Physics Letters B 782 (2018) 440–467 Contents lists available at ScienceDirect Physics Letters B www.elsevier.com/locate/physletb Search for new physics in events with two soft oppositely charged leptons and missing transverse momentum in proton–proton collisions at √ s = 13 TeV .The CMS Collaboration � CERN, Switzerland a r t i c l e i n f o a b s t r a c t Article history: Received 5 January 2018 Received in revised form 1 May 2018 Accepted 15 May 2018 Available online 25 May 2018 Editor: M. Doser Keywords: CMS SUSY Compressed Leptons Missing energy A search is presented for new physics in events with two low-momentum, oppositely charged leptons (electrons or muons) and missing transverse momentum in proton-proton collisions at a centre-of-mass energy of 13 TeV. The data collected using the CMS detector at the LHC correspond to an integrated luminosity of 35.9 fb−1. The observed event yields are consistent with the expectations from the standard model. The results are interpreted in terms of pair production of charginos and neutralinos (χ̃± 1 and χ̃0 2 ) with nearly degenerate masses, as expected in natural supersymmetry models with light higgsinos, as well as in terms of the pair production of top squarks (̃t), when the lightest neutralino and the top squark have similar masses. At 95% confidence level, wino-like χ̃± 1 /χ̃0 2 masses are excluded up to 230 GeV for a mass difference of 20 GeV relative to the lightest neutralino. In the higgsino-like model, masses are excluded up to 168 GeV for the same mass difference. For ̃t pair production, top squark masses up to 450 GeV are excluded for a mass difference of 40 GeV relative to the lightest neutralino. © 2018 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3. 1. Introduction Supersymmetry (SUSY) [1–5] is a widely considered extension of the standard model (SM) of particle physics, as it can provide solutions to several open questions in the SM, in particular those related to the hierarchy problem [6–8] and the nature of dark mat- ter. SUSY predicts superpartners of SM particles whose spins differ by one-half unit with respect to their SM partners. In R-parity con- serving models [9], SUSY particles are pair-produced and their de- cay chains end in the stable, lightest SUSY particle (LSP), which in many models corresponds to the lightest neutralino (χ̃0 1 ). A stable LSP would escape undetected, yielding a characteristic signature of a large magnitude of missing transverse momentum (pmiss T ) in col- lisions at the CERN LHC. As a stable, neutral and weakly interacting particle, the neutralino matches the properties required of a dark matter candidate [10]. The absence of SUSY signals in previous experiments, as well as at the LHC, can be interpreted as an indication that SUSY par- ticles have very large mass, leading to the expectation that SUSY events have large visible energy and momentum. As a result, the many searches that yield the most stringent limits on the masses � E-mail address: cms -publication -committee -chair @cern .ch. of the SUSY particles are based on events with large pmiss T and energetic final-state objects such as leptons and jets. Another in- terpretation for the absence of a SUSY signal is that the SUSY particles are in a part of the parameter space that is not easily ac- cessible. One such scenario, where previously mentioned searches would not be sensitive, is where the mass spectrum is compressed, i.e. the mass splitting between the produced SUSY particles and the LSP is small. When the mass splittings between SUSY particles are small, the visible energy in the event, and also potentially the pmiss T , is relatively low, which motivates searches in events with low-momentum objects. Compressed mass spectra arise in several SUSY models, in- cluding natural SUSY, i.e. SUSY models that solve the hierarchy problem with little fine tuning. It has been pointed out in sev- eral studies, for example in Refs. [6–8,11–15], that naturalness imposes constraints on the masses of higgsinos, top squarks, and gluinos. Natural SUSY is generally considered to require at least one coloured SUSY particle of mass below approximately one TeV. Further, it is often assumed that this particle is the top squark (̃t). More recently, however, the hypothesis of natural SUSY requiring a light top squark has been disputed as arising from oversimpli- fied assumptions [16–18]. Irrespective of the top squark, higgsinos remain a complementary window to natural SUSY as they are gen- erally expected to be light. As pointed out in Refs. [19–22], light https://doi.org/10.1016/j.physletb.2018.05.062 0370-2693/© 2018 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3. https://doi.org/10.1016/j.physletb.2018.05.062 http://www.ScienceDirect.com/ http://www.elsevier.com/locate/physletb http://creativecommons.org/licenses/by/4.0/ mailto:cms-publication-committee-chair@cern.ch https://doi.org/10.1016/j.physletb.2018.05.062 http://creativecommons.org/licenses/by/4.0/ http://crossmark.crossref.org/dialog/?doi=10.1016/j.physletb.2018.05.062&domain=pdf The CMS Collaboration / Physics Letters B 782 (2018) 440–467 441 higgsinos are likely to have a compressed mass spectrum, poten- tially leading to signatures with soft leptons and moderate pmiss T . Thus far, the most sensitive searches in this model have been car- ried out by experiments at LEP [23,24] and ATLAS [25]. The LEP experiments excluded χ̃± 1 masses up to 103.5 GeV for a mass split- ting between the χ̃± 1 and χ̃0 1 of at least 3 GeV. The search described in this letter is designed for neutralinos and charginos, which are collectively referred to as “electroweaki- nos”, in a model where these electroweakinos form a compressed mass spectrum [19,21,22,26]. Two models are considered where the electroweakinos are either pure wino/bino-like or where the lightest electroweakinos are of mostly higgsino nature. The search has discovery potential also when a light top squark and the LSP are nearly degenerate in mass and the top squark decays to four fermions. A more detailed discussion of such models can be found in Ref. [27]. The near-degeneracy in mass of the top squark and the LSP is typical of the so-called “co-annihilation region”, in which the LSP is the sole source of dark matter [28]. In the models considered in this analysis, the visible decay products in the SUSY signal have low momentum, which can be distinguished from SM processes when a jet with large transverse momentum (pT) from initial-state radiation (ISR) leads to a large boost of the SUSY particle pair. This boost also enhances the pmiss T in the event. A similar search has previously been reported by the ATLAS Collaboration [25]. For the signal studied in this letter, SUSY particles can decay leptonically, and the presence of low-pT lep- tons can be used to discriminate against otherwise dominant SM backgrounds, such as multijet production through quantum chro- modynamics (QCD) and Z + jets events with invisible Z boson de- cays. The current strategy is similar to that in the previous publica- tion based on 8 TeV data [29], with the main difference being the deployment of a new trigger selection that improves the sensitivity of the search in events with two muons and low pmiss T . In addition, the selection has further been optimized for electroweakinos with a compressed mass spectrum. At least one jet is required in the final state; in the case of the signal, this jet must arise from ISR, which provides the final-state particles with a boost in the trans- verse plane, and thereby the potential for moderate or large pmiss T in the event. Unlike the 8 TeV analysis, there is no upper limit on the number of jets in the event. 2. CMS detector The central feature of the CMS apparatus is a superconduct- ing solenoid of 6 m internal diameter, providing a magnetic field of 3.8 T. Within the solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass and scintillator hadron calorimeter (HCAL), each com- posed of a barrel and two endcap sections. Forward calorimeters extend the pseudorapidity (η) coverage provided by the barrel and endcap detectors. Muons are detected in gas-ionization chambers embedded in the steel flux-return yoke outside the solenoid. Events of interest are selected using a two-tiered trigger sys- tem [30]. The first level (L1), composed of custom hardware pro- cessors, uses information from the calorimeters and muon detec- tors to select events at a rate of around 100 kHz within a time interval of less than 4 μs. The second level, known as the high-level trigger (HLT), consists of a farm of processors running a version of the full event reconstruction software optimized for fast pro- cessing, and reduces the event rate to around 1 kHz before data storage. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kine- matic variables, can be found in Ref. [31]. 3. Data and simulated samples The data used in this search correspond to an integrated lumi- nosity of 35.9 fb−1 of proton–proton (pp) collisions at a centre-of- mass energy of 13 TeV, recorded in 2016 using the CMS detector. The data are selected using two triggers: an inclusive pmiss T trig- ger, which is used for signal regions (SRs) with an offline pmiss T cut > 200 GeV and an additional trigger which requires two muons to lower the offline pmiss T cut to 125 GeV. Both the muon pT and the muon pair pT have a trigger online cut of pT > 3 GeV. The inclusive pmiss T triggers correspond to an integrated luminosity of 35.9 fb−1, whereas the events recorded with the dimuon + pmiss T trigger cor- respond to 33.2 fb−1. Simulated signal and major background processes, such as tt, W + jets, and Z + jets are generated with the MadGraph5_ amc@nlo 2.2.2 [32,33] event generator at leading order (LO) pre- cision in perturbative QCD using the MLM merging scheme [34]. Additional partons are modelled in these samples. The diboson processes WW, ZZ, and Wγ are generated with the MadGraph5_ amc@nlo 2.2.2 event generator at next-to-leading order (NLO) pre- cision using the FxFx merging scheme [33], while the WZ process is generated at NLO with powheg v2.0 [35–39]. Rare background processes (e.g. ttW, ttZ, WWW, ZZZ, WZZ, and WWZ) are also gen- erated at NLO precision with MadGraph5_amc@nlo 2.2.2 (2.3.2.2 for ttZ) [32,33]. The rare background from single top quarks pro- duced in association with a W boson is generated at NLO precision with powheg v1.0 [40]. The NNPDF3.0 [41] LO and NLO parton distribution functions (PDF) are used for the simulated samples generated at LO and NLO. Showering, hadronization and the un- derlying event description are carried out using the pythia 8.212 package [42] with the CUETP8M1 underlying event tune [43,44]. A detailed simulation of the CMS detector is based on the Geant4 [45] package. A fast detector simulation [46] is used for the large number of signal samples, corresponding to different SUSY particle masses. The trigger, lepton identification, and b tagging efficiencies are corrected in the simulation through application of scale factors measured in dedicated data samples [47]. Corrections for the use of the fast detector simulation are also applied. For the signal, we consider the neutralino–chargino (χ̃0 2 –χ̃± 1 ) pair production where the mass degenerate χ̃0 2 and χ̃± 1 are as- sumed to decay to the LSP via virtual Z and W bosons. The decays of electroweakinos are carried out using pythia, assuming a con- stant matrix element. The SM branching fractions are assumed for the decays of the virtual Z and W bosons. The simulation of the χ̃0 2 (χ̃± 1 ) decay takes into account the Breit–Wigner shape of the Z (W) boson mass. The production cross sections corre- spond to those of pure wino production [48–50] computed at NLO plus next-to-leading-logarithmic (NLL) precision. A second mass scan simulates a simplified model of ̃ t-pair production, in which a heavy chargino mediates the decay of the t̃ into leptons and χ̃0 1 , namely t̃ → bχ̃± 1 → bW∗χ̃0 1 . The mass of the χ̃± 1 is set to (m̃t + mχ̃0 1 )/2, and the mass difference between ̃t and χ̃0 1 is set to be less than 80 GeV, thus b jets are expected to have a pT below 25 GeV. Fig. 1 shows diagrams for these two simplified models. We denote the upper diagram in Fig. 1 as TChi and the lower diagram as T2tt. The masses are given with the model name, i.e. TChi150/20 (T2tt150/20) denotes a χ̃0 2 -χ̃± 1 (̃t pair) production, where the pro- duced particles have a mass of 150 GeV and a mass difference to the LSP of 20 GeV. We interpret the results of this search in two variations of the electroweakino model. While the model described above uses pure wino cross sections with the χ̃0 2 and χ̃± 1 mass degenerate, these additional models resemble a scenario where the electroweaki- nos are of higgsino nature. The first of these higgsino simplified 442 The CMS Collaboration / Physics Letters B 782 (2018) 440–467 Fig. 1. Production and decay of an electroweakino pair (upper) and of a chargino- mediated ̃t pair (lower). models features associated χ̃0 2 and χ̃± 1 production and as such corresponds to the same diagram as the one shown in Fig. 1 (upper). The second higgsino model considers associated χ̃0 2 -χ̃0 1 production. In both cases, the mass of the chargino is given as mχ̃± 1 = (mχ̃0 2 + mχ̃0 1 )/2, and the χ̃0 2 decays via an off-shell Z bo- son, and if applicable, the χ̃± 1 decays via an off-shell W boson. The simplified models do not include any spin correlations in the decays. In the simplified higgsino model, this can lead to a differ- ent M(��) distribution that we do not account for. In addition to the electroweakino models, we interpret the results in a phenomenological minimal supersymmetric model (pMSSM) [51], in which the higgsino (μ), bino (M1), and wino (M2) mass parameters are varied. There is only a small depen- dency on tan β , which is set to 10. All other mass parameters are assumed to be decoupled. To reduce the parameter space to a two- dimensional grid, M2 is set to 2M1. This convention is inspired by electroweakino mass unification at the grand unified theory scale. Since the focus is on electroweak production only, the gluino mass parameter M3 is assumed to be decoupled. All trilinear couplings are discarded. In this model, the higgsino mass parameter μ is var- ied between 100 and 200 GeV, while M1 varies between 300 GeV and 1 TeV. Events for this “higgsino pMSSM” are generated with MadGraph5_amc@nlo [52]. The NLO cross sections are computed using Prospino 2 [53]. Several additional packages [54–58] are used to calculate mass spectra and particle decays. 4. Object reconstruction The analysis makes use of the particle-flow (PF) algorithm [59], which reconstructs and identifies each individual particle through an optimized combination of information from the various ele- ments of the CMS detector. The difficulties in reconstructing the event of interest, because of the presence of the large average number of interactions per bunch crossing (pileup), are mitigated by a primary vertex selection and other methods described be- low. The reconstructed vertex with the largest value of summed physics-object p2 T is taken to be the primary pp interaction vertex. The physics objects are the jets, clustered using the jet finding al- gorithm [60,61] with the tracks assigned to the vertex as inputs, and the associated pmiss T , taken as the negative vector pT sum of those jets. The leading and subleading muon (electron) are required to sat- isfy pT > 5 GeV, |η| < 2.4 (2.5). A requirement of pT < 30 GeV on the leptons is also applied; this threshold is identified as the pT value below which the current analysis is more sensitive in the compressed regions compared to other CMS analyses. To increase the sensitivity in the compressed mass regime, the lower thresh- old on the pT of the subleading muon is set to 3.5 GeV in the high-pmiss T regions of the ̃t search. Muons are required to satisfy standard identification crite- ria [62], and to be isolated within a cone in η–φ space of radius R = √ ( η)2 + ( φ)2 = 0.3: the pT sum of other charged par- ticle tracks within the cone, Isoabs, is required to be less than 5 GeV. In addition, the quantity Isorel, which is the ratio of Isoabs and the pT of the muon, is required to be less than 0.5. Contam- ination from pileup within the isolation cone is subtracted using techniques that utilize charged particle deposits within the cone itself [62]. Electrons from prompt decays are selected using a multivariate discriminant based on the energy distribution in the shower and track quality variables. The loose working point employed by the H → ZZ∗ → 4� analysis [63] is used for pT < 10 GeV, and a tighter one for pT > 10 GeV. The same definition of isolation and the same isolation criteria are applied for electrons as used for muons. To suppress nonprompt leptons, requirements on the three- dimensional impact parameter [64] relative to the primary vertex, IP3D, and its significance, SIP3D, are applied. Leptons are required to have IP3D < 0.01 cm and SIP3D < 2 standard deviations (s.d.). The combined efficiency for reconstruction, selection and isola- tion depends on the pT of the lepton. The efficiencies are in the range 70% (50%) for muons (electrons) at 5 GeV, up to 80% (60%) for muons (electrons) at 30 GeV. Jets are clustered using the anti-kT algorithm [60] with a dis- tance parameter of 0.4 [65], as implemented in the FastJet pack- age [61]. The momentum of a jet, which is determined by the vectorial sum of all particle momenta in the jet, is found from simulation to be within 5 to 10% of the true momentum over the full pT spectrum and detector acceptance. An offset correc- tion is applied to jet energies to take into account the contribution from pileup [66]. Jet energy corrections are obtained from simula- tion, and confirmed through in situ measurements of the energy balance in dijet and photon + jet events [67]. Jets are selected with pT > 25 GeV and |η| < 2.4. In the following, the transverse hadronic energy, HT, is defined as the scalar pT sum of the se- lected jets. Jets arising from the hadronization of b quarks are identi- fied through the combined secondary vertex (CSV) tagger [68,69], which employs both secondary vertex and track-based information. In this analysis, a loose working point corresponding to a b tagging efficiency of about 80% is used with misidentification rates of 10% and 40% for light-quark or gluon jets and for c quark jets, respec- tively [68]. The �p miss T is determined using the PF-reconstructed objects. A variety of event filters are applied to remove detector- and beam related noise [70]. 5. Event selection The analysis requires two oppositely charged leptons (N� = 2), of either same (ee, μμ) or different flavour (eμ), and moderate pmiss T in the final state, together with at least one jet in the event. The main backgrounds arise from events in which one of the leptons is not prompt (mainly from W + jets events), events from fully leptonic tt decays (tt(2�)), and Drell–Yan (DY) processes with subsequent decays γ /Z∗ → ττ → ��ν�ν�ντ ντ . Smaller back- grounds are from tW production (tW) and the diboson processes The CMS Collaboration / Physics Letters B 782 (2018) 440–467 443 WW and ZZ∗ , with Z∗ → �� and Z → νν (VV). Processes such as ttW, ttZ, WWW, ZZZ, WZZ and WWZ as well as processes in- cluding the Higgs boson have very small contributions, and are grouped together as “Rare”. The following event selection shown in Table 1 includes a number of requirements designed to reduce these backgrounds: • 0.6 < pmiss T /HT < 1.4: this criterion is effective in rejecting SM events comprised uniquely of jets produced through the strong interaction, referred to as QCD multijet events, while remain- ing efficient for events with ISR, as in the case of the signal. The bounds on the ratio pmiss T /HT is determined from a study of a control region (CR) at low-pmiss T and with dimuon mass close to that of the J/ψ meson. This requirement rejects such events while leaving the signal unaffected. • b jet event veto: requiring events where no jet is tagged as originating from b quarks significantly reduces the tt back- ground in which b jets originate from the decay of the top quarks. This requirement is applied to all jets with pT > 25 GeV and uses the b tagging selection criteria described in Section 4. The efficiency for a potential signal from ̃t decays is not affected significantly since in the compressed ̃t-LSP model, the b jets are expected to have small pT and are therefore not tagged. • M(ττ ) < 0 or M(ττ ) > 160 GeV: this requirement on the esti- mate of the ditau mass is designed to reject the large back- ground from Z → ττ decays, with the τ leptons decaying leptonically. The quantity M(ττ ) [22] is computed as follows: since the τ leptons from the decay of a Z boson have large pT compared to their mass, the direction of the outgoing lep- ton is approximately the same as that of the τ lepton (i.e. R(�, τ ) ≈ 0). The magnitudes of the lepton momentum vec- tors are then rescaled so that the lepton pair balances the hadronic recoil. For Z → ττ events, this leads to a fairly good approximation of the original τ momenta. The invariant mass of the two τ leptons, M(ττ ), is estimated by the invariant mass of the two scaled leptons. In some events, the estimate of the magnitude of the τ momentum results in a negative value when the flight direction is opposite to the direction of the lepton. In such cases, M(ττ ) is set to its negative value. • MT(�i, pmiss T ) < 70 GeV, for i = 1, 2: the transverse mass MT is defined as MT(�, pmiss T ) = √ 2p� T pmiss T ( 1 − cos [ φ ( �, pmiss T )]) , and �1 and �2 are the leading and subleading leptons, respec- tively. For the signal, the leading lepton is typically aligned with the boost direction of the LSP ( φ(�, pmiss T ) ≈ 0). This re- quirement is effective in further suppressing the tt background for the electroweakino search, but not for the ̃ t search. It is therefore only applied in the electroweakino search. • J/ψ , and ϒ veto: to suppress background contributions from J/ψ , low-mass γ ∗ , and ϒ decays, the dilepton invariant mass M(��) is required to satisfy M(��) > 4 GeV and to also lie out- side the range 9 < M(��) < 10.5 GeV. This veto is only applied to same flavour lepton pairs. • pmiss T > 125 GeV: to ensure high trigger efficiency, both the pmiss T and the muon corrected pmiss T , which is computed from the vectorial sum of the pmiss T and the pT of the muons se- lected in the event, is required to be larger than 125 GeV. The region 125 GeV < pmiss T < 200 GeV is only accessible by the dimuon trigger and therefore only dimuon pairs are consid- ered. The region pmiss T > 200 GeV includes also electrons. • Trigger acceptance: in the online selection, the lepton pair is required to have a small boost of pT > 3 GeV, together with an Table 1 Common selection requirements for the signal regions. The subleading lepton pT threshold is reduced to 3.5 GeV for muons in the high-pmiss T , ̃t-like signal region. Variable SR selection criteria N� 2 (μμ, μe, ee) q(�1)q(�2) −1 pT(�1), pT(�2) [5,30]GeV pT(μ2) for high-pmiss T t̃-like SR [3.5,30]GeV |ημ| <2.4 |ηe| <2.5 IP3D <0.01 cm SIP3D <2 Isorel(�1,2) <0.5 Isoabs(�1,2) <5 GeV pT(jet) >25 GeV |η|(jet) <2.4 Nb (pT >25 GeV, CSV) 0 M(��) [4,9] or [10.5,50]GeV (for μμ and ee) pT(��) >3 GeV pmiss T >125 GeV (for μμ) >200 GeV (for μe, ee) pmiss T (muon corrected) >125 GeV (for μμ) >200 GeV (for μe, ee) pmiss T /HT [0.6,1.4] HT >100 GeV M(ττ ) veto [0,160]GeV MT(�i , pmiss T ), i = 1,2 <70 GeV (electroweakino selection only) upper bound on the dimuon invariant mass M(��) < 60 GeV, to limit the trigger rate. To remain fully efficient after of- fline reconstruction, an upper bound of 50 GeV on M(��) and a lower requirement on the dilepton transverse momentum pT(��) > 3 GeV are imposed. • HT > 100 GeV: this requirement suppresses backgrounds with low hadronic activity in the event. For the selected events, a set of SRs are defined, based on the dilepton invariant mass and pmiss T . For events with leptons of same flavour and opposite charge, four SRs are defined in M(��) ranges of 4–9, 10.5–20, 20–30, and 30–50 GeV. These SRs are intended for searches for χ̃0 2 → Z∗χ̃0 1 events, where M(��) is related to the mass difference between the two electroweakinos. For events with leptons of different flavour and opposite charge, three SRs are defined in the leading lepton pT ranges of 5–12, 12–20, and 20–30 GeV. The definition of the bins of the SRs can be found in Table 2. To exploit the potential of the dimuon plus pmiss T trigger, events are separated according to the value of pmiss T : in total three ranges are used for the signal regions, namely pmiss T ∈ 125–200, 200–300, and >300 GeV for the ̃t search, and pmiss T ∈ 125–200, 200–250, and >250 GeV for the electroweakino search. Since the low-pmiss T re- gion contains events accessible only via the dimuon+ pmiss T trigger, only μμ pairs are considered. The muons need to be of oppo- site charge. Conversely, in the high-pmiss T regions, both electron and muon flavours are considered. The electroweakino SRs are populated by ee and μμ pairs, where the leptons are oppositely charged. For the ̃t SRs, eμ pairs are also considered. For the latter, the pT threshold on the trailing lepton is reduced to 3.5 GeV for muons in the high-pmiss T region to gain sensitivity in the search for t̃ signal. The acceptance times efficiency for the signal model TChi150/20 (T2tt350/330) in the electroweakino (stop) selection is between 3 × 10−5 (3 × 10−5) and 7 × 10−5 (15 × 10−5). The efficiency times acceptance for muons is about 2 to 5 times higher than for elec- trons in the electroweakino selection and about 1.5 to 3 times higher in the stop selection. 444 The CMS Collaboration / Physics Letters B 782 (2018) 440–467 Table 2 Definition of bins in the two SRs. The lowest pmiss T region includes only muon pairs, since it is only accessible by the dimuon trigger. Electroweakino search region t̃ search region pmiss T [GeV] M(��)[GeV] pmiss T [GeV] plepton T [GeV] [125, 200] [4,9] [125, 200] [5, 12] [12, 20] [20, 30] [10.5,20] [20,30] [30,50] [200, 250] [4,9] [200, 300] [5, 12] [12, 20] [20, 30] [10.5,20] [20,30] [30,50] >250 [4,9] >300 [5, 12] [12, 20] [20, 30] [10.5,20] [20,30] [30,50] Table 3 Summary of changes in selection criteria relative to Table 1 for CRs and the VV validation region (VR). DY CR tt (2�) CR VV VR No upper requirement on pT (�) Isorel < 0.1 as an or condition with the SR isolation 0 < M(ττ ) < 160 GeV IP3D < 0.0175 cm, SIP3D < 2.5 s.d. pT(�1) > 20 GeV, or IP3D > 0.01 cm, or SIP3D > 2 s.d. MT as for electroweakino SR No requirements on MT At least one b-tagged jet with pT > 40 GeV pT(�1) > 20 GeV |same flavour M(��) − M(Z)| > 10 GeV MT > 90 GeV 6. Background estimation Backgrounds with two prompt leptons are estimated using CRs chosen to be mostly free from signal but when possible, with sim- ilar kinematic characteristics as the events in the signal regions. Different CRs are employed for each SM process that contributes significantly to the signal region, i.e. the tt dilepton background and the DY + jets background. The normalization of the diboson background is cross checked in a validation region (VR). For each background, the number of events in each SR is esti- mated using the number of events observed in the corresponding CR, and a transfer factor that is used to describe the expected ratio of events in the SR and CR for the process in question. The trans- fer factor for a specific process, Fprocess, is determined from Monte Carlo (MC) simulation of the process through the ratio Fprocess = NSR MC process NCR MC process . Since a CR typically contains contributions from other physics processes, they need to be subtracted from the observed number of events in the CR, NCR data. These contributions, NCR MC other, are small compared to the main process for which the CR is defined, and are thus estimated using MC simulation. The estimate of the back- ground from a specific physics process in the SR is then given by NSR process = ( NCR data − NCR MC other ) Fprocess. Systematic uncertainties in the value of Fprocess are included when determining the full uncertainty in NSR process. The total background in the SR is given as the sum of the backgrounds expected from each process. The different CRs are split into two pmiss T bins: The low pmiss T bin with pmiss T between 125 and 200 GeV is used to constrain the SRs with the same pmiss T range, while the high pmiss T bin with pmiss T >200 GeV is used to constrain all SRs with pmiss T above 200 GeV. The shapes for M(��) and the lepton pT are taken directly from simulation. A summary of all CRs for prompt lepton backgrounds is given in Table 3. For the diboson background, a validation region enriched in VV (mainly WW events) is added. This region is used to establish how well the simulation agrees with data in order to validate the uncertainty assigned to the diboson simulation. About half of the events in this region stem from VV. 6.1. The DY + jets control region The main difference between the CR for the DY + jets back- ground and the SR lies in the requirement imposed on the Mττ variable; the CR consists of events that are vetoed in the SR se- lection, namely those events with Mττ in the range 0–160 GeV. To increase the efficiency for leptons from τ decays, the im- pact parameter requirements are relaxed to IP3D < 0.0175 cm and SIP3D < 2.5 s.d. The variation of the scale factors applied to sim- ulation by changing the cuts on IP3D and SIP3D was found to be negligible. In addition, the 30 GeV upper bound on the lepton pT is removed, and the region with lepton pT < 20 GeV, IP3D < 0.01 cm, and SIP3D < 2 is also removed to reduce the presence of potential signal. The distributions in kinematic quantities of these events, in- cluding the variables used to define the signal regions, M(��) and the leading lepton pT, are well described in simulation. The event yields estimated from simulation and the observed event yields are listed in Table 4. 6.2. The tt (2�) control region To obtain a sample enriched in tt events, at least one jet is required to be identified as originating from b quarks. To reduce potential signal contamination, the leading b-tagged jet is required to satisfy pT > 40 GeV. To increase the number of events in the CR, while still avoiding potentially large signal contamination, the upper bound on the lepton pT is also removed. The event yields estimated from simulation and the observed event yields are also shown in Table 4. The CMS Collaboration / Physics Letters B 782 (2018) 440–467 445 Table 4 Data and simulation yields for the DY and tt (2�) CRs, corresponding to integrated luminosities of 35.9 fb−1 (high-pmiss T region) and 33.2 fb−1 (low-pmiss T region). The SR scale factors are derived by subtracting the other processes from the observed data count, and dividing this number by the expected event yields from simulation for the process in question. The uncertainties are statistical only. pmiss T DY CR tt (2�) CR 125–200 GeV >200 GeV 125–200 GeV >200 GeV DY + jets or tt 70.1 ± 5.1 64.5 ± 3.3 1053.7 ± 9.4 535.7 ± 7.1 All SM processes 82.6 ± 5.5 75.2 ± 3.6 1170.0 ± 11.0 710.4 ± 11.1 Data 84 75 1157 680 SR scale factor 1.02 ± 0.13 0.99 ± 0.13 0.99 ± 0.03 0.94 ± 0.05 6.3. Nonprompt background The background from nonprompt or misidentified leptons is evaluated using a “tight-to-loose” method. Events where at least one lepton fails the tight identification and isolation criteria but passes a looser selection define the “application region”. Events in this region are weighted by a transfer factor based on the proba- bility that nonprompt leptons passing the loose requirements also satisfy the tight ones. The resulting estimate is corrected for the presence of prompt leptons in the application region. The probability for nonprompt or misidentified leptons to pass the tight selection criteria is referred to as the misidentification probability, which is determined as a function of lepton pT and η. This probability is measured using a dedicated data sample, the “measurement region” (MR), which is enriched in the background from SM events containing only jets produced via strong interac- tion, referred to as QCD multijet events. This method has been used in several multilepton analyses at CMS and is described in more detail in Ref. [71]. The MR is defined through the presence of one loose lepton, obtained by relaxing the isolation and im- pact parameter requirements, and through a jet with pT > 30 GeV, separated from the lepton by R > 0.7. For muons, events are se- lected through prescaled single-lepton triggers with no isolation requirements. For electrons, a mixture of prescaled jet triggers is used. The method includes a correction for the presence of prompt leptons in the MR, mostly due to W and Z boson production in as- sociation with jets. The probability for prompt leptons to pass the tight selection criteria is taken from simulation and is corrected with a data-to-simulation scale factor extracted from data enriched in Z → �� decays. In this analysis, the misidentification probability measured in QCD multijet events is applied to loosely identified leptons in events that are dominated by W + jets and tt production. The lat- ter can have both a different composition in terms of the flavour of the jets that give rise to the nonprompt leptons, as well as different kinematic properties, potentially resulting in a different effective misidentification probability. These effects are studied by com- paring the misidentification probabilities measured in simulated events of these two processes in the kinematic regions probed by this analysis. A closure test is then performed by applying the misidentification probability measured in the QCD simulated mul- tijet events to a sample of W + jets events. The yield of events passing the tight identification criteria is compared with the es- timate obtained by applying the misidentification probability to events in the application region. The method is found to be con- sistent within a level of <40%; this value is used as a systematic uncertainty in the estimate of the normalization of the reducible background. To further constrain the contribution of the nonprompt lepton background in the SR, a dedicated CR consisting of same-sign (SS) leptons is defined. Requiring the two lepton candidates to have the same sign increases significantly the probability that at least one of the two is a nonprompt or misidentified lepton. The SS CR is defined using the ̃t selection in the pmiss T > 200 GeV region, where Fig. 2. Same-sign CR for ̃t selection and pmiss T > 200 GeV. The distribution of the leading lepton pT is used as input to the final signal extraction. A signal from neutralino–chargino (χ̃0 2 –χ̃± 1 ) production is superimposed. the opposite charge requirement of the two leptons is modified to same-sign. In the SS CR, the prediction of the nonprompt lep- ton background is derived from the “tight-to-loose” method and agrees with the data. Fig. 2 shows the leading lepton pT distribu- tion in the SS CR. It also shows the near absence of a signal. The distribution of the leading lepton pT is used as input to the final fit that performs the signal extraction, as its constraining power is significant, given the significant uncertainty on the measured misidentification probability. 7. Systematic uncertainties This section summarizes the systematic uncertainties in the es- timate of the background from the various SM processes. For each source of systematic uncertainty, we present both the effect on the corresponding specific background and the overall effect on the to- tal background predictions are listed in Table 5. The uncertainty in the predicted nonprompt lepton background contains a statistical component due to the statistical uncertainty in the application region event yield, it ranges from 10% to 50%. When applied in the SR, the uncertainty is 4% to 20%. Another source of statistical uncertainty arises from limited statistics in data and simulation in the DY + jets and tt (2�) CRs. The effect on the predicted yields in the SR, obtained using the transfer fac- tor described in Section 6, is approximately 13% for the DY + jets background and 3% for the tt background. 446 The CMS Collaboration / Physics Letters B 782 (2018) 440–467 Table 5 Relative uncertainties in the final total background predictions for each individual systematic source of uncertainty. Systematic source of uncertainty Typical uncertainty (%) VV background normalization 3–25 Nonprompt lepton background normalization 4–20 DY + jets background normalization 4–20 tt background normalization 2–8 Rare background normalization 1–3 Jet energy scale 2–12 b tagging 2–6 Pileup 1–5 Lepton selection 1–4 Integrated luminosity 2.5 Trigger 1–2 tt modelling <1 For the tt background, we have considered a set of systematic uncertainties arising from the modelling of the kinematic distri- butions in the simulation of this process. The spin correlation of the top quarks has been varied by 20%, based on the ATLAS and CMS [72,73] measurements and a comparison between different generators (MadGraph5_amc@nlo versus powheg). The helicity amplitudes of the W boson in top quark decays have been varied by 5%. A top quark pT modelling uncertainty has also been derived by reweighting the simulated tt events based on the number of ISR jets (N ISR jets), so as to make the jet multiplicity agree with data. The reweighting factors range from 0.92 to 0.51 for N ISR jets between 1 and 6. The systematic uncertainty in these reweighting factors is taken to be equal to one half of the deviation of the factor from unity. The combined effect of this set of tt modelling uncertainties on the total number of predicted tt background events is found to be in the range 3–5%. For the DY + jets background, the uncertainty in the resolution of the pT of the system recoiling against the two leptons is ob- tained from data dominated by Z → μμ events. The uncertainty affects the DY estimate, which uses the efficiency of the require- ments on Mττ from simulation. The effect on the estimated yields of DY + jets is found to be negligible (<1%). As presented in Section 6, the method used to estimate the background from nonprompt and misidentified leptons leads to a 40% uncertainty on the normalization. In the global fit this uncer- tainty is reduced to 25%. A 50% uncertainty is assigned for the diboson background nor- malization, which is checked in the dedicated region described in Section 6. In this region, which is enriched in W W events with similar kinematic properties as the events in the SR, the simula- tion is found to agree, within the given uncertainty, with the data. A conservative 100% uncertainty is assigned to the very small rare backgrounds that are dominated by the tW process. The experimental uncertainties related to b tagging, trigger, lep- ton reconstruction, identification, and isolation criteria have been propagated and their effect on the final results ranges from 2% up to 12%. The jet energy scale corrections (JEC) are applied to match jet energies measured in data and simulation. The JEC are affected by an intrinsic uncertainty, which affects all simulated background, leading to typically 2–12% uncertainties in the final predictions. An uncertainty of 2.5% is assigned to the integrated luminosity measured by CMS for the 2016 data taking period [74]. This affects the estimate of the rare SM backgrounds that rely on the measured data luminosity. Finally, the uncertainty related to pileup has been estimated by varying the minimum-bias cross section by ±5% and reweighting the pileup distribution accordingly. The systematic uncertainty is found to be in the range 1–5%. As the signal yields are from simulation, additional systematic uncertainties are applied in two categories. One arises from the systematic uncertainty in the inclusive NLO + NLL [48–50] cross section used for the normalization, determined by varying the renormalization and factorization scales and the PDF. The depen- dence on these QCD scales yields a total uncertainty of 3%. The other category arises from the uncertainty in the product of the signal acceptance and efficiency. It is important to properly model the ISR that leads to the boost of the produced SUSY particles in the transverse plane. In partic- ular, for the electroweakino benchmark, the modelling of the ISR with MadGraph5_amc@nlo affects the total transverse momentum pISR T of the system of SUSY particles, which can be improved by reweighting pISR T in the simulated signal events. This reweighting is based on pT studies of events containing a Z boson [75], in which the factors range between 1.18 at pISR T of 125 GeV, and 0.78 for pISR T > 600 GeV. The deviation from 1.0 is taken as the systematic uncertainty of the reweighting procedure. For the ̃t benchmark to improve the modelling of the multiplicity of additional jets from ISR, the events are reweighted based on the N ISR jets, using the same corrections used for the top background as described earlier in this section. The typical uncertainties on the final results from the ISR modelling are found to be in the range 2–7%. We account for differences observed in pmiss T reconstruction ef- fects in full and fast simulation used for signal. The uncertainties vary between 3 and 5%. The uncertainties related to potential dif- ferences in b tagging between the full and fast simulation and in the JEC vary in the range 1–2%. These uncertainties, together with those related to the pre- dicted backgrounds described in Section 6, are included as log- normal distributed nuisance parameters in the likelihood approach. 8. Results The estimated yields of the SM background processes and the data observed in the SRs are shown in Figs. 3 and 4. No signifi- cant excess has been observed. The estimates in the SR bins are extracted from a maximum likelihood fit of the data using the ex- pected yields described in Section 6, namely the DY + jets, tt (2�), and SS CRs. Log-normal distributions for nuisance parameters are used to describe the systematic uncertainties of Section 7. The un- certainties in the predicted yields quoted in the following are those determined from the fit. The predicted yields along with the data are also summarized in Tables 6 and 7 for each bin of the SR.The total uncertainty in the yield for each SM process includes the systematic and statisti- cal uncertainties described in Section 7, added in quadrature. The largest deviation from the SM expectation is seen in a bin of the electroweakino search region. The bin with pmiss T ∈ [200, 250] GeV and M(��) ∈ [10.5, 20] GeV has 3.5 ±0.9 expected events but 0 ob- served. The smaller number of events observed in this bin drives the observed exclusion to higher values than expected, as can be seen in the next section. Overall, there is good agreement between expectation and observation. 9. Interpretation The results are interpreted in terms of the simplified mod- els with compressed mass spectra for χ̃0 2 χ̃± 1 → Z∗W±∗χ̃0 1 χ̃0 1 and for t̃̃t → bχ̃± 1 bχ̃∓ 1 with the subsequent decay χ̃± 1 → W±∗χ̃0 1 as discussed in Section 3. A binned likelihood fit of signal and the background expectations to the data is performed. This fit takes as input the yields in the SRs (12 for the electroweakino interpreta- tion and 9 for the top squark interpretation), together with those The CMS Collaboration / Physics Letters B 782 (2018) 440–467 447 Fig. 3. Left: electroweakino search regions in bins of M(��) for 125 < pmiss T < 200 GeV (muon only channel) for 33.2 fb−1; middle: 200 < pmiss T < 250 GeV (muon and electron channel) for 35.9 fb−1; right: pmiss T > 250 GeV (muon and electron channel) for 35.9 fb−1. A signal from neutralino–chargino (χ̃0 2 –χ̃± 1 ) production is superimposed. The gap between 9 and 10.5 GeV corresponds to the ϒ veto. Fig. 4. Left: ̃t search regions in bins of leading lepton pT for 125 < pmiss T < 200 GeV (muon only channel) for 33.2 fb−1; middle: 200 < pmiss T < 300 GeV (muon and electron channel) for 35.9 fb−1; right: pmiss T > 300 GeV (muon and electron channel) for 35.9 fb−1. A signal from ̃t pair production is superimposed. in the two CRs (125 < pmiss T < 200 GeV and pmiss T > 200 GeV) for the tt and DY + jets estimates, and the three pT bins for same-sign leptons for the pmiss T > 200 GeV CR. These background-dominated bins also help to constrain the uncertainties in the background taken from simulation and the one predicted by the “tight-to- loose” method. Upper limits on the cross sections in the benchmark models at 95% confidence level (CL) are extracted. We use asymptotic for- mulae [76] to derive the results. To set limits, the CLs criterion, as described in [77,78], is used. Figures 5 and 6 show the ob- served and expected upper limits on the electroweakino and ̃t pair production cross sections for the benchmarks considered in this search. For the electroweakino simplified model, the production cross sections are computed at NLO + NLL precision in the limit of a mass degenerate wino χ̃0 2 and χ̃± 1 , a light bino χ̃0 1 , and assuming all other SUSY particles to be heavy and decoupled [48–50]. Masses of χ̃0 2 up to 230 GeV for a m(χ̃0 2 , ̃χ0 1 ) of 20 GeV are excluded. The existence of ̃t masses up to 450 GeV with a m(̃t, ̃χ0 1 ) of 40 GeV is ruled out for this specific model. The expected and observed exclusion contours for the higgsino pMSSM are shown in Fig. 7. The higgsino mass parameter μ is excluded up to 160 GeV, when the bino mass parameter M1 is 300 GeV and the wino mass parameter M2 is 600 GeV. For larger values of M1 and M2, the mass splitting m(χ̃0 2 , ̃χ0 1 ) becomes smaller and the sensitivity is reduced. For M1 = 700 GeV, μ is ex- cluded up to 100 GeV. Fig. 8 shows the expected and observed exclusion contours and upper limits on cross sections at 95% CL in a higgsino simplified model. To calculate the cross sections in this model, a scan in |μ|, M1, M2 and tan β is carried out. All parameters are required to be real, M2 to be positive and tan β ∈ [1, 100]. The remain- ing SUSY particle masses are decoupled, and all trilinear couplings are discarded. The parameter space is then scanned to achieve the 448 The CMS Collaboration / Physics Letters B 782 (2018) 440–467 Table 6 The number of events observed in the data and the result of the fit of the backgrounds to the data in the electroweakino search regions. The uncertainty indicated is determined from the fit to the 33.2 and 35.9 fb−1 integrated luminosities. Values for the M(��) ranges are in GeV. Rare background event yields are omitted when they do not contribute to the SR bin. 125 < pmiss T < 200 GeV 4 < M(��) < 9 10.5 < M(��) < 20 20 < M(��) < 30 30 < M(��) < 50 tt(2�) 0.23 ± 0.16 1.9 ± 0.52 2.80 ± 0.65 3.60 ± 0.75 DY + jets 0.83 ± 0.63 3.7 ± 1.5 4.9 ± 1.5 1.60 ± 0.99 VV 0.82 ± 0.48 0.71 ± 0.65 1.7 ± 1.0 2.2 ± 1.2 Nonprompt lepton 1.7 ± 0.7 5.7 ± 1.5 7.5 ± 1.7 3.3 ± 1.1 Rare — 0.46+0.64 −0.45 — 0.33+0.49 −0.32 Total SM prediction 3.5 ± 1.0 12.0 ± 2.3 17.0 ± 2.4 11.0 ± 2.0 Data 2 15 19 18 200 < pmiss T < 250 GeV 4 < M(��) < 9 10.5 < M(��) < 20 20 < M(��) < 30 30 < M(��) < 50 tt(2�) 0.21 ± 0.17 0.38 ± 0.18 0.11+0.11 −0.10 — DY + jets 0.69 ± 0.62 0.67 ± 0.32 0.42 ± 0.27 — VV 0.26+0.28 −0.25 0.29+0.32 −0.28 0.42 ± 0.33 0.33 ± 0.29 Nonprompt lepton 0.44 ± 0.32 2.0 ± 0.7 1.0 ± 0.6 0.03+0.14 −0.02 Rare — 0.14+0.39 −0.13 — 0.17+0.37 −0.16 Total SM prediction 1.6 ± 0.7 3.5 ± 0.9 2.0 ± 0.7 0.51+0.52 −0.50 Data 1 0 3 1 pmiss T > 250 GeV 4 < M(��) < 9 10.5 < M(��) < 20 20 < M(��) < 30 30 < M(��) < 50 tt(2�) — 0.19 ± 0.14 0.091 ± 0.091 0.27 ± 0.14 DY + jets 0.24 ± 0.19 0.24 ± 0.17 0.17 ± 0.16 0.014+0.019 −0.013 VV 0.43 ± 0.35 0.29+0.29 −0.28 0.41 ± 0.29 0.66 ± 0.45 Nonprompt lepton 0.28+0.33 −0.27 0.77 ± 0.44 0.38 ± 0.30 0.23 ± 0.18 Rare 0.45+0.57 −0.44 — 0.49+0.62 −0.48 0.04+0.28 −0.03 Total SM prediction 1.4 ± 0.7 1.5 ± 0.6 1.5 ± 0.8 1.2 ± 0.6 Data 2 1 2 0 Table 7 The number of events observed in the data and the result of the fit of the backgrounds to the data in the ̃t search regions. The uncertainty indicated is determined from the fit to the 33.2 and 35.9 fb−1 integrated luminosities. Values for the pT(�1) ranges are in GeV. Rare background event yields are omitted when they do not contribute to the SR bin. 125 < pmiss T < 200 GeV 5 < pT(�1) < 12 12 < pT(�1) < 20 20 < pT(�1) < 30 tt(2�) 1.9 ± 0.4 11.0 ± 1.9 23.0 ± 3.5 DY + jets 2.9 ± 1.4 5.6 ± 1.9 4.6 ± 1.7 VV 0.8 ± 0.7 4.9+6.3 −4.8 9.4 ± 5.4 Nonprompt lepton 8.5 ± 1.9 15.0 ± 2.6 15.0 ± 2.6 Rare 0.10+0.16 −0.09 0.93+1.0 −0.92 1.8 ± 1.7 Total SM prediction 14.0 ± 2.3 37.0 ± 6.8 54.0 ± 6.5 Data 16 51 67 200 < pmiss T < 300 GeV 5 < pT(�1) < 12 12 < pT(�1) < 20 20 < pT(�1) < 30 tt(2�) 1.3 ± 0.35 9.9 ± 1.2 15 ± 2.2 DY + jets 0.92 ± 0.83 2.4 ± 0.9 1.6 ± 0.6 VV 2.5 ± 1.4 7.1 ± 4.0 12.0 ± 6.2 Nonprompt lepton 18.0 ± 3.2 20.0 ± 3.4 15.0 ± 2.7 Rare 0.52+0.54 −0.51 1.96 ± 1.46 1.45 ± 1.13 Total SM prediction 23.0 ± 3.5 41.0 ± 5.6 45.0 ± 7.0 Data 23 40 44 pmiss T > 300 GeV 5 < pT(�1) < 12 12 < pT(�1) < 20 20 < pT(�1) < 30 tt(2�) 0.39 ± 0.25 1.6 ± 0.5 1.6 ± 0.4 DY + jets 0.33 ± 0.26 0.28 ± 0.18 0.19 ± 0.07 VV 0.93 ± 0.53 2.5 ± 1.4 4.2 ± 2.2 Nonprompt lepton 3.1 ± 1.1 5.6 ± 1.3 4.0 ± 1.3 Rare — 0.15+0.18 −0.14 0.45+0.50 −0.44 Total SM prediction 4.7 ± 1.3 10.0 ± 1.9 10.0 ± 2.5 Data 4 11 9 The CMS Collaboration / Physics Letters B 782 (2018) 440–467 449 Fig. 5. The observed 95% CL exclusion contours (black curves) assuming the NLO + NLL cross sections, with the variations corresponding to the uncertainty in the cross section for electroweakino. The dashed (red) curves present the 95% CL expected limits with the band covering 68% of the limits in the absence of signal. Results are based on a simplified model of χ̃0 2 χ̃± 1 → Z∗W∗χ̃0 1 χ̃0 1 process with a pure wino pro- duction cross section. (For interpretation of the colours in the figure(s), the reader is referred to the web version of this article.) Fig. 6. The observed 95% CL exclusion contours (black curves) assuming the NLO + NLL cross sections, with the variations corresponding to the uncertainty in the cross section for ̃t. The dashed (red) curves present the 95% CL expected limits with the band covering 68% of the limits in the absence of signal. A simplified model of the ̃t pair production, followed by the ̃t → bχ̃± 1 and the subsequent χ̃± 1 → W∗χ̃0 1 decay is used for the ̃t search. In this latter model, the mass of the χ̃± 1 is set to be (m̃t + mχ̃0 1 )/2. maximum higgsino content for χ̃0 2 , χ̃± 1 , and χ̃0 1 [79]. For a m between 15 and 20 GeV, the production model of pp → χ̃0 2 χ̃± 1 and pp → χ̃0 2 χ̃0 1 is excluded for masses up to χ̃0 2 ∼ 167 GeV. 10. Summary A search is presented for new physics in events with two low-momentum leptons of opposite charge and missing trans- verse momentum in data collected by the CMS experiment at a centre-of-mass energy of 13 TeV, corresponding to an integrated Fig. 7. The observed 95% CL exclusion contours (black curve) assuming the NLO cross sections, with the variations corresponding to the uncertainty in the cross sections for the higgsino pMSSM, which has been introduced in the text. The dashed (red) curves present the band covering 68% of the limits in the absence of signal. The model considers all possible production processes. Fig. 8. The observed 95% CL exclusion contours (black curves) assuming the NLO + NLL cross sections, with the variations corresponding to the uncertainty in the cross sections for the higgsino simplified models. The dashed (red) curves present the expected limits with the associated band covering 68% of the limits in the absence of signal. luminosity of up to 35.9 fb−1. The data are found to be con- sistent with standard model expectations. The results are inter- preted in the framework of supersymmetric simplified models targeting electroweakino mass-degenerate spectra and ̃t-χ̃0 1 mass- degenerate benchmark models. For the ̃t chargino-mediated decay into bW∗χ̃0 1 , top squark masses of up to 450 GeV are excluded in a simplified model for m(̃t, ̃χ0 1 ) = 40 GeV. The search further probes the χ̃0 2 χ̃± 1 → Z∗W∗χ̃0 1 χ̃0 1 process for mass differences ( m) between χ̃0 2 and χ̃0 1 of less than 20 GeV. Assuming wino produc- tion cross sections, χ̃0 2 masses up to 230 GeV are excluded for m of 20 GeV. The search is also sensitive to higgsino production; in a simplified higgsino model, χ̃0 2 masses up to 167 GeV are ex- cluded for m of 15 GeV, while in a higgsino pMSSM, limits in the higgsino-bino mass parameters μ–M1 plane are extracted. 450 The CMS Collaboration / Physics Letters B 782 (2018) 440–467 Acknowledgements We congratulate our colleagues in the CERN accelerator depart- ments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS in- stitutes 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 construc- tion 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); COLCIEN- CIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of Fin- land, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH (Hun- gary); 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, RFBR and RAEP (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEP- Center, 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 pro- gramme and the European Research Council and Horizon 2020 Grant, contract No. 675440 (European Union); the Leventis Foun- dation; 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’Agricul- ture (FRIA-Belgium); the Agentschap voor Innovatie door Weten- schap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Sci- ence 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, the National Sci- ence Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priori- ties Research Program by Qatar National Research Fund; the Pro- grama Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chula- longkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Welch Foun- dation, contract C-1845; and the Weston Havens Foundation (USA). 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Paktinat Mehdiabadi 27, F. Rezaei Hosseinabadi, B. Safarzadeh 28, M. Zeinali Institute for Research in Fundamental Sciences (IPM), Tehran, Iran M. Felcini, M. Grunewald University College Dublin, Dublin, Ireland M. Abbrescia a,b, C. Calabria a,b, A. Colaleo a, D. Creanza a,c, L. Cristella a,b, N. De Filippis a,c, M. De Palma a,b, F. Errico a,b, L. Fiore a, G. Iaselli a,c, S. Lezki a,b, G. Maggi a,c, M. Maggi a, B. Marangelli a,b, G. Miniello a,b, S. My a,b, S. Nuzzo a,b, A. Pompili a,b, G. Pugliese a,c, R. Radogna a, A. Ranieri a, G. Selvaggi a,b, A. Sharma a, L. Silvestris a,14, R. Venditti a, P. Verwilligen a, G. Zito a a INFN Sezione di Bari, Bari, Italy b Università di Bari, Bari, Italy c Politecnico di Bari, Bari, Italy G. Abbiendi a, C. Battilana a,b, D. Bonacorsi a,b, L. Borgonovi a,b, S. Braibant-Giacomelli a,b, R. Campanini a,b, P. Capiluppi a,b, A. Castro a,b, F.R. Cavallo a, S.S. Chhibra a,b, G. Codispoti a,b, The CMS Collaboration / Physics Letters B 782 (2018) 440–467 457 M. Cuffiani a,b, G.M. Dallavalle a, F. Fabbri a, A. Fanfani a,b, D. Fasanella a,b, P. Giacomelli a, C. Grandi a, L. Guiducci a,b, F. Iemmi, S. Marcellini a, G. Masetti a, A. Montanari a, F.L. Navarria a,b, A. Perrotta a, A.M. Rossi a,b, T. Rovelli a,b, G.P. Siroli a,b, N. Tosi a a INFN Sezione di Bologna, Bologna, Italy b Università di Bologna, Bologna, Italy S. Albergo a,b, S. Costa a,b, A. Di Mattia a, F. Giordano a,b, R. Potenza a,b, A. Tricomi a,b, C. Tuve a,b a INFN Sezione di Catania, Catania, Italy b Università di Catania, Catania, Italy G. Barbagli a, K. Chatterjee a,b, V. Ciulli a,b, C. Civinini a, R. D’Alessandro a,b, E. Focardi a,b, G. Latino, P. Lenzi a,b, M. Meschini a, S. Paoletti a, L. Russo a,29, G. Sguazzoni a, D. Strom a, L. Viliani a a INFN Sezione di Firenze, Firenze, Italy b Università di Firenze, Firenze, Italy L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera 14 INFN Laboratori Nazionali di Frascati, Frascati, Italy V. Calvelli a,b, F. Ferro a, F. Ravera a,b, E. Robutti a, S. Tosi a,b a INFN Sezione di Genova, Genova, Italy b Università di Genova, Genova, Italy A. Benaglia a, A. Beschi b, L. Brianza a,b, F. Brivio a,b, V. Ciriolo a,b,14, M.E. Dinardo a,b, S. Fiorendi a,b, S. Gennai a, A. Ghezzi a,b, P. Govoni a,b, M. Malberti a,b, S. Malvezzi a, R.A. Manzoni a,b, D. Menasce a, L. Moroni a, M. Paganoni a,b, K. Pauwels a,b, D. Pedrini a, S. Pigazzini a,b,30, S. Ragazzi a,b, T. Tabarelli de Fatis a,b a INFN Sezione di Milano-Bicocca, Milano, Italy b Università di Milano-Bicocca, Milano, Italy S. Buontempo a, N. Cavallo a,c, S. Di Guida a,d,14, F. Fabozzi a,c, F. Fienga a,b, A.O.M. Iorio a,b, W.A. Khan a, L. Lista a, S. Meola a,d,14, P. Paolucci a,14, C. Sciacca a,b, F. Thyssen a a INFN Sezione di Napoli, Napoli, Italy b Università di Napoli ‘Federico II’, Napoli, Italy c Università della Basilicata, Potenza, Italy d Università G. Marconi, Roma, Italy P. Azzi a, N. Bacchetta a, L. Benato a,b, D. Bisello a,b, A. Boletti a,b, R. Carlin a,b, A. Carvalho Antunes De Oliveira a,b, P. Checchia a, M. Dall’Osso a,b, P. De Castro Manzano a, T. Dorigo a, U. Dosselli a, F. Fanzago a, F. Gasparini a,b, U. Gasparini a,b, A. Gozzelino a, S. Lacaprara a, P. Lujan, M. Margoni a,b, A.T. Meneguzzo a,b, N. Pozzobon a,b, P. Ronchese a,b, R. Rossin a,b, A. Tiko, E. Torassa a, M. Zanetti a,b, G. Zumerle a,b a INFN Sezione di Padova, Padova, Italy b Università di Padova, Padova, Italy c Università di Trento, Trento, Italy A. Braghieri a, A. Magnani a, P. Montagna a,b, S.P. Ratti a,b, V. Re a, M. Ressegotti a,b, C. Riccardi a,b, P. Salvini a, I. Vai a,b, P. Vitulo a,b a INFN Sezione di Pavia, Pavia, Italy b Università di Pavia, Pavia, Italy L. Alunni Solestizi a,b, M. Biasini a,b, G.M. Bilei a, C. Cecchi a,b, D. Ciangottini a,b, L. Fanò a,b, P. Lariccia a,b, R. Leonardi a,b, E. Manoni a, G. Mantovani a,b, V. Mariani a,b, M. Menichelli a, A. Rossi a,b, A. Santocchia a,b, D. Spiga a a INFN Sezione di Perugia, Perugia, Italy b Università di Perugia, Perugia, Italy 458 The CMS Collaboration / Physics Letters B 782 (2018) 440–467 K. Androsov a, P. Azzurri a,14, G. Bagliesi a, L. Bianchini a, T. Boccali a, L. Borrello, R. Castaldi a, M.A. Ciocci a,b, R. Dell’Orso a, G. Fedi a, L. Giannini a,c, A. Giassi a, M.T. Grippo a,29, F. Ligabue a,c, T. Lomtadze a, E. Manca a,c, G. Mandorli a,c, A. Messineo a,b, F. Palla a, A. Rizzi a,b, P. Spagnolo a, R. Tenchini a, G. Tonelli a,b, A. Venturi a, P.G. Verdini a a INFN Sezione di Pisa, Pisa, Italy b Università di Pisa, Pisa, Italy c Scuola Normale Superiore di Pisa, Pisa, Italy L. Barone a,b, F. Cavallari a, M. Cipriani a,b, N. Daci a, D. Del Re a,b, E. Di Marco a,b, M. Diemoz a, S. Gelli a,b, E. Longo a,b, F. Margaroli a,b, B. Marzocchi a,b, P. Meridiani a, G. Organtini a,b, R. Paramatti a,b, F. Preiato a,b, S. Rahatlou a,b, C. Rovelli a, F. Santanastasio a,b a INFN Sezione di Roma, Rome, Italy b Sapienza Università di Roma, Rome, Italy N. Amapane a,b, R. Arcidiacono a,c, S. Argiro a,b, M. Arneodo a,c, N. Bartosik a, R. Bellan a,b, C. Biino a, N. Cartiglia a, R. Castello a,b, F. Cenna a,b, M. Costa a,b, R. Covarelli a,b, A. Degano a,b, N. Demaria a, B. Kiani a,b, C. Mariotti a, S. Maselli a, E. Migliore a,b, V. Monaco a,b, E. Monteil a,b, M. Monteno a, M.M. Obertino a,b, L. Pacher a,b, N. Pastrone a, M. Pelliccioni a, G.L. Pinna Angioni a,b, A. Romero a,b, M. Ruspa a,c, R. Sacchi a,b, K. Shchelina a,b, V. Sola a, A. Solano a,b, A. Staiano a, P. Traczyk a,b a INFN Sezione di Torino, Torino, Italy b Università di Torino, Torino, Italy c Università del Piemonte Orientale, Novara, Italy S. Belforte a, M. Casarsa a, F. Cossutti a, G. Della Ricca a,b, A. Zanetti a a INFN Sezione di Trieste, Trieste, Italy b Università di Trieste, Trieste, Italy D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S. Sekmen, D.C. Son, Y.C. Yang Kyungpook National University, Daegu, Republic of Korea H. Kim, D.H. Moon, G. Oh Chonnam National University, Institute for Universe and Elementary Particles, Kwangju, Republic of Korea J.A. Brochero Cifuentes, J. Goh, T.J. Kim Hanyang University, Seoul, Republic of Korea S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S. Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Korea University, Seoul, Republic of Korea J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu Seoul National University, Seoul, Republic of Korea H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park University of Seoul, Seoul, Republic of Korea Y. Choi, C. Hwang, J. Lee, I. Yu Sungkyunkwan University, Suwon, Republic of Korea V. Dudenas, A. Juodagalvis, J. Vaitkus Vilnius University, Vilnius, Lithuania The CMS Collaboration / Physics Letters B 782 (2018) 440–467 459 I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali 31, F. Mohamad Idris 32, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia R. Reyes-Almanza, G. Ramirez-Sanchez, M.C. Duran-Osuna, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz 33, R.I. Rabadan-Trejo, R. Lo