Search for new physics in same-sign dilepton events in proton–proton collisions at \(\sqrt{s} = 13\,\text {TeV} \)

The European Physical Journal C, Aug 2016

A search for new physics is performed using events with two isolated same-sign leptons, two or more jets, and missing transverse momentum. The results are based on a sample of proton–proton collisions at a center-of-mass energy of 13\(\,\text {TeV}\) recorded with the CMS detector at the LHC, corresponding to an integrated luminosity of 2.3 \(\mathrm{fb}^{1}\). Multiple search regions are defined by classifying events in terms of missing transverse momentum, the scalar sum of jet transverse momenta, the transverse mass associated with a \(\mathrm {W}\) boson candidate, the number of jets, the number of \(\mathrm{b} \) quark jets, and the transverse momenta of the leptons in the event. The analysis is sensitive to a wide variety of possible signals beyond the standard model. No excess above the standard model background expectation is observed. Constraints are set on various supersymmetric models, with gluinos and bottom squarks excluded for masses up to 1300 and 680\(\,\text {GeV}\), respectively, at the 95 % confidence level. Upper limits on the cross sections for the production of two top quark-antiquark pairs (119\(\,\text {fb}\)) and two same-sign top quarks (1.7\(\,\text {pb}\)) are also obtained. Selection efficiencies and model independent limits are provided to allow further interpretations of the results.

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Search for new physics in same-sign dilepton events in proton–proton collisions at \(\sqrt{s} = 13\,\text {TeV} \)

Eur. Phys. J. C Search for new physics in same-sign dilepton events √ in proton-proton collisions at s = 13 TeV CMS Collaboration 0 1 2 4 5 0 CERN , 1211 Geneva 23 , Switzerland 1 Faculty of Science, University of Split , Split , Croatia Z. Antunovic, M. Kovac 2 Faculty of Physics, Institute of Experimental Physics, University of Warsaw , Warsaw , Poland K. Bunkowski, A. Byszuk 3 , K. Doroba , A. Kalinowski, M. Konecki, J. Krolikowski, M. Misiura, M. Olszewski, M. Walczak 4 University of California , Los Angeles , USA R. Cousins , P. Everaerts, A. Florent, J. Hauser, M. Ignatenko, D. Saltzberg, E. Takasugi, V. Valuev, M. Weber 5 University of Notre Dame , Notre Dame , USA N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D. J. Karmgard, N. Kellams, K. Lannon, N. Marinelli, F. Meng A search for new physics is performed using events with two isolated same-sign leptons, two or more jets, and missing transverse momentum. The results are based on a sample of proton-proton collisions at a center-of-mass energy of 13 TeV recorded with the CMS detector at the LHC, corresponding to an integrated luminosity of 2.3 fb1. Multiple search regions are defined by classifying events in terms of missing transverse momentum, the scalar sum of jet transverse momenta, the transverse mass associated with a W boson candidate, the number of jets, the number of b quark jets, and the transverse momenta of the leptons in the event. The analysis is sensitive to a wide variety of possible signals beyond the standard model. No excess above the standard model background expectation is observed. Constraints are set on various supersymmetric models, with gluinos and bottom squarks excluded for masses up to 1300 and 680 GeV, respectively, at the 95 % confidence level. Upper limits on the cross sections for the production of two top quark-antiquark pairs (119 fb) and two same-sign top quarks (1.7 pb) are also obtained. Selection efficiencies and model independent limits are provided to allow further interpretations of the results. - 1 Introduction Searches for new physics in final states with two leptons that have same-sign (SS) charges provide a powerful probe for searches of new physics, both because standard model (SM) processes with this signature are few and have low cross sections, and because this signature is produced in a large number of important new-physics scenarios. Examples of the latter include the production of supersymmetric (SUSY) particles [ 1,2 ], Majorana neutrinos [3], vector-like quarks [ 4 ], and SS top quark pairs [ 5,6 ]. In the SUSY framework [ 7– 15 ], the SS signature can arise through gluino pair production. For example, the Majorana nature of the gluino allows gluino pairs to decay via SS charginos, yielding two SS W bosons. Gluino pair production can also yield four W bosons, e.g., from the decay of four top quarks, which may result in the SS dilepton final state. Alternatively, cascade decays of pair-produced squarks can lead to the SS dilepton signature. Searches for new physics in the SS channel have been previously performed at the CERN LHC by the ATLAS [ 16–18 ] and CMS [ 19–23 ] Collaborations. This paper describes a search for new physics in the final state with two or more leptons and including a SS pair (μ±μ±, μ±e±, or e±e±, where μ is a muon and e an electron). The analysis is based on proton–proton (pp) collision data at √s = 13 TeV, corresponding to an integrated luminosity of 2.3 fb−1 collected with the CMS detector in 2015. The search strategy resembles that used in our analysis of 19.5 fb1 of data collected at √s = 8 TeV [ 23 ], which excluded gluino masses in the four top quark signature up to about 1050 GeV. We design an inclusive analysis sensitive to a wide range of new-physics processes produced via strong interactions and yielding undetected particles in the final state. The interpretations of the results consider R-parity conserving SUSY models [ 24 ], as well as cross section limits on the production of two top quark-antiquark (tt) pairs and of two SS top quarks. We also provide model independent limits to allow further interpretations of the results. With respect to Ref. [ 23 ], the kinematic regions are redefined and improvements in the event selection are implemented, both of which increase the sensitivity to new-physics scenarios at √s = 13 TeV. 2 The CMS detector The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3.8 T. Within the field volume are several particle detection systems. Charged-particle trajectories are measured with silicon pixel and strip trackers, covering 0 ≤ φ < 2π in azimuth and |η| < 2.5 in pseudorapidity, where η ≡ − ln[tan(θ /2)] and θ is the polar angle of the trajectory of the particle with respect to the counterclockwise beam direction. The transverse momentum, namely the component of the momentum p in the plane orthogonal to the beam, is defined as pT = p sin θ . Surrounding the silicon trackers, a lead tungstate crystal electromagnetic calorimeter and a brass and scintillator hadron calorimeter provide energy measurements of electrons, photons, and hadronic jets in the range |η| < 3.0. Muons are identified and measured within |η| < 2.4 by gas-ionization detectors embedded in the steel flux-return yoke of the solenoid. Forward calorimeters on each side of the interaction point encompass 3.0 < |η| < 5.0. The CMS trigger consists of a two-stage system. The first level of the CMS trigger system, composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events in a fixed time interval of less than 4 µs. The high-level trigger (HLT) processor farm further decreases the event rate from around 100 kHz to less than 1 kHz, before data storage. A more detailed description of the CMS detector can be found in Ref. [ 25 ]. 3 Event selection and Monte Carlo simulation Events are selected with two sets of HLT algorithms. The first requires two very loosely isolated leptons, one satisfying pT > 17 GeV and the other satisfying pT > 8 GeV for a muon and 12 GeV for an electron. The isolation is evaluated with respect to nearby tracks for a muon and to both tracks and calorimetric objects for an electron. The second set of triggers selects events with lowered pT thresholds of 8 GeV and without a restriction on the isolation, but requiring a hadronic activity H HLT > 300 GeV, where H HLT is the T T scalar pT sum of all jets with pT > 40 GeV and |η| < 3.0 identified by the HLT. Typical trigger efficiencies for leptons satisfying the selection criteria described below are 94 % (98 %) per muon (electron), with 100 % efficiency for the H HLT requirement. T In the subsequent analysis, muon candidates are reconstructed by combining information from the silicon tracker and the muon spectrometer in a global fit [ 26 ]. A selection is performed using the quality of the geometrical matching between the tracker and muon system measurements. We select muons with well-determined charge by imposing an additional criterion: δpT(μ)/ pT(μ) < 0.2, where δpT(μ) is the uncertainty in the measurement of the muon pT from the global fit. Electron candidates are reconstructed by combining clusters of energy in the electromagnetic calorimeter with tracks in the silicon tracker [ 27 ]. The identification is performed using a Boosted Decision Tree multivariate discriminant [ 28 ] based on shower shape and track quality variables. The nominal selection criteria are designed to provide a maximum rejection of electron candidates from multijet production while maintaining approximately 90 % efficiency for electrons from the decay of W or Z bosons. A relaxed selection on the multivariate discriminant is used to define “loose” criteria for electron identification. To improve the accuracy of the electron charge reconstruction, we require the position of the calorimeter deposit, relative to the linear projection of the deposits in the pixel detector to the inner calorimeter surface, to be consistent with the charge determination from the full track fit. Electrons originating from photon conversions are suppressed by rejecting candidates that are either without energy deposits in the innermost layers of the tracking system, or that are associated with a displaced vertex compatible with a photon conversion. Lepton candidates are required to be consistent with originating from the collision vertex for which the summed pT2 of the associated physics objects is the largest. The transverse (longitudinal) impact parameter of the leptons must not exceed 0.5 (1.0) mm with respect to this vertex, and they must fulfill the requirement |d3D|/σ (d3D) < 4, where d3D is the three-dimensional impact parameter with respect to the vertex, and σ (d3D) is its uncertainty from the track fit. The charged leptons produced in decays of heavy particles, such as W and Z bosons or SUSY particles (“prompt” leptons), are typically spatially isolated from the hadronic activity in the event, while leptons produced in hadron decays or in photon conversions, as well as hadrons misidentified as leptons, are usually embedded in jets (“nonprompt” leptons). This distinction becomes less evident for systems with a high Lorentz boost, where decay products tend to overlap and jets may contribute to the energy deposition around prompt leptons. This problem is mitigated with an isolation definition constructed using the following three variables: • the mini-isolation variable (Imini) [ 29 ], computed as the ratio of the scalar pT sum of charged hadrons, neutral hadrons, and photons within a cone of radius R ≡ ( η)2 + ( φ)2 around the lepton candidate direction at the vertex, to the transverse momentum of the lepton candidate ( pT( )). The cone radius R depends on pT( ) as: 10 GeV R ( pT( )) = min [max( pT( ), 50 GeV), 200 GeV] . (1) The varying isolation cone definition takes into account the increased collimation of the decay products of a hadron as its pT increases, and it reduces the inefficiency from accidental overlap between the lepton and jets in a busy event environment. The momentum estimate of each particle is performed by the particle-flow (PF) algorithm [ 30,31 ], which identifies individual particles through a combination of information from different detector components. , • the ratio of the pT of the lepton to that of the closest jet within a distance R = 0.4: (2) (3) (4) where the definition of a jet is given below. In case of no jet within this distance, the value of pratio is set to 1. The T pratio variable is a measure of the isolation in a larger cone T and improves the performance of the isolation definition, especially for low- pT nonprompt leptons, which are more likely than high- pT leptons to appear in a jet that is wider than the Imini cone. • the prel variable [ 32 ], defined as the transverse momen T tum of the lepton relative to the residual momentum of the closest jet after lepton momentum subtraction: prel T = |( p(jet) − p( )) × p( )| | p(jet) − p( )| . This variable allows the identification of leptons that accidentally overlap with jets. A lepton is considered to be isolated if the following condition is satisfied: Imini < I1 AND ( pTratio > I2 OR pTrel > I3). The values of Ii , with i = 1, 2, 3, depend on the lepton flavor: because the probability to misidentify a lepton is higher for electrons, tighter isolation values are used in this case (see Table 1). In addition, a “loose” isolation criterion is defined as Imini < 0.4. Muons (electrons) are required to have pT > 10 (15) GeV and |η| < 2.4 (2.5); at least one SS lepton pair with an invariant mass above 8 GeV must be present in the event. In order to reduce backgrounds from inclusive production of the Z boson and from low-mass resonances decaying into lepton pairs, the SS pair is rejected if there is an additional lepton in the event that satisfies loose requirements and that forms an opposite-sign, same-flavor pair with an invariant mass less than 12 GeV or between 76 and 106 GeV with one of the two SS leptons. Jets and missing transverse momentum (ETmiss) are recon structed with the PF algorithm. We define E miss as the magT nitude of the vector sum of all PF candidate transverse momenta [ 33 ]. For jet clustering, the anti-kt algorithm [ 34 ] with a distance parameter of 0.4 is utilized. Jets are required to satisfy quality requirements [ 35 ] to remove those consistent with anomalous energy deposits. After the estimated contribution from additional pp interactions in the same or adjacent bunch crossings (pileup) is subtracted, jet energies are corrected for residual nonuniformity and nonlinearity of the detector response using simulation and data. Jets are required to have pT > 40 GeV and to lie within the tracker acceptance |η| < 2.4. Jets must be separated from loosely identified leptons by R > 0.4, so that jets already employed for the calculation of lepton isolation variables are not considered further in the analysis. We require Njet ≥ 2, where Njet denotes the number of selected jets in the event. The hadronic activity in the event (HT) is defined as the scalar pT sum of the selected jets. To identify jets originating from b quarks, the combined secondary vertex algorithm CSVv2 [ 36 ] is used. Jets with pT > 25 GeV and |η| < 2.4 are considered as b-tagged if they satisfy the requirements of the medium working point of the algorithm. These requirements result in approximately a 70 % efficiency for tagging a b quark jet, and a less than 1 % mistagging rate for light-quark and gluon jets in tt events. The number of b-tagged jets in the event is denoted as Nb. Monte Carlo (MC) simulation, which includes the contribution of pileup, is used to estimate the background from SM processes with prompt SS leptons (see Sect. 5) and to calculate the efficiency for various new-physics scenarios. The SM background samples are produced with the MadGraph5_aMC@NLO 2.2.2 generator [ 37 ] at leading order (LO) or next-to-leading order (NLO) accuracy in perturbative quantum chromodynamics, with the exception of diboson samples, which are produced with the powheg v2 [ 38,39 ] generator. The NNPDF3.0LO [40] parton distribution functions (PDFs) are used for the simulated samples generated at LO, and the NNPDF3.0NLO [ 40 ] PDFs for the samples generated at NLO. Parton showering and hadronization are described using the pythia 8.205 generator [ 41 ] with the CUETP8M1 tune [ 42,43 ]. The CMS detector response for the background samples is modeled with the Geant4 package [44]. The signal samples are generated with MadGraph5_aMC@NLO at LO precision, including up to two additional partons in the matrix element calculations; parton showering and hadronization, as well as decays of SUSY particles, are simulated with pythia, while the detector simulation is performed with the CMS fast simulation package [ 45 ]. Isolation variable I1 I2 I3 (GeV) Muons 0.16 0.76 7.2 Electrons 0.12 0.80 7.2 4 Search strategy This analysis is designed as an inclusive search, sensitive to models matching two assumptions: a strong-interaction production mechanism, leading to relatively large hadronic P2 P1 activity, and the presence of undetected particles in the final state, yielding sizable E miss. In the process of defining the T search strategy, R-parity conserving SUSY is taken as a guideline because of its rich variety of signatures. In this context, signal models that can lead to the experimental signature of SS lepton pairs differentiate themselves in the numbers of W bosons, b jets, and light-flavor jets produced in the decays of SUSY particles. In addition, the mass differences between the SUSY particles involved in the decay chains affect the energy spectra of the decay products, resulting in differences between the models in the distributions of kinematic quantities such as the pT of the leptons, HT, and E miss. T We consider SUSY scenarios in the context of simplified models of new-particle production [ 46,47 ]. Models with four W bosons and four b jets involve gluino pair production, followed by the decay of each gluino through a chain containing third-generation squarks. If the gluino is lighter than all squarks, and the top squark is the lightest squark, the gluino undergoes a three-body decay mediated by an off-shell top squark. If the dominant top squark decay is t1 → tχ10, where χ10 is the lightest neutralino, taken to be the stable, undetected, lightest SUSY particle (LSP), then the gluino threebody decay is g → ttχ10 (T1tttt model in Fig. 1, upper left). If instead the dominant top squark decay is t1 → bχ1+, the gluino three-body decay is g → tbχ1+ (T5ttbbWW model in Fig. 1, upper middle); the latter signature can also arise if the bottom squark is the lightest squark and decays as b1 → tχ1−. If the top squark is light enough to be on-shell and decays predominantly to a top quark and the LSP, gluinos decay through the chain g → t1t → ttχ10 (T5tttt model in Fig. 1, upper right). If instead the top squark mainly decays to the charm quark and the LSP, gluinos decay as in the T5ttcc model (Fig. 1, lower left); in this case only two W bosons and two b jets are produced. Events with four W bosons and two b jets can arise from bottom squark pair production, where each bottom squark decays to a top quark and a chargino, and the chargino decays to an LSP and a (possibly off-shell) W boson (T6ttWW model in Fig. 1, lower middle). Finally, SS lepton pairs can be produced in association with large values of HT, E miss, and Njet, but without b jets. T In particular, events with two W bosons and four light-flavor quark jets can arise from gluino pair production if each gluino decays to two light quarks and a chargino. The two charginos can have the same charge and each decay to a W boson and the LSP (T5qqqqWW model in Fig. 1, lower right). In the case that the difference in mass between the chargino and the LSP is small, the W bosons are off-shell and produce soft leptons. To increase the sensitivity to new-physics scenarios, we categorize events based on their kinematic properties as follows. First, three exclusive lepton selections are defined: • high–high (HH) selection: two SS leptons, each with pT ≥ 25 GeV; • high–low (HL) selection: two SS leptons, one with pT ≥ 25 GeV and the other with 10 ≤ pT < 25 GeV; • low–low (LL) selection: two SS leptons, each with 10 ≤ pT < 25 GeV. The high lepton pT threshold suppresses the contribution from nonprompt leptons; hence the SM background in the HH region arises primarily from events with prompt SS leptons. The nonprompt lepton background is largely contained in the HL region, where the high- pT lepton is typically prompt and the low- pT lepton nonprompt. The LL region is characterized by a very small background since all processes where at least one lepton originates from an on-shell vector boson are suppressed by the low- pT requirements, while events with two nonprompt leptons are suppressed by the kinematic requirements described below; the main residual contribution in this region is from nonprompt leptons. Second, search regions (SR) are introduced so that the analysis is sensitive to a variety of new-physics scenarios. SRs are defined separately for the HH, HL, and LL selections using the HT, E miss, Njet, and Nb variables: Njet and T Nb separate signal from background for scenarios with a large production of jets and/or b jets, while HT and E miss increase T sensitivity to models with different masses of SUSY particles. In addition, we make use of the M min variable, defined T as: MTmin = min MT( 1, ETmiss), MT( 2, ETmiss) , (5) where MT( , ETmiss) = 2 pT( )ETmiss(1 − cos φ ,ETmiss ) is the transverse mass and φ ,ETmiss is the azimuthal angle difference between the directions of the lepton and of the missing transverse momentum [ 48 ]. In the case of an SS lepton pair from tt or W+jets processes, where one lepton is prompt and the other nonprompt, this variable has a cutoff near the W boson mass; consequently, the nonprompt lepton background is suppressed for SRs requiring M min > 120 GeV T and is large for M min < 120 GeV. In order to better charac T terize the background we use a fine SR binning in kinematic regions where SM processes are abundant (e.g., low M min T and low E miss), while, due to the low background, we use a T coarser binning in regions with tight selections. Finally, inclusive search regions in the HH and HL categories are defined in the tails of the E miss and HT variables; T the boundaries E miss > 300 GeV and HT > 1125 GeV (for T ETmiss ≤ 300 GeV) are chosen so that each of these regions typically contains 1 background event. A summary of the selection criteria is presented in Tables 2, 3 and 4. All SRs are non-overlapping. They are combined statistically to obtain the final results (Sect. 7). 5 Backgrounds Backgrounds in the SS dilepton final state can be divided into three categories: • Nonprompt leptons: Nonprompt leptons are leptons from heavy-flavor decays, hadrons misidentified as leptons, muons from light-meson decays in flight, or electrons from unidentified conversions of photons in jets. Depending on the signal region, this background is dominated by tt and W+jets processes; it represents the largest background for regions with low M min and low HT. T • SM processes with SS dileptons: Standard model processes that yield an SS lepton pair include multi-boson production (considering W, Z, H, and prompt γ ), single boson production in association with a tt pair, and doubleparton scattering. The dominant sources are WZ and ttW production, which contribute primarily to SRs with zero and ≥1 b jets, respectively. WZ events contribute to the background when the Z boson decays leptonically and is off-shell, when one of the Z-boson decay leptons is not identified, or when the Z boson decays to τ leptons that result in a semileptonic final state. SM processes with SS dileptons are the largest background in the signal regions defined by tight kinematic selections. • Charge misidentification: Charge misidentification arises from events with opposite-sign isolated leptons in which the charge of an electron is misidentified, mostly due to severe bremsstrahlung in the tracker material. Overall, this is a small background. The nonprompt lepton background is estimated from data using the “tight-to-loose” ratio method, which was employed in previous versions of the analysis [ 19–23 ] but has been improved for the current study. It is based on a control sample of events (application region) where one lepton fails the nominal (tight) selection but passes the loose requirements, defined by relaxing the isolation selection for muons, and both the isolation and identification requirements for electrons. Events in this control region are reweighted by the factor TL/(1 − TL), where TL is the probability for a nonprompt lepton that satisfies the loose selection to also satisfy the tight selection [ 19 ]. Its value is measured in a multijetenriched data set (measurement region), using events from single-lepton triggers after applying a selection designed to suppress electroweak processes (Drell–Yan and W+jets) and after subtracting their residual contribution; this selection requires only one lepton in the event, E miss < 20 GeV, and T MT < 20 GeV. The measurement is made as a function of the lepton pT and η, separately for each lepton flavor (μ or e) and trigger (with or without isolation). The method assumes that TL has the same value in the measurement and application regions. The main sources of discrepancy are identified as differences in the momentum spectrum and the flavor of the parton producing the nonprompt lepton. These two effects are mitigated in the following way. First, TL is parameterized as a function of pcorr, defined as the lepton pT plus the energy in the isoT lation cone exceeding the isolation threshold value – this quantity is highly correlated with the mother parton pT, and thus the parameterization is robust against mother parton pT variations. The second effect, i.e., flavor dependence, is relevant for electrons only: while nonprompt muons originate predominantly from heavy-flavor decays, nonprompt electrons receive sizable contributions from misidentified Nb 0 1 2 <120 >120 <120 >120 <120 >120 ≥3 Inclusive <120 >120 Inclusive 50–200 >200(∗) 50–200 >200(∗) 50–200 hadrons and conversions. The effect of variations in the flavor composition is suppressed by adjusting the loose electron identification criteria so that the numerical value of TL for electrons from light flavors matches that for electrons from heavy flavors. The loose lepton selection is defined based on MC studies, but we verify that TL is not significantly different in data events with and without b jets. As a cross-check of the prediction, an alternative TL measurement, similar to that described in Ref. [ 49 ], is performed in the dilepton control region where one of the leptons fails the impact parameter requirement. The predictions from the two methods are found to be consistent, both in MC samples and in data. The background from SM processes with a prompt SS lepton pair is evaluated from simulation, accounting for both theoretical and experimental uncertainties. The WZ background is normalized to data in a control region requiring at least two jets, no b jets, E miss > 30 GeV, and three leptons, T where two of the leptons form a same-flavor, opposite-sign pair with an invariant mass within 15 GeV of the Z boson mass; the measured normalization factor is found to be compatible with unity within about one standard deviation. The MC simulation of WZ production is used to relate the number of expected WZ events in the signal regions to the WZ event yield in the control region. Finally, the charge misidentification background is estimated by reweighting events with opposite-sign lepton pairs by the charge misidentification probability. For electrons this probability is obtained from simulated tt events and from e±e± data in the Z mass window, and it lies in the range 10−5–10−3 depending on the electron pT and η. Studies of simulated events indicate that the muon charge misidentification probability is negligible. <120 <120 <120 <120 Nb 0 1 2 ≥3 Nb Systematic uncertainties can affect both the overall normalization and the relative population of signal and background processes. A summary of their effects on the SR yields is given in Table 5. Experimental systematic uncertainties are mostly the consequence of differing event selection efficiencies in data and simulation. Lepton identification and trigger efficiencies are computed with the “tag-and-probe” technique [ 26,27 ] with an uncertainty of 2 and 4 %, respectively. For signal samples, additional uncertainties of 4–10 % are included to account for differences in the lepton efficiency between the fast and Geant4-based simulations. The jet energy scale uncertainty varies between 2 and 8 %, depending on the jet pT and η. Its impact is assessed by shifting the energy of each jet and propagating the variation to all dependent kinematic quantities (HT, E miss, Njet, Nb, and M min); correlation effects due T T to the migration of events from one SR to another are taken into account. These variations yield estimated uncertainties of 2–10 %. A similar approach is used to estimate the uncertainties associated with the b tagging efficiencies for light SR26 SR2 SR4 SR6 Emiss ∈ [ 50, 200 ] GeV T Emiss > 200 GeV T SR1 SR3 SR5 SR7 SR8 Table 5 Summary of systematic uncertainties in the event yields in the SRs. The first six uncertainties are related to experimental factors for all processes whose yield is estimated from simulation; the next five are uncertainties in these yields related to the event simulation process itself. The last three uncertainties are related to background processes whose yield is estimated from data Source Lepton selection Trigger efficiency Jet energy scale b tagging Pileup Integrated luminosity Scale variations (ttZ and ttW) Parton distribution functions (ttW and ttZ) W±W± normalization Other backgrounds Monte Carlo statistical precision Nonprompt leptons Charge misidentification WZ normalization Typical uncertainty (%) 2.3 fb-1 (13 TeV) Data Nonprompt lep. WZ ttW X+γ Charge misid. ttZ/H WW Rare SM 2.3 fb-1 (13 TeV) Data Nonprompt lep. WZ ttW X+γ Charge misid. ttZ/H WW Rare SM CMS Baseline selection 2.3 fb-1 (13 TeV) Data Nonprompt lep. WZ ttW X+γ Charge misid. ttZ/H WW Rare SM 20 40 60 80 100 120 140 160 180 200 MTmin (GeV) CMS Baseline selection CMS Baseline selection 7 Njet 2 3 4 5 6 0 1 2 3 Nb flavor and b quark jets [ 36 ], which are parameterized as a function of pT and η and are found to be of order 5 % for the highly populated SRs. The uncertainty in the modeling of pileup is 1–5 % depending on the SR. The uncertainty in the integrated luminosity is 2.7 % [ 50 ]. The background sources estimated from simulation are subject to theoretical uncertainties related to unknown higher-order effects and to uncertainties in the knowledge of the PDFs. The former are estimated by simultaneously varying the renormalization and factorization scales up and down by a factor of two. The effect on the overall cross section is found to be 13 % for ttW events and 11 % for ttZ events, while the effect on the acceptance for the various SRs amounts to 3–8 % depending on HT. The magnitude of the uncertainty related to the PDFs is obtained using variations of the NNPDF3.0 set [ 40 ]. The overall uncertainty is ∼4 % for the ttW and ttZ samples. Theoretical uncertainties are also considered for the remaining minor backgrounds estimated from simulation: a similar procedure is used for the W±W± process, leading to an overall uncertainty of 30 %, while a 50 % uncertainty is assigned to processes with a prompt γ and to the sum of the other rare processes. For all backgrounds estimated from simulation we account for the statistical uncertainty of the MC samples. The remaining sources of uncertainty are those related to the methods that are used to estimate the nonprompt lepton, charge misidentification, and WZ backgrounds. An overall normalization uncertainty of 30 % is assigned to the nonprompt lepton background prediction. This uncertainty accounts for the performance of the method on simulated data and for the differences in the prediction from the two alternative procedures described in Sect. 5. An additional uncertainty is associated with the subtraction procedure to remove Drell–Yan and W+jets events from the measurement region; 5 10 15 20 25 30 5 10 15 20 s60 e it r n E50 40 30 20 10 0 CMS HH SRs 2.3 fb-1 (13 TeV) Data Nonprompt lep. WZ X+γ ttZ/H ttW Charge misid. WW Rare SM 25 SR s e itr 5 n E 4 3 2 1 0 the overall effect on the nonprompt lepton background yield is 1–20 %, depending on the SR considered, and is larger for high- pT leptons. Finally, we account for the statistical uncertainty in the number of events observed in the application region. The background from charge misidentification is assigned a systematic uncertainty of 26 %, which corresponds to the difference between the e±e± event yield in the Z mass window in data and simulation. The uncertainty in the WZ background is measured to be 30 % in the control region. It includes statistical uncertainties and systematic uncertainties due to non-WZ background subtraction. Using the same procedure as described above, uncertainties in the extrapolation from the control to the signal regions are assessed from the propagation of the uncertainty in the jet energy scale and in the b tagging efficiencies. 7 Results Distributions of the five kinematic variables used to define the SRs, HT, E miss, M min, Njet, and Nb, are shown in Fig. 2 T T after a baseline selection requiring a pair of SS leptons, two jets, and either E miss > 30 GeV or HT > 500 GeV. The T results are shown in comparison to the background prediction. The event yields in the SRs after the full selection are HH event yields Expected SM 36.0 ± 7.0 1 2.8 ± 2.1 1.05 ± 0.36 1.49 ± 0.52 2.29 ± 0.49 0.11 ± 0.04 0.91 ± 0.31 0.16 ± 0.06 21.6 ± 5.2 8.6 ± 1.4 2.10 ± 0.92 2.24 ± 0.40 1.09 ± 0.21 0.25 ± 0.11 0.37 ± 0.12 0.19 ± 0.08 4.9 ± 1.0 2.90 ± 0.47 0.47 ± 0.09 1.43 ± 0.25 0.40 ± 0.10 0.08 ± 0.04 0.17 ± 0.06 0.14 ± 0.04 0.21 ± 0.06 0.46 ± 0.12 0.005 ± 0.016 0.03 ± 0.02 0.02 ± 0.01 0.02 ± 0.01 1.91 ± 0.32 0.85 ± 0.18 presented in Fig. 3 and in Table 6; no significant deviation from the SM background prediction is observed. The largest local significances are 2.2 and 1.8 standard deviations in HL SR8 and in HH SR10, respectively. The results of the search are used to constrain the benchmark SUSY models presented in Sect. 4. For each mass point in the SUSY particle mass spectrum, results from all SRs are combined to extract cross section exclusion limits at the 95 % confidence level (CL), using the asymptotic formulation of the modified frequentist CLs criterion [ 51–54 ]. Signal and background uncertainties are included as log-normal nuisance parameters and, when relevant, take into account correlation effects among different SRs and/or different processes. Exclusion contours make use of the cross section values calculated at NLO plus next-to-leading logarithmic (NLL) accuracy, assuming that all SUSY particles other than those included in the respective diagram are too heavy to participate in the interaction [ 55–60 ]. In general, the SR with the largest sensitivity is HH SR31, which requires E miss > 300 GeV T and is inclusive in the other variables. However, depending on the model and the region of parameter space, other SRs contribute significantly to the total sensitivity: for instance, a considerable contribution comes from HL SR25 in case of signal models with a soft lepton, from HH SR32 and HL SR26 in case of high HT, from HH SR3 and SR8 in case of no b jets, and from HH SR24 and SR26 in case of 2 or more b jets. Results for models with gluinos decaying to virtual third generation squarks are shown in Fig. 4 as a function of the gluino and LSP masses. For the T1tttt model (Fig. 4-left), in the regions of the SUSY parameter space with a large mass difference between the gluino and the LSP, the results are rather stable with respect to LSP mass variations, and gluino masses up to 1300 GeV are excluded. Near the kinematic threshold mg − mχ10 = 2(mW + mb), the gluino mass limit becomes weaker and is reduced to 1050 GeV for an LSP mass of 800 GeV. Results for the T5ttbbWW model with nearly degenerate χ1± and χ10 masses are shown in Fig. 4-right; the limit on the gluino mass lies in the range 950–1100 GeV except for very small χ1± and χ10 masses, where the sensitivity increases because of the large Lorentz boost of the leptons from the χ1± decay. Results for models with a gluino decaying to an onshell top squark are shown in Fig. 5 as a function of the gluino and LSP masses. For the T5tttt model (Fig. 5-top), fcourrvweshiacrhe wobetatiankeed mast1 fo=r thmeχT101+ttttmmt,osdieml iilnar Feixgc.lu4s-lieofnt because the production cross section and the final-state particles are the same. The limit becomes weaker when there is a small mass difference between the top squark and the LSP: for mt1 − mχ10 = 20 GeV, the limit on the gluino mass is 1140 GeV for small LSP masses and about 850 GeV for mmo1d0e=lw7i0t0h GtheeVs(aFmige.S5U-bSoYttopmarlteicftl)e. Imnathsse vcaalsueeosf, tthheeTs5enttscicχ tivity is slightly reduced because of the smaller number of leptons and b jets in the final state (Fig. 5-bottom right). Figure 6 shows the results for b squark production in the T6ttWW model in the chargino (χ1±) versus b squark mass ) CMS eV1600 pp → ~g~g, ~g→ ttχ∼01 G ( 0∼χ11 400 m 1200 Fig. 4 Exclusion regions at the 95 % CL in the mχ10 versus mg plane for the T1tttt (left) and T5ttbbWW (right) models, where for the T5ttbbWW tmheodeexlcmluχd1±ed=crmosχs10s+ect5ioGnevVa.luTehsefroirgahtg-ihvaenndpsoiidnet cinoltohre sScUalSeYinpdairctaicteles mass plane. The solid, black curves represent the observed exclusion limits assuming the NLO+NLL cross sections (thick line), or their variations of ±1 standard deviation (thin lines). The dashed, red curves show the expected limits with the corresponding ±1 standard deviation experimental uncertainties. Excluded regions are to the left and below the limit curves )eV1600 pCpM→S~g~g, ~g→ ~t1t,~t1→ tχ∼01 NLO2+.3NLfbL-1e(x1c3luTsieoVn) (G Observed ± 1 σtheory 0∼χ11 400 m Expected ± 1 σexperiment 1200 m~t1 = m∼χ0 + 20 GeV 1 )eV1600 pCpM→S~g~g, ~g→ ~t1t,~t1→ cχ∼01 NLO2.+3NfLbL-1e(1xc3luTseioVn) (G Observed ± 1 σtheory 0∼χ11 400 m Expected ± 1 σexperiment 1200 m~t1 = m∼χ0 + 20 GeV 1 1000 800 600 400 200 0 600 +mb 0 =mW ~ - m∼χ1 mg ) b p ( n 10 itco e s s s o r c 1 on it m il r e p 10-1 pu L C % 5 9 10-2 1000 800 600 400 200 )eV1600 pCpM→S~g~g, ~g→ ~t1t,~t1→ tχ∼01 NLO2+.3NLfbL-1e(x1c3luTsieoVn) (G Observed ± 1 σtheory 0∼χ11 400 m Expected ± 1 σexperiment 1200 m~t1 = m∼χ0 + mt 1 1000 800 600 400 200 Fmigg.fo5r mEoxdcellusswiointhrtehgeiognlusiantotdheec9ay5in%g CtoLaninonth-sehpellalntoeposfqmuaχr10k:vTe5rstuttst with mt1 = mχ10 + mt (top), T5tttt with mt1 = mχ10 + 20 GeV (botpFloarnceh,awrghienroe tmheasLsSesPumpatsos5is50asGsueVm,ebd tsoqubaermkm10a=sse5s0bGeleoVw. χ 680 GeV are excluded. The limit on the b squark mass is reduced to 500 GeV in regions where mχ± is within 100 GeV of mb1 , while a milder reduction is 1observed in regions where the difference between mχ± and mχ0 is less than 150 GeV. 1 1 Results for the T5qqqqWW model are shown in Fig. 7 as a function of the gluino and LSP masses, with two different assumptions for the chargino mass: it is either assumed to Ibne tthhee afivrestracgaeseof(Fmigg.a7n-dlemftχ),10 ,thoer ietxicslusestiotno mlimχ10it+on20gGlueinVo. masses exceeds 1100 GeV for LSP masses up to 400 GeV; for larger LSP masses the limit is reduced to 830 GeV at mχ10 = 700 GeV. In the second case (Fig. 7-right), due to the tom left), and T5ttcc with mt1 = mχ10 + 20 GeV (bottom right). For a description of the notation, see Fig. 4 smaller mass difference, leptons in the final state are soft and thus the sensitivity is reduced. The results of the search are also used to set 95 % CL upper limits on the double tt production cross section, whose SM value computed at NLO precision [ 37 ] is 9.1 fb. The upper limit on σ (pp → tttt) is found to be 119 fb, with an expected result of 102−+3557 fb. With the current integrated luminosity, the sensitivity to this signature is limited by the statistical precision. Limits at the 95 % CL on the SS top quark pair production cross section are determined using events that satisfy the baseline selection categorized according to number of b jets (Fig. 2-bottom right); apart from the charge requirement, the detector acceptance and the selection efficiency for the signal are assumed to match those of SM tt events. The observed ) V e G ( ±∼χ11 000 m 800 600 400 200 Fig. 6 Exclusion regions at the 95 % CL in the plane of mχ1± versus mb1 for the T6ttWW model with mχ10 = 50 GeV. For a description of the notation, see Fig. 4 (expected) upper limit on σ (pp → tt) + σ (pp → tt) is 1.7 pb (1.5+−00..74 pb). Finally, we report model independent limits on the product of cross section, detector acceptance, and selection efficiency, σA , for the production of an SS dilepton pair in the two inclusive HH regions, SR31 and SR32, using the CLs criterion without the asymptotic approximation. In SR31 the limit is computed as a function of the minimum threshold on E miss for HT > 300 GeV, while in SR32 it is computed as a T function of the HT threshold for 50 < E miss < 300 GeV. The T results are shown in Fig. 8, where, in regions with no observed events, the minimum limit value of 1.3 fb is obtained. These limits can be used to test additional BSM models, after ) CMS eV1400 pp → ~g~g, ~g→ qq'Wχ∼01 G 0( 1 m∼χ1200 0 600 800 1000 1200 1400 1600 m~g (GeV) 800 1000 1200 1400 1600 m~g (GeV) Fmiχg1±. 7= Emxχc10lu+si2o0n GreegVio(nrsigahttt)h.eFo9r5a%deCscLriipntitohne opflathnee nooftmatχio10nv,esreseuFsimg.g4for the T5qqqqWW model with mχ1± = 0.5(mg + mχ10 ) (left) and with accounting for the event selection efficiency. The lepton efficiency ranges between 70–85 % (45–70 %) for generated muons (electrons) with |η| < 2.4 and pT > 25 GeV, increasing as a function of pT and converging to the maximum value for pT > 60 GeV; the efficiencies of the HT and E miss T requirements are mostly determined by the jet energy and E miss resolutions, which are discussed in Refs. [33,35]. T 8 Summary The results of a search for new physics in same-sign dilepton events using the CMS detector at the LHC and based on a data sample of pp collisions at √s = 13 TeV, corresponding to an integrated luminosity of 2.3 fb−1, are presented. The data are analyzed in nonoverlapping signal regions defined with different selections on lepton and event kinematic variables, as well as jet and b quark jet multiplicities. No significant deviation from the standard model expectations is observed. The results are used to set limits on the production of supersymmetric particles in various simplified models. Gluino and bottom squark masses are excluded at the 95 % confidence level up to 1300 and 680 GeV, respectively. These results extend the limits obtained in the previous version of the analysis [ 23 ] by about 250 GeV on the gluino mass, and 150 GeV on the bottom squark mass. In addition, 95 % confidence level upper limits of 119 fb and 1.7 pb are set on the cross sections for the production of two top quark-antiquark pairs and for the production of two SS top quarks, respectively. Model independent limits and selection efficiencies are provided to allow further interpretations of the results, using alternative models to those examined here. ) CMS eV1400 pp → ~g~g, ~g→ qq'Wχ∼01 G 0( 1 m∼χ1200 10-2 1000 800 600 400 200 5 ) b f ( L 4.5 C % 4 5 t9 3.5 a itm 3 li εA 2.5 σ 2 1 CMS Model independent σAε exclusion limit Fig. 8 Limits on the product of cross section, detector acceptance, and selection efficiency, σA , for the production of an SS dilepton pair as a function of E miss in HH SR31 (left) and of HT in HH SR32 (right) T Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); MoER, ERC IUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia); MESTD (Serbia); SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie programme and the European Research Council and EPLANET (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWTBelgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS programme of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund; the Mobility Plus programme of the Ministry of Science and Higher Education (Poland); the OPUS programme of the National Science Center (Poland); MIUR project 20108T4XTM (Italy); the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF; the National Priorities Research Program by Qatar National Research Fund; the Programa Clarín-COFUND del Principado de Asturias; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); and the Welch Foundation, contract C-1845. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm ons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funded by SCOAP3. CMS Collaboration Yerevan Physics Institute, Yerevan, Armenia V. Khachatryan, A. M. Sirunyan, A. Tumasyan National Centre for Particle and High Energy Physics, Minsk, Belarus V. Mossolov, N. Shumeiko, J. Suarez Gonzalez Université de Mons, Mons, Belgium N. Beliy Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil W. L. Aldá Júnior, F. L. Alves, G. A. Alves, L. Brito, C. Hensel, A. Moraes, M. E. Pol, P. Rebello Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato4, A. Custódio, E. M. 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Wang, Z. Xu Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia N. Godinovic, D. Lelas, I. Puljak, P. M. Ribeiro Cipriano Institute Rudjer Boskovic, Zagreb, Croatia V. Brigljevic, D. Ferencek, K. Kadija, S. Micanovic, L. Sudic Charles University, Prague, Czech Republic M. Finger7, M. Finger Jr.7 Universidad San Francisco de Quito, Quito, Ecuador E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt S. Elgammal8, A. Mohamed9, Y. Mohammed10, E. Salama8,11 National Institute of Chemical Physics and Biophysics, Tallinn, Estonia B. Calpas, M. Kadastik, M. Murumaa, L. Perrini, M. Raidal, A. Tiko, C. Veelken Department of Physics, University of Helsinki, Helsinki, Finland P. Eerola, J. Pekkanen, M. Voutilainen Helsinki Institute of Physics, Helsinki, Finland J. Härkönen, V. Karimäki, R. Kinnunen, T. Lampén, K. Lassila-Perini, S. 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Katkov14, P. Lobelle Pardo, B. Maier, H. Mildner, M. U. Mozer, T. Müller, Th. Müller, M. Plagge, G. Quast, K. Rabbertz, S. Röcker, F. Roscher, M. Schröder, G. Sieber, H. J. Simonis, R. Ulrich, J. Wagner-Kuhr, S. Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece G. Anagnostou, G. Daskalakis, T. Geralis, V. A. Giakoumopoulou, A. Kyriakis, D. Loukas, I. Topsis-Giotis National and Kapodistrian University of Athens, Athens, Greece A. Agapitos, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary N. Filipovic Institute of Nuclear Research ATOMKI, Debrecen, Hungary N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi University of Debrecen, Debrecen, Hungary M. Bartók19, P. Raics, Z. L. Trocsanyi, B. Ujvari National Institute of Science Education and Research, Bhubaneswar, India S. Bahinipati, S. Choudhury21, P. Mal, K. Mandal, A. Nayak22, D. K. Sahoo, N. Sahoo, S. K. Swain Indian Institute of Technology Madras, Madras, India P. K. Behera Bhabha Atomic Research Centre, Mumbai, India R. Chudasama, D. Dutta, V. Jha, V. Kumar, A. K. Mohanty13, P. K. Netrakanti, L. M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research, Mumbai, India S. Bhowmik23, R. K. Dewanjee, S. Ganguly, S. Kumar, M. Maity23, B. Parida, T. Sarkar23 Tata Institute of Fundamental Research-A, Mumbai, India T. Aziz, S. Dugad, G. Kole, B. Mahakud, S. Mitra, G. B. Mohanty, N. Sur, B. Sutar Tata Institute of Fundamental Research-B, Mumbai, India S. Banerjee, M. Guchait, Sa. Jain, G. Majumder, K. Mazumdar, N. Wickramage24 Indian Institute of Science Education and Research (IISER), Pune, India S. Chauhan, S. Dube, A. Kapoor, K. Kothekar, A. Rane, S. Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran H. Behnamian, S. Chenarani25, E. Eskandari Tadavani, S. M. Etesami25, A. Fahim26, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi, F. Rezaei Hosseinabadi, B. Safarzadeh27, M. Zeinali University College Dublin, Dublin, Ireland M. Felcini, M. Grunewald INFN Sezione di Baria , Università di Barib, Politecnico di Baric, Bari, Italy M. Abbresciaa,b, C. Calabriaa,b, C. Caputoa,b, A. Colaleoa , D. Creanzaa,c, L. Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, L. Fiorea , G. Iasellia,c, G. Maggia,c, M. Maggia , G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R. Radognaa,b, A. Ranieria , G. Selvaggia,b, L. Silvestrisa,13, R. Vendittia,b, P. Verwilligena INFN Sezione di Bolognaa , Università di Bolognab, Bologna, Italy G. Abbiendia , C. Battilana, D. Bonacorsia,b, S. Braibant-Giacomellia,b, L. Brigliadoria,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F. R. Cavalloa , S. S. Chhibraa,b, G. Codispotia,b, M. Cuffiania,b, G. M. Dallavallea , F. Fabbria , A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia , C. Grandia , L. Guiduccia,b, S. Marcellinia , G. Masettia , A. Montanaria , F. L. Navarriaa,b, A. Perrottaa , A. M. Rossia,b, T. Rovellia,b, G. P. Sirolia,b, N. Tosia,b,13 INFN Sezione di Cataniaa , Università di Cataniab, Catania, Italy S. Albergoa,b, M. Chiorbolia,b, S. Costaa,b, A. Di Mattiaa , F. Giordanoa,b, R. Potenzaa,b, A. Tricomia,b, C. Tuvea,b INFN Laboratori Nazionali di Frascati, Frascati, Italy L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera13 INFN Sezione di Genovaa , Università di Genovab, Genoa, Italy V. Calvellia,b, F. Ferroa , M. Lo Veterea,b, M. R. Mongea,b, E. Robuttia , S. Tosia,b INFN Sezione di Padovaa , Università di Padovab, Padua, Italy, Università di Trentoc, Trento, Italy P. Azzia,13, N. Bacchettaa , L. Benatoa,b, D. Biselloa,b, A. Bolettia,b, R. Carlina,b, A. Carvalho Antunes De Oliveiraa,b, INFN Sezione di Pisaa , Università di Pisab, Scuola Normale Superiore di Pisac, Pisa, Italy K. Androsova,28, P. Azzurria,13, G. Bagliesia , J. Bernardinia , T. Boccalia , R. Castaldia , M. A. Cioccia,28, R. Dell’Orsoa , S. Donatoa,c, G. Fedi, A. Giassia , M. T. Grippoa,28, F. Ligabuea,c, T. Lomtadzea , L. Martinia,b, A. Messineoa,b, F. Pallaa , A. Rizzia,b, A. Savoy-Navarroa,29, P. Spagnoloa , R. Tenchinia , G. Tonellia,b, A. Venturia , P. G. Verdinia INFN Sezione di Triestea , Università di Triesteb, Trieste, Italy S. Belfortea , M. Casarsaa , F. Cossuttia , G. Della Riccaa,b, C. La Licataa,b, A. Schizzia,b, A. Zanettia Kyungpook National University, Daegu, Korea D. H. Kim, G. N. Kim, M. S. Kim, S. Lee, S. W. Lee, Y. D. Oh, S. Sekmen, D. C. Son, Y. C. Yang Chonbuk National University, Jeonju, Korea A. Lee Hanyang University, Seoul, Korea J. A. Brochero Cifuentes, T. J. Kim Seoul National University, Seoul, Korea J. Almond, J. Kim, S. B. Oh, S. H. Seo, U. K. Yang, H. D. Yoo, G. B. Yu University of Seoul, Seoul, Korea M. Choi, H. Kim, H. Kim, J. H. Kim, J. S. H. Lee, I. C. Park, G. Ryu, M. S. Ryu Sungkyunkwan University, Suwon, Korea Y. Choi, J. Goh, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania V. Dudenas, A. Juodagalvis, J. Vaitkus Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz32, A. Hernandez-Almada, R. Lopez-Fernandez, J. Mejia Guisao, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City, Mexico S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita Universidad Autonoma de Puebla, Puebla, Mexico S. Carpinteyro, I. Pedraza, H. A. Salazar Ibarguen, C. Uribe Estrada Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico A. Morelos Pineda University of Auckland, Auckland, New Zealand D. Krofcheck University of Canterbury, Christchurch, New Zealand P. H. Butler National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan A. Ahmad, M. Ahmad, Q. Hassan, H. R. Hoorani, W. A. Khan, M. A. Shah, M. Shoaib, M. Waqas Petersburg Nuclear Physics Institute, Gatchina, St. Petersburg, Russia L. Chtchipounov, V. Golovtsov, Y. Ivanov, V. Kim36, E. Kuznetsova37, V. Murzin, V. Oreshkin, V. Sulimov, A. Vorobyev National Research Nuclear University ‘Moscow Engineering Physics Institute’ (MEPhI), Moscow, Russia M. Chadeeva38, M. Danilov38, O. Markin P. N. Lebedev Physical Institute, Moscow, Russia V. Andreev, M. Azarkin35, I. Dremin35, M. Kirakosyan, A. Leonidov35, S. V. Rusakov, A. Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia A. Baskakov, A. Belyaev, E. Boos, M. Dubinin39, L. Dudko, A. Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S. Petrushanko, V. Savrin, A. Snigirev Faculty of Physics and Vinca Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia P. Adzic40, P. Cirkovic, D. Devetak, J. Milosevic, V. Rekovic Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain J. Alcaraz Maestre, E. Calvo, M. Cerrada, M. Chamizo Llatas, N. Colino, B. De La Cruz, A. Delgado Peris, A. Escalante Del Valle, C. Fernandez Bedoya, J. P. Fernández Ramos, J. Flix, M. C. Fouz, P. Garcia-Abia, O. Gonzalez Lopez, S. Goy Lopez, J. M. Hernandez, M. I. Josa, E. Navarro De Martino, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo, A. Quintario Olmeda, I. Redondo, L. Romero, M. S. Soares Universidad Autónoma de Madrid, Madrid, Spain J. F. de Trocóniz, M. Missiroli, D. Moran Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain I. J. Cabrillo, A. Calderon, J. R. Castiñeiras De Saa, E. Curras, M. Fernandez, J. Garcia-Ferrero, G. Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, F. Matorras, J. Piedra Gomez, T. Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte CERN, European Organization for Nuclear Research, Geneva, Switzerland D. Abbaneo, E. Auffray, G. Auzinger, M. Bachtis, P. Baillon, A. H. Ball, D. Barney, P. Bloch, A. Bocci, A. Bonato, C. Botta, T. Camporesi, R. Castello, M. Cepeda, G. Cerminara, M. D’Alfonso, D. d’Enterria, A. Dabrowski, V. Daponte, A. David, M. De Gruttola, F. De Guio, A. De Roeck, E. Di Marco41, M. Dobson, M. Dordevic, B. Dorney, T. du Pree, D. Duggan, M. Dünser, N. Dupont, A. Elliott-Peisert, S. Fartoukh, G. Franzoni, J. Fulcher, W. Funk, D. Gigi, K. Gill, M. Girone, F. Glege, D. Gulhan, S. Gundacker, M. Guthoff, J. Hammer, P. Harris, J. Hegeman, V. Innocente, P. Janot, H. Kirschenmann, V. Knünz, A. Kornmayer13, M. J. Kortelainen, K. Kousouris, M. Krammer1, P. Lecoq, C. Lourenço, M. T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F. Meijers, S. Mersi, E. Meschi, F. Moortgat, S. Morovic, M. Mulders, H. Neugebauer, S. Orfanelli42, L. Orsini, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, A. Racz, T. Reis, G. Rolandi43, M. Rovere, M. Ruan, H. Sakulin, J. B. Sauvan, C. Schäfer, C. Schwick, M. Seidel, A. Sharma, P. Silva, M. Simon, P. Sphicas44, J. Steggemann, M. Stoye, Y. Takahashi, M. Tosi, D. Treille, A. Triossi, A. Tsirou, V. Veckalns45, G. I. Veres19, N. Wardle, A. Zagozdzinska33, W. D. Zeuner National Taiwan University (NTU), Taipei, Taiwan Arun Kumar, P. Chang, Y. H. Chang, Y. W. Chang, Y. Chao, K. F. Chen, P. H. Chen, C. Dietz, F. Fiori, W.-S. Hou, Y. Hsiung, Y. F. Liu, R.-S. Lu, M. Miñano Moya, E. Paganis, A. Psallidas, J. F. Tsai, Y. M. Tzeng Department of Physics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand B. Asavapibhop, G. Singh, N. Srimanobhas, N. Suwonjandee Physics Department, Middle East Technical University, Ankara, Turkey B. Bilin, S. Bilmis, B. Isildak52, G. Karapinar53, M. Yalvac, M. Zeyrek Bogazici University, Istanbul, Turkey E. Gülmez, M. Kaya54, O. Kaya55, E. A. Yetkin56, T. Yetkin57 Istanbul Technical University, Istanbul, Turkey A. Cakir, K. Cankocak, S. Sen58 Institute for Scintillation Materials of National Academy of Science of Ukraine, Kharkov, Ukraine B. Grynyov National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov, Ukraine L. Levchuk, P. Sorokin Rutherford Appleton Laboratory, Didcot, UK K. W. Bell, A. Belyaev60, C. Brew, R. M. Brown, L. Calligaris, D. Cieri, D. J. A. Cockerill, J. A. Coughlan, K. Harder, S. Harper, E. Olaiya, D. Petyt, C. H. Shepherd-Themistocleous, A. Thea, I. R. Tomalin, T. Williams Brunel University, Uxbridge, UK J. E. Cole, P. R. Hobson, A. Khan, P. Kyberd, D. Leslie, I. D. Reid, P. Symonds, L. Teodorescu, M. Turner Baylor University, Waco, USA A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, N. Pastika The University of Alabama, Tuscaloosa, USA O. Charaf, S. I. Cooper, C. Henderson, P. Rumerio University of California, Santa Barbara, Santa Barbara, USA N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta, K. Flowers, M. Franco Sevilla, P. Geffert, C. George, F. Golf, L. Gouskos, J. Gran, R. Heller, J. Incandela, N. Mccoll, S. D. Mullin, A. Ovcharova, J. Richman, D. Stuart, I. Suarez, C. West, J. Yoo Carnegie Mellon University, Pittsburgh, USA M. B. Andrews, V. Azzolini, B. Carlson, T. Ferguson, M. Paulini, J. Russ, M. Sun, H. Vogel, I. Vorobiev University of Colorado Boulder, Boulder, USA J. P. Cumalat, W. T. Ford, F. Jensen, A. Johnson, M. Krohn, T. Mulholland, K. Stenson, S. R. Wagner Fairfield University, Fairfield, USA D. Winn Florida International University, Miami, USA S. Linn, P. Markowitz, G. Martinez, J. L. Rodriguez Florida Institute of Technology, Melbourne, USA M. M. Baarmand, V. Bhopatkar, S. Colafranceschi64, M. Hohlmann, D. Noonan, T. Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA M. R. Adams, L. Apanasevich, D. Berry, R. R. Betts, I. Bucinskaite, R. Cavanaugh, O. Evdokimov, L. Gauthier, C. E. Gerber, D. J. Hofman, P. Kurt, C. O’Brien, I. D. Sandoval Gonzalez, P. Turner, N. Varelas, H. Wang, Z. Wu, M. Zakaria, J. Zhang Johns Hopkins University, Baltimore, USA I. Anderson, B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A. V. Gritsan, P. Maksimovic, M. Osherson, J. Roskes, U. Sarica, M. Swartz, M. Xiao, Y. Xin, C. You Kansas State University, Manhattan, USA A. Ivanov, K. Kaadze, S. Khalil, M. Makouski, Y. Maravin, A. Mohammadi, L. K. Saini, N. Skhirtladze, S. Toda Lawrence Livermore National Laboratory, Livermore, USA D. Lange, F. Rebassoo, D. Wright University of Maryland, College Park, USA C. Anelli, A. Baden, O. Baron, A. Belloni, B. Calvert, S. C. Eno, C. Ferraioli, J. A. Gomez, N. J. Hadley, S. Jabeen, R. G. Kellogg, T. Kolberg, J. Kunkle, Y. Lu, A. C. Mignerey, Y. H. Shin, A. Skuja, M. B. Tonjes, S. C. Tonwar Massachusetts Institute of Technology, Cambridge, USA D. Abercrombie, B. Allen, A. Apyan, R. Barbieri, A. Baty, R. Bi, K. Bierwagen, S. Brandt, W. Busza, I. A. Cali, Z. Demiragli, L. Di Matteo, G. Gomez Ceballos, M. Goncharov, D. Hsu, Y. Iiyama, G. M. Innocenti, M. Klute, D. Kovalskyi, K. Krajczar, Y. S. Lai, Y.-J. Lee, A. Levin, P. D. Luckey, A. C. Marini, C. Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, J. Salfeld-Nebgen, G. S. F. Stephans, K. Sumorok, K. Tatar, M. Varma, D. Velicanu, J. Veverka, J. Wang, T. W. Wang, B. Wyslouch, M. Yang, V. Zhukova University of Mississippi, Oxford, USA J. G. Acosta, S. Oliveros The Ohio State University, Columbus, USA J. Alimena, L. Antonelli, J. Brinson, B. Bylsma, L. S. Durkin, S. Flowers, B. Francis, A. Hart, C. Hill, R. Hughes, W. Ji, B. Liu, W. Luo, D. Puigh, B. L. Winer, H. W. Wulsin University of Puerto Rico, Mayaguez, USA S. Malik Purdue University, West Lafayette, USA A. Barker, V. E. Barnes, D. Benedetti, S. Folgueras, L. Gutay, M. K. Jha, M. Jones, A. W. Jung, K. Jung, D. H. Miller, N. Neumeister, B. C. Radburn-Smith, X. Shi, J. Sun, A. Svyatkovskiy, F. Wang, W. Xie, L. Xu Purdue University Calumet, Hammond, USA N. Parashar, J. Stupak University of Rochester, Rochester, USA B. Betchart, A. Bodek, P. de Barbaro, R. Demina, t. Duh, Y. t. Ferbel, M. Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K. H. Lo, P. Tan, M. Verzetti Rutgers, The State University of New Jersey, Piscataway, USA J. P. Chou, E. Contreras-Campana, Y. Gershtein, T. A. Gómez Espinosa, E. Halkiadakis, M. Heindl, D. Hidas, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath, K. Nash, H. Saka, S. Salur, S. Schnetzer, D. Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker University of Tennessee, Knoxville, USA M. Foerster, J. Heideman, G. Riley, K. Rose, S. Spanier, K. Thapa Wayne State University, Detroit, USA C. Clarke, R. Harr, P. E. Karchin, P. Lamichhane, J. Sturdy University of Wisconsin-Madison, Madison, WI, USA D. A. Belknap, S. Dasu, L. Dodd, S. Duric, B. Gomber, M. Grothe, M. Herndon, A. Hervé, P. Klabbers, A. Lanaro, A. Levine, K. Long, R. Loveless, I. Ojalvo, T. Perry, G. A. Pierro, G. Polese, T. Ruggles, A. Savin, A. Sharma, N. Smith, W. H. Smith, D. Taylor, N. Woods † Deceased 1: Also at Vienna University of Technology, Vienna, Austria 2: Also at State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China 3: Also at Institut Pluridisciplinaire Hubert Curien, Université de Strasbourg, Université de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France 4: Also at Universidade Estadual de Campinas, Campinas, Brazil 5: Also at Université Libre de Bruxelles, Bruxelles, Belgium 6: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany 7: Also at Joint Institute for Nuclear Research, Dubna, Russia 8: Now at British University in Egypt, Cairo, Egypt 9: Also at Zewail City of Science and Technology, Zewail, Egypt 10: Now at Fayoum University, El-Fayoum, Egypt 11: Now at Ain Shams University, Cairo, Egypt 12: Also at Université de Haute Alsace, Mulhouse, France 13: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland 14: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia 15: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany 16: Also at University of Hamburg, Hamburg, Germany 17: Also at Brandenburg University of Technology, Cottbus, Germany 18: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary 19: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary 20: Also at University of Debrecen, Debrecen, Hungary 21: Also at Indian Institute of Science Education and Research, Bhopal, India 22: Also at Institute of Physics, Bhubaneswar, India 23: Also at University of Visva-Bharati, Santiniketan, India 24: Also at University of Ruhuna, Matara, Sri Lanka 25: Also at Isfahan University of Technology, Isfahan, Iran 26: Also at University of Tehran, Department of Engineering Science, Tehran, Iran 27: Also at Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran 28: Also at Università degli Studi di Siena, Siena, Italy 29: Also at Purdue University, West Lafayette, USA 30: Also at International Islamic University of Malaysia, Kuala Lumpur, Malaysia 31: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia 32: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico 33: Also at Warsaw University of Technology, Institute of Electronic Systems, Warsaw, Poland 34: Also at Institute for Nuclear Research, Moscow, Russia 35: Now at National Research Nuclear University ‘Moscow Engineering Physics Institute’ (MEPhI), Moscow, Russia 36: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia 37: Also at University of Florida, Gainesville, USA 38: Also at P.N. Lebedev Physical Institute, Moscow, Russia 39: Also at California Institute of Technology, Pasadena, USA 40: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia 41: Also at INFN Sezione di Roma Università di Roma, Rome, Italy 42: Also at National Technical University of Athens, Athens, Greece 43: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy 44: Also at National and Kapodistrian University of Athens, Athens, Greece 45: Also at Riga Technical University, Riga, Latvia 46: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia 1. R.M. Barnett , J.F. Gunion , H.E. Haber , Discovering supersymmetry with like-sign dileptons . Phys. Lett. B 315 , 349 ( 1993 ). doi: 10 . 1016/ 0370 - 2693 ( 93 ) 91623 - U . arXiv: hep-ph/9306204 2. M. Guchait , D.P. Roy , Like-sign dilepton signature for gluino production at CERN LHC including top quark and Higgs boson effects . Phys. Rev. D 52 , 133 ( 1995 ). doi:10.1103/PhysRevD.52. 133. arXiv:hep-ph/9412329 3. 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V. Khachatryan, A. M. Sirunyan, A. Tumasyan, W. Adam. Search for new physics in same-sign dilepton events in proton–proton collisions at \(\sqrt{s} = 13\,\text {TeV} \), The European Physical Journal C, 2016, 439, DOI: 10.1140/epjc/s10052-016-4261-z