, I.M. Snyder118, S. Snyder27, R. Sobie172,o, F. Socher47, A. Soffer155, A. Søgaard49, D.A. Soh153, G. Sokhrannyi78, C.A. Solans Sanchez32, M. Solar130, E.Yu. Soldatov100, U. Soldevila170, A.A. Solodkov132, A. Soloshenko68, O.V. Solovyanov132, V. Solovyev125, P. Sommer51, H. Son165, A. Sopczak130, D. Sosa60b, C.L. Sotiropoulou126a,126b, R. Soualah167a,167c, A.M. Soukharev111,c, D. South45, B.C. Sowden80, S. Spagnolo76a,76b, M. Spalla126a,126b, M. Spangenberg173, F. Span`o80, D. Sperlich17, F. Spettel103, T.M. Spieker60a, R. Spighi22a, G. Spigo32, L.A. Spiller91, M. Spousta131, R.D. St. Denis56,∗, A. Stabile94a, R. Stamen60a, S. Stamm17, E. Stanecka42, R.W. Stanek6, C. Stanescu136a, M.M. Stanitzki45, B.S. Stapf109, S. Stapnes121, E.A. Starchenko132, G.H. Stark33, J. Stark58, S.H Stark39, P. Staroba129, P. Starovoitov60a, S. St¨arz32, R. Staszewski42, P. Steinberg27, B. Stelzer144, H.J. Stelzer32, O. Stelzer-Chilton163a, – 39 – JHEP06(2018)022 H. Stenzel55, G.A. Stewart56, M.C. Stockton118, M. Stoebe90, G. Stoicea28b, P. Stolte57, S. Stonjek103, A.R. Stradling8, A. Straessner47, M.E. Stramaglia18, J. Strandberg149, S. Strandberg148a,148b, M. Strauss115, P. Strizenec146b, R. Str¨ohmer177, D.M. Strom118, R. Stroynowski43, A. Strubig49, S.A. Stucci27, B. Stugu15, N.A. Styles45, D. Su145, J. Su127, S. Suchek60a, Y. Sugaya120, M. Suk130, V.V. Sulin98, DMS Sultan162a,162b, S. Sultansoy4c, T. Sumida71, S. Sun59, X. Sun3, K. Suruliz151, C.J.E. Suster152, M.R. Sutton151, S. Suzuki69, M. Svatos129, M. Swiatlowski33, S.P. Swift2, I. Sykora146a, T. Sykora131, D. Ta51, K. Tackmann45, J. Taenzer155, A. Taffard166, R. Tafirout163a, N. Taiblum155, H. Takai27, R. Takashima72, E.H. Takasugi103, T. Takeshita142, Y. Takubo69, M. Talby88, A.A. Talyshev111,c, J. Tanaka157, M. Tanaka159, R. Tanaka119, S. Tanaka69, R. Tanioka70, B.B. Tannenwald113, S. Tapia Araya34b, S. Tapprogge86, S. Tarem154, G.F. Tartarelli94a, P. Tas131, M. Tasevsky129, T. Tashiro71, E. Tassi40a,40b, A. Tavares Delgado128a,128b, Y. Tayalati137e, A.C. Taylor107, G.N. Taylor91, P.T.E. Taylor91, W. Taylor163b, P. Teixeira-Dias80, D. Temple144, H. Ten Kate32, P.K. Teng153, J.J. Teoh120, F. Tepel178, S. Terada69, K. Terashi157, J. Terron85, S. Terzo13, M. Testa50, R.J. Teuscher161,o, T. Theveneaux-Pelzer88, F. Thiele39, J.P. Thomas19, J. Thomas-Wilsker80, P.D. Thompson19, A.S. Thompson56, L.A. Thomsen179, E. Thomson124, M.J. Tibbetts16, R.E. Ticse Torres88, V.O. Tikhomirov98,ar, Yu.A. Tikhonov111,c, S. Timoshenko100, P. Tipton179, S. Tisserant88, K. Todome159, S. Todorova-Nova5, S. Todt47, J. Tojo73, S. Tok´ar146a, K. Tokushuku69, E. Tolley113, L. Tomlinson87, M. Tomoto105, L. Tompkins145,as, K. Toms107, B. Tong59, P. Tornambe51, E. Torrence118, H. Torres144, E. Torr´o Pastor140, J. Toth88,at, F. Touchard88, D.R. Tovey141, C.J. Treado112, T. Trefzger177, F. Tresoldi151, A. Tricoli27, I.M. Trigger163a, S. Trincaz-Duvoid83, M.F. Tripiana13, W. Trischuk161, B. Trocm´e58, A. Trofymov45, C. Troncon94a, M. Trottier-McDonald16, M. Trovatelli172, L. Truong147b, M. Trzebinski42, A. Trzupek42, K.W. Tsang62a, J.C-L. Tseng122, P.V. Tsiareshka95, G. Tsipolitis10, N. Tsirintanis9, S. Tsiskaridze13, V. Tsiskaridze51, E.G. Tskhadadze54a, K.M. Tsui62a, I.I. Tsukerman99, V. Tsulaia16, S. Tsuno69, D. Tsybychev150, Y. Tu62b, A. Tudorache28b, V. Tudorache28b, T.T. Tulbure28a, A.N. Tuna59, S.A. Tupputi22a,22b, S. Turchikhin68, D. Turgeman175, I. Turk Cakir4b,au, R. Turra94a, P.M. Tuts38, G. Ucchielli22a,22b, I. Ueda69, M. Ughetto148a,148b, F. Ukegawa164, G. Unal32, A. Undrus27, G. Unel166, F.C. Ungaro91, Y. Unno69, C. Unverdorben102, J. Urban146b, P. Urquijo91, P. Urrejola86, G. Usai8, J. Usui69, L. Vacavant88, V. Vacek130, B. Vachon90, K.O.H. Vadla121, A. Vaidya81, C. Valderanis102, E. Valdes Santurio148a,148b, S. Valentinetti22a,22b, A. Valero170, L. Val´ery13, S. Valkar131, A. Vallier5, J.A. Valls Ferrer170, W. Van Den Wollenberg109, H. van der Graaf109, P. van Gemmeren6, J. Van Nieuwkoop144, I. van Vulpen109, M.C. van Woerden109, M. Vanadia135a,135b, W. Vandelli32, A. Vaniachine160, P. Vankov109, G. Vardanyan180, R. Vari134a, E.W. Varnes7, C. Varni53a,53b, T. Varol43, D. Varouchas119, A. Vartapetian8, K.E. Varvell152, J.G. Vasquez179, G.A. Vasquez34b, F. Vazeille37, T. Vazquez Schroeder90, J. Veatch57, V. Veeraraghavan7, L.M. Veloce161, F. Veloso128a,128c, S. Veneziano134a, A. Ventura76a,76b, M. Venturi172, N. Venturi32, A. Venturini25, V. Vercesi123a, M. Verducci136a,136b, W. Verkerke109, A.T. Vermeulen109, J.C. Vermeulen109, M.C. Vetterli144,d, N. Viaux Maira34b, O. Viazlo84, I. Vichou169,∗, T. Vickey141, O.E. Vickey Boeriu141, G.H.A. Viehhauser122, S. Viel16, L. Vigani122, M. Villa22a,22b, M. Villaplana Perez94a,94b, E. Vilucchi50, M.G. Vincter31, V.B. Vinogradov68, A. Vishwakarma45, C. Vittori22a,22b, I. Vivarelli151, S. Vlachos10, M. Vogel178, P. Vokac130, G. Volpi126a,126b, H. von der Schmitt103, E. von Toerne23, V. Vorobel131, K. Vorobev100, M. Vos170, R. Voss32, J.H. Vossebeld77, N. Vranjes14, M. Vranjes Milosavljevic14, V. Vrba130, M. Vreeswijk109, R. Vuillermet32, I. Vukotic33, P. Wagner23, W. Wagner178, J. Wagner-Kuhr102, H. Wahlberg74, S. Wahrmund47, J. Wakabayashi105, J. Walder75, R. Walker102, W. Walkowiak143, V. Wallangen148a,148b, – 40 – JHEP06(2018)022 sAlso at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece tAlso at Centre for High Performance Computing, CSIR Campus, Rosebank, Cape Town, South Africa uAlso at Louisiana Tech University, Ruston LA, United States of America vAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain wAlso at Department of Physics, The University of Michigan, Ann Arbor MI, United States of America xAlso at Graduate School of Science, Osaka University, Osaka, Japan yAlso at Fakult¨at f¨ur Mathematik und Physik, Albert-Ludwigs-Universit¨at, Freiburg, Germany zAlso at Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands aa Also at Department of Physics, The University of Texas at Austin, Austin TX, United States of America ab Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia ac Also at CERN, Geneva, Switzerland ad Also at Georgian Technical University (GTU),Tbilisi, Georgia ae Also at Ochadai Academic Production, Ochanomizu University, Tokyo, Japan af Also at Manhattan College, New York NY, United States of America ag Also at Departamento de F´ısica, Pontificia Universidad Cat´olica de Chile, Santiago, Chile ah Also at The City College of New York, New York NY, United States of America ai Also at School of Physics, Shandong University, Shandong, China aj Also at Departamento de Fisica Teorica y del Cosmos, Universidad de Granada, Granada, Portugal ak Also at Department of Physics, California State University, Sacramento CA, United States of America al Also at Moscow Institute of Physics and Technology State University, Dolgoprudny, Russia am Also at Departement de Physique Nucleaire et Corpusculaire, Universit´e de Gen`eve, Geneva, Switzerland an Also at Institut de F´ısica d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain ao Also at School of Physics, Sun Yat-sen University, Guangzhou, China ap Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia, Bulgaria aq Also at Faculty of Physics, M.V.Lomonosov Moscow State University, Moscow, Russia ar Also at National Research Nuclear University MEPhI, Moscow, Russia as Also at Department of Physics, Stanford University, Stanford CA, United States of America at Also at Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Budapest, Hungary au Also at Giresun University, Faculty of Engineering, Turkey av Also at CPPM, Aix-Marseille Universit´e and CNRS/IN2P3, Marseille, France aw Also at Department of Physics, Nanjing University, Jiangsu, China ax Also at Institute of Physics, Academia Sinica, Taipei, Taiwan ay Also at University of Malaya, Department of Physics, Kuala Lumpur, Malaysia az Also at LAL, Univ. Paris-Sud, CNRS/IN2P3, Universit´e Paris-Saclay, Orsay, France ∗Deceased – 47 –