Search for dark matter produced in association with heavy-flavor quark pairs in proton-proton collisions at \(\sqrt{s}= 13\,\text{TeV} \)

The European Physical Journal C, Dec 2017

A search is presented for an excess of events with heavy-flavor quark pairs (\({t}\overline{{t}} \) and \({b} \overline{{b}} \)) and a large imbalance in transverse momentum in data from proton–proton collisions at a center-of-mass energy of 13\(\,\text{TeV}\). The data correspond to an integrated luminosity of 2.2\(\,\text{fb}^{-1}\) collected with the CMS detector at the CERN LHC. No deviations are observed with respect to standard model predictions. The results are used in the first interpretation of dark matter production in \({t}\overline{{t}} \) and \({b} \overline{{b}} \) final states in a simplified model. This analysis is also the first to perform a statistical combination of searches for dark matter produced with different heavy-flavor final states. The combination provides exclusions that are stronger than those achieved with individual heavy-flavor final states.

A PDF file should load here. If you do not see its contents the file may be temporarily unavailable at the journal website or you do not have a PDF plug-in installed and enabled in your browser.

Alternatively, you can download the file locally and open with any standalone PDF reader:

https://link.springer.com/content/pdf/10.1140%2Fepjc%2Fs10052-017-5317-4.pdf

Search for dark matter produced in association with heavy-flavor quark pairs in proton-proton collisions at \(\sqrt{s}= 13\,\text{TeV} \)

Eur. Phys. J. C Search for dark matter produced in association with heavy-flavor √ quark pairs in proton-proton collisions at s = 13 TeV CMS Collaboration 0 1 0 CERN , 1211 Geneva 23 , Switzerland 1 Faculty of Physics, Institute of Experimental Physics, University of Warsaw , Warsaw , Poland K. Bunkowski, A. Byszuk 2 , K. Doroba , A. Kalinowski, M. Konecki, J. Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak A search is presented for an excess of events with heavy-flavor quark pairs (t t and bb) and a large imbalance in transverse momentum in data from proton-proton collisions at a center-of-mass energy of 13 TeV. The data correspond to an integrated luminosity of 2.2 fb−1 collected with the CMS detector at the CERN LHC. No deviations are observed with respect to standard model predictions. The results are used in the first interpretation of dark matter production in t t and bb final states in a simplified model. This analysis is also the first to perform a statistical combination of searches for dark matter produced with different heavy-flavor final states. The combination provides exclusions that are stronger than those achieved with individual heavy-flavor final states. - 1 Introduction Astrophysical and cosmological observations [ 1–3 ] provide strong support for the existence of dark matter (DM), which could originate from physics beyond the standard model (BSM). In a large class of BSM models, DM consists of stable, weakly-interacting massive particles (WIMPs). In collider experiments, WIMPs (χ ) could be pair-produced through the exchange of new mediating fields that couple to DM and to standard model (SM) particles. Following their production, the WIMPs would escape detection, thereby creating an imbalance of transverse momentum (missing transverse momentum, pmiss) in the event. T If the new physics associated with DM respects the principle of minimal flavor violation [ 4,5 ], the interactions of spin0 mediators retain the Yukawa structure of the SM. This principle is motivated by the apparent lack of new flavor physics at the electroweak (EWK) scale. Because only the top quark has a Yukawa coupling of order unity, WIMP DM couples preferentially to the heavy top quark in models with minimal flavor violation. In high energy proton-proton collisions, this coupling leads to the production of t t + χ χ at lowest-order via a scalar (φ) or pseudoscalar (a) mediator (Fig. 1), and to the production of so-called mono-X final states through a top quark loop [ 6–14 ]. At the CERN Large Hadron Collider (LHC), the t t + χ χ process can be probed directly via the t t + pmiss and bb + pmiss signatures. The bb + pmiss signa T T T ture provides additional sensitivity to the bb+χ χ process for models in which mediator couplings to up-type quarks are suppressed, as can be the case in Type-II two Higgs doublet models [ 15 ]. This paper describes a search for DM produced with a t t or bb pair in pp collisions at √s = 13 TeV with the CMS experiment at the LHC. A potential DM signal is extracted from simultaneous fits to the pmiss distributions in the bb + pmiss T T and t t + pmiss search channels. Data from control regions T enriched in SM t t , W + jets, and Z + jets processes are included in the fits, to constrain the major backgrounds. The top quark nearly always decays to a W boson and a b quark. The W boson subsequently decays leptonically (to charged leptons and neutrinos) or hadronically (to quark pairs). The dileptonic, lepton( )+jets, and all-hadronic t t final states consist, respectively, of events in which both, either, or neither of the W bosons decay leptonically. Each of these primary t t final states are explored. Previous LHC searches for DM produced with heavyflavor quark pairs were interpreted using effective field theories that parameterize the DM-SM coupling in terms of an interaction scale M∗ [ 16–18 ]. An earlier search by the CMS Collaboration investigated the + jets t t final state using 19.7 fb−1 of data collected at √s = 8 TeV [ 19 ]. That search excluded values of M∗ below 118 GeV, assuming mχ = 100 GeV. The ATLAS Collaboration performed a similar search separately for the all-hadronic and + jets t t final states and obtained comparable limits on M∗ [ 20 ]. More recently, the limitations of effective field theory interpretations of DM production at the LHC has led to the development of simplified models that remain valid when the mediating particle is produced on-shell [ 21 ]. This analysis adopts the simplified model framework to provide the first interpretag g φ/a t (b) χ χ t (b) tion of heavy-flavor search results in terms of the decays of spin-0 mediators with scalar or pseudoscalar couplings. This paper also reports the first statistical combination of dileptonic (ee, eμ, μμ), + jets (e, μ), and all-hadronic t t + χ χ searches, as well as the first combination of t t + χ χ and bb + χ χ search results. The paper is organized as follows. Section 2 reviews the properties of the CMS detector and the particle reconstruction algorithms used in the analysis. Section 3 describes the modeling of t t +χ χ and bb +χ χ signal and SM background events, and Sect. 4 provides the selections applied to data and simulation. Section 5 discusses the techniques used to extract a potential DM signal in the t t + pmiss and bb + pmiss T T search channels. Section 6 describes the systematic uncertainties considered in the analysis. The results of the search and their interpretation within a simplified DM framework are presented in Sect. 7. Section 8 concludes with a summary of the results. 2 CMS detector and event reconstruction The CMS detector [ 22 ] is a multipurpose apparatus optimized for the study high transverse momentum ( pT) physics processes in pp and heavy ion collisions. A superconducting solenoid surrounds the central region, providing a magnetic field of 3.8 T parallel to the beam direction. Charged particle trajectories are measured using the silicon pixel and strip trackers, which cover the pseudorapidity region of |η| < 2.5. A lead tungstate crystal electromagnetic calorimeter (ECAL) and a brass and scintillator hadron calorimeter (HCAL) surround the tracking volume, and cover the region with |η| < 3. Each calorimeter is composed of a barrel and two endcap sections. A steel and quartz-fiber Cherenkov forward hadron calorimeter extends the coverage to |η| < 5. The muon system consists of gas-ionization detectors embedded in the steel flux return yoke outside the solenoid, and covers the region of |η| < 2.4. The first level of the CMS trigger system is composed of special hardware processors that select the most interesting events in less than 4 μs using information from the calorimeters and muon detectors. This system reduces the event rate from 40 MHz to approximately 100 kHz. The highlevel trigger processor farm performs a coarse reconstruction of events selected by the first-level trigger, and applies additional selections to reduce the event rate to less than 1 kHz for storage. Event reconstruction is based on the CMS Particle Flow (PF) algorithm [ 23,24 ], which combines information from all CMS subdetectors to identify and reconstruct the individual particles emerging from a collision: electrons, muons, photons, and charged and neutral hadrons. Interaction vertices are reconstructed using the deterministic annealing algorithm [25]. The primary vertex is selected as that with the largest sum of pT2 of its associated charged particles. Events are required to have a primary vertex that is consistent with being in the luminous region. Jets are reconstructed by clustering PF candidates using the anti-kT algorithm [ 26,27 ] with a distance parameter of 0.4. Corrections based on jet area are applied to remove the energy from additional collisions in the same or neighboring bunch crossing (pileup) [28]. Energy scale calibrations determined from the comparison of simulation and data are then applied to correct the four momenta of the jets [ 29 ]. Jets are required to have pT > 30 GeV, |η| < 2.4, and to satisfy a loose set of identification criteria designed to reject events arising from spurious detector and reconstruction effects. The combined secondary vertex b tagging algorithm (CSVv2) is used to identify jets originating from the hadronization of bottom quarks [ 30,31 ]. Jets are considered to be b-tagged if the CSVv2 discriminant for that jet passes a requirement that roughly corresponds to efficiencies of 70% to tag bottom quark jets, 20% to mistag charm quark jets, and 1% to misidentify light-flavor jets as b jets. Efficiency scale factors in the range of 0.92–0.98, varying with jet pT, are applied to simulated events in order to reproduce the b tagging performance for bottom and charm quark jets observed in data. A scale factor of 1.14 is applied to simulation to reproduce the measured mistag rate for light-flavor quark and gluon jets. The pmiss variable is initially calculated as the magnitude T of the vector sum of the pT of all PF particles. This quantity is adjusted by applying jet energy scale corrections. Detector noise, inactive calorimeter cells, and cosmic rays can give rise to events with severely miscalculated pmiss. Such events T are removed via a set of quality filters that take into account the timing and distribution of signals from the calorimeters, missed tracker hits, and global characteristics of the event topology. Electron candidates are reconstructed by combining tracking information with energy depositions in the ECAL [ 32 ]. The energy of the ECAL clusters is required to be compatible with the momentum of the associated electron track. Muon candidates are reconstructed by combining tracks from the inner silicon tracker and the outer muon system [ 33 ]. Tracks associated with muon candidates must be consistent with a muon originating from the primary vertex, and must satisfy a set of quality criteria [ 33 ]. Electrons and muons are selected with pT > 30 GeV and |η| < 2.1 for consistency with the coverage of the single-lepton triggers, and are required to be isolated from hadronic activity, to reject hadrons misidentified as leptons. Relative isolation is defined as the scalar pT sum of PF candidates within a R = √η2 + φ2 cone of radius 0.4 or 0.3 centered on electrons or muons, respectively, divided by the lepton pT. Relative isolation is nominally required to be less than 0.035 (0.065) for electrons in the barrel (endcap), respectively, and less than 0.15 for muons. Identification requirements, based on hit information in the tracker and muon systems, and on energy depositions in the calorimeters, are imposed to ensure that candidate leptons are well-measured. These restrictive isolation and identification criteria are used to select events from the dileptonic t t , + jets t t , W( ν) + jets, and Z ( ) + jets processes. The efficiencies of the requirements for electrons (muons) with pT > 30 GeV range from 52 to 83% (91 to 96%), for increasing lepton pT. Less restrictive lepton isolation and identification requirements are used to reject events containing additional leptons with pT > 10 GeV. Efficiencies for these requirements range from 66 to 96% for electrons and 73 to 99% for muons, for increasing lepton pT. Electron and muon selection efficiency scale factors are applied in simulation to match the efficiencies measured in data using the tagand-probe procedure [ 34 ]. Averaged over lepton pT, the electron and muon efficiency scale factors for the more restrictive selection requirements are 98 and 99%, respectively. The “resolved top tagger” (RTT) is a multivariate discriminant that uses jet properties and kinematics to identify top quarks that decay into three resolved jets. The input observables are the values of the quark/gluon discriminant [ 35 ], which combines track multiplicity, jet shape, and fragmentation information for each jet, values of the b tagging discriminants, and the opening angles between the candidate b jet and the two jets from the candidate W boson. Within each jet triplet, the b candidate is considered to be the jet with the largest value of the b tagging discriminant. The RTT discriminant also utilizes the χ 2 value of a simultaneous kinematic fit to the top quark and W boson masses [ 36 ]. The fit attempts to satisfy the mass constraints by allowing the jet momenta and energies to vary within their measured resolutions. The RTT is implemented as a boosted decision tree using the TMVA framework [ 37 ], and is trained on simulated + jets t t events using correct (incorrect) jet combinations as signal (background). The performance of the RTT discriminant is characterized with data enriched in SM + jets t t events containing four in 1600 b / ts 1400 n ve 1200 E 1000 800 600 400 200 . im1.5 /S1.0 taa0.5 D CMS Data l+jets tt with matched jets l+jets tt combinatorial Other background or more jets. At least one of these jets is required to be btagged. The output discriminant for these events is plotted in Fig. 2. Each entry in the plot corresponds to the jet triplet with the highest RTT score in the event. Data are modeled using simulated +jets t t signal events, and simulated events for each of the primary backgrounds (dileptonic t t , W + jets, single t). The simulation is split into three classes that correspond to correctly tagged jet triplets and the two possibilities for mistagging, as explained below. Simulation describes the data well. A jet triplet is considered as a tagged top quark decay when the RTT discriminant value is greater than zero. There are three efficiencies associated with the RTT selection, which correspond to the three classes of events in Fig. 2: + jets t t events in which the hadronically-decaying top quark is correctly identified (“t t (1 ) matched”), + jets t t events in which an incorrect combination of jets is tagged (“t t (1 ) combinatorial”), and events with no hadronicallydecaying top quarks that contain a mistagged jet triplet (“other background”). Dileptonic t t events are used to extract the nonhadronic mistag rate in data. Then, + jets t t events are used to extract the tagging and mistagging efficiencies for hadronically-decaying top quarks through a fit to the trijet mass distribution. Mass templates obtained from simulation are associated with each efficiency term in the fit. The efficiency of the RTT > 0 selection for events determined to be t t (1 ) matched, t t (1 ) combinatorial, or other background are 0.97 ± 0.03, 0.80 ± 0.05, and 0.69 ± 0.02, respectively. Corresponding data-to-simulation scale factors are found to be consistent with unity. The bb + pmiss search includes vetoes on hadronically T decaying τ leptons, which are reconstructed from PF candidates using the “hadron plus strips” algorithm [ 38 ]. The algorithm combines one or three charged pions with up to two neutral pions. Neutral pions are reconstructed by the PF algorithm from the photons that arise from π 0 → γ γ decay. Photons are reconstructed from ECAL energy clusters, which are corrected to recover the energy deposited by photon conversions and bremsstrahlung. Photons are identified and distinguished from jets and electrons using cut-based criteria that include the isolation and transverse shape of the ECAL deposit, and the ratio of HCAL/ECAL energies in a region surrounding the candidate photon. 3 Modeling and simulation The associated production of DM and heavy-flavor quark pairs provides rich detector signatures that include significant pmiss accompanied by high- pT jets, bottom quarks, and lepT tons. The largest backgrounds in the t t + pmiss and bb + pmiss T T searches are SM t t events, inclusive W boson production in which the W decays leptonically (W( ν) + jets), and inclusive Z boson production in which the Z decays to neutrinos (Z (νν¯ )+jets). Simulated events are used throughout the analysis to determine signal and background expectations. Where possible, corrections determined from data are applied to the simulations. Monte Carlo (MC) samples of SM t t and single t backgrounds are generated at next-to-leading order (NLO) in quantum chromodynamics (QCD) using Powhegv2 and Powhegv1 [ 39–41 ], respectively. As with all MC generators subsequently described, Powheg is interfaced with Pythia8.205 [ 42 ] for parton showering using the CUETP8M1 tune [ 43 ]. Samples of Z + jets, W + jets, and QCD multijet events are produced at leading order (LO) using MG5_amc@nlo v2.2.2 [ 44 ] with the MLM prescription [ 45 ] for matching jets from the matrix element calculation to the parton shower description. The W + jets and Z + jets samples are corrected using EWK and QCD NLO/LO K-factors calculated with MG5_amc@nlo as functions of the generated boson pT. The simulation of t t + γ , t t + W, and t t + Z events makes use of NLO matrix element calculations implemented in MG5_amc@nlo, and the FxFx [ 46 ] prescription to merge multileg processes. Diboson processes (WW, WZ, and ZZ) are generated at NLO using either MG5_amc@nlo or Powhegv2. The signal processes are simulated using simplified models that were developed in the LHC Dark Matter Forum (DMF) [ 21 ]. The DM particles χ are assumed to be Dirac fermions, and the mediators are spin-0 particles with scalar (φ) or pseudoscalar (a) couplings. The coupling strength of the mediator to SM fermions is assumed to be gqq = gq yq where: yq = √2mq /v is the SM Yukawa coupling, mq is the quark mass, and v = 246 GeV is the Higgs field vacuum expectation value. As per the recommendations of the LHC Dark Matter Working Group [ 47 ], gq is taken to be flavor universal and equal to 1. Likewise, the coupling strength of the mediator to DM, gχ , is set to 1 and is independent of the DM mass. The LHC DMF spin-0 models do not account for mixing between the φ scalar and the SM Higgs boson [ 48 ]. As is discussed in [ 21 ], the pmiss spectra of both the scalar and T pseudoscalar mediated processes broaden with increasing mediator mass. For mφ/a larger than twice the top quark mass (mtop), the pmiss distributions of the scalar and pseudoscalar T processes are essentially identical. As mφ/a decreases below 2mtop, the pmiss spectra of the two processes increasingly dif T fer, with the distribution of the scalar process peaking at lower pmiss values [ 49,50 ]. For all mediator masses, the total cross T section of the scalar process is larger than that of the pseudoscalar equivalent [50]. This analysis focuses on the mχ = 1 GeV LHC DMF benchmark point, which provides a convenient signal reference for both low and high mass mediators. The t t + χ χ and bb + χ χ signals are generated at LO in QCD using MG5_amc@nlo with up to one additional jet in the final state. Jets from the matrix element calculations are matched to the parton shower descriptions using the MLM prescription. Angular correlations in the decays of the top quarks are included using MadSpin v2.2.2 [ 51 ]. Minimum decay widths are assumed for the mediators, and are calculated from the partial width formulas given in Ref. [ 52 ]. This calculation assumes that the spin-0 mediators couple only to SM quarks and the DM fermion χ . Simulated signal samples are produced for a DM mass of mχ = 1 GeV and for mediator masses in the range of 10–500 GeV. The relative width of the scalar (pseudoscalar) mediator varies between 4 and 6% (4–8%) for this mediator mass range. The predicted rates of the bb + χ χ process, which is generated in the 4-flavor scheme, are adjusted to match the cross sections calculated in the 5-flavor scheme [ 21,53 ]. All samples generated at LO and NLO use corresponding NNPDF3.0 [ 54 ] parton distribution function (PDF) sets. All signal and background samples are processed using a detailed simulation of the CMS detector based on Geant4 [ 55 ]. The samples are reweighted to account for the distribution of pileup observed in data. 4 Event selection Signal events are expected to exhibit both large pmiss from T the production of two noninteracting DM particles and event topologies consistent with the presence of top quarks or b quark jets. Data are therefore collected using triggers that select events containing large pmiss or high- pT leptons. Data T for the dileptonic and +jets t t + pTmiss searches are obtained using single-lepton triggers that require an electron (muon) with pT ≥ 27 (20) GeV. These trigger selections are more than 90% efficient for PF-reconstructed electrons and muons that satisfy the pT, identification, and isolation requirements imposed. The trigger used for the bb + pmiss and all-hadronic T t t + pmiss searches selects events based on the amount of T pmiss and H miss reconstructed using a coarse version of the T T PF algorithm. The H miss variable is defined as the magnitude T of the vector sum of the pT of all jets in the event with pT > 20 GeV, |η| < 5.0. Jets reconstructed from detector noise are removed in the H miss calculation by additionally requiring T neutral hadron energy fractions of less than 0.9. The pmiss and T H miss requirements for this trigger are 120 GeV. The trigger T is nearly 100% efficient for events that satisfy subsequent selections based on fully-reconstructed PF pmiss. T Additional selections, described in Sect. 4.1 and summarized in Table 1, are applied to define eight independent regions of data that are sensitive to DM signals: two bb + pTmiss, one + jets t t + pTmiss, three dileptonic t t + pTmiss, and two all-hadronic t t + pmiss regions. Control regions T (CRs) enriched in various background processes are also defined and are used to improve background estimates in the aforementioned signal regions (SRs). In the CRs, individual signal selection requirements are inverted to enhance background yields and to prevent event overlaps with the SRs. Collectively, the SRs and CRs associated with the individual t t + χ χ and bb + χ χ production and decay modes are referred to as “channels”. The bb + χ χ channel and the three t t + χ χ channels are used in simultaneous pmiss fits T (described in Sect. 5) to extract a potential DM signal. The fits allow the background-enriched CRs to constrain the contributions of SM t t , W + jets, and Z + jets processes within the CRs and SRs of each channel. The selections used to define the SRs and CRs are described in Sects. 4.1 and 4.2, respectively. Tables 1 and 2 briefly summarize these selections. Table 2 defines a CR labeling scheme that is extensively used in subsequent sections. 4.1 Signal region selections Dileptonic t t + pmiss Events in the dileptonic t t SR are T required to contain exactly two leptons that satisfy stringent identification and isolation requirements. One of the leptons must have pT > 30 GeV, while the second must have pT > 10 GeV. Events containing additional, loosely identified leptons with pT > 10 GeV are rejected. Events are also required to have pmiss > 50 GeV, and to contain two or T more jets, at least one of which must satisfy b tagging requirements. Overlaps between the dileptonic SR and the dileptonic and Z + jets CRs of the + jets t t + pTmiss and bb + pTmiss channels (discussed in Sect. 4.2) are removed by vetoing events that satisfy the selections for those CRs. These vetoes detail in Sect. 4.1. Vetoes are applied in the dileptonic tt + pmiss signal T region to remove overlaps with the + jets tt + pTmiss and bb + pTmiss control regions. These control regions are summarized in Table 2 and discussed in Sect. 4.2 pmiss T ≥ 50 GeV ≥ 160 GeV ≥ 200 GeV ≥ 200 GeV Other selections min φ(−→p T , −→p Tmiss) > 1.2 rad m > 20 GeV |mee,μμ − m Z | > 15 GeV Dileptonic tt control region veto Z + jets control region veto MT > 160 GeV MTW2 > 200 GeV min φ(−→p jTeti , −→p Tmiss) > 1.2 rad 0,1RTT min φ(−→p jTeti , −→p Tmiss) > 1.0 rad 2 RTT min φ(−→p jTeti , −→p Tmiss) > 0.4 rad min φ(−→p jTeti , −→p Tmiss) > 0.5 rad mpT iss 160 mT + p ≥ t t s h t t je 1 i a i r e t ir ) c (s n n io io t g c e le r s se la is n mT e g p h i t s + fo .2 ted tt w 4 ia ts ie . c e v tc o j r s e e s + v S A ) s s i mT o t ) p e s −→ v is o ts 3 s s e a J ≥ 4 6 ≥ ≥ s n o i eg μ lro sn ,μ μμ tron tepo ee,μ roμ roμ roμ reo roμ roμ e μ μ e μ μ c L e e e 0 e e e 0 e e μ e μ e e μ e μ e d D D W W W Z e s u d n u o r d s g n s k u i c o mT s s a r p is is eb ckg + mpT mpT teednhfi itaannb ticonptt tjse tjsett+ jtse+ tjse tjse tjsett+ jtse+ tjse jtse tjsett tjse ticonptt jtse tjsett tjse itconptt to om lie + + /Z + + + /Z + + + + lie + + + lie W W W Z D W Z D O in l 2 i e ta l l e e A B C D E F G abT ind abL lsA lsB ahd ahd ahd ahd ahd ahd ahd bbA bbB bbC bbD bbE bbF bbG bbH Ibb Jbb T T R 2 ≥ , ) s s i mT p −→ i, t je T p −→ ( ≥ ≥ T T R 1 , 0 , ) s s i mT p −→ i, t je T p −→ ( φ V e G remove 2.5% of the events from the dileptonic t t + pmiss SR. T The azimuthal opening angle between the pT vector of the dilepton system and the pTmiss vector, φ (−→p T , −→p Tmiss), is required to be larger than 1.2 radians. This requirement preferentially selects events consistent with a t t system recoiling against the invisibly decaying DM mediator. The dilepton mass, m , is required to be larger than 20 GeV. In dielectron and dimuon events, m is also required to be at least 15 GeV away from the Z boson mass [ 56 ]. These requirements reduce backgrounds from low-mass dilepton resonances and from leptonic Z boson decays. Events that satisfy these criteria are divided among three SR categories that correspond to the flavor assignments of the two selected leptons: ee, eμ, and μμ. Signal efficiencies for the dileptonic t t + pmiss SR event selections range from T 6 × 10−3 to 10−2 for mediator masses between 10 GeV and 500 GeV. The denominator used in the efficiency calculation is the total number of signal events, irrespective of the t t final state. The low efficiencies result primarily from the small dileptonic branching fraction. + jets t t + pTmiss Events in the +jets t t SR are selected by requiring pmiss > 160 GeV, exactly one lepton, and three T or more jets, of which at least one must satisfy the b tagging criteria. The lepton is required to have pT > 30 GeV, and to pass tight identification criteria. Events must not contain additional leptons with pT > 10 GeV that satisfy a looser set of identification requirements. To reduce SM + jets t t and W + jets backgrounds, the transverse mass, calculated from −→p Tmiss and the lepton momentum (−→p T) as: MT = 2 pT pTmiss(1 − cos φ (−→p T, −→p Tmiss)), (1) is required to be larger than 160 GeV. Following these selections, the remaining background events primarily consist of dileptonic t t final states in which one of the leptons is not identified. Because of the requirement of pmiss > 160 GeV, this background tends to contain T events with Lorentz-boosted top quark decays in which the b jet is closely aligned with the direction of the neutrino. This background is suppressed by requiring that the smallest azimuthal angle formed from the missing transverse momentum vector and each of the two highest pT jets in the event, min φ (−→p jTeti , −→p Tmiss) where i = 1, 2, be larger than 1.2 radians. In addition, the MTW2 variable [ 57 ] is required to be larger than 200 GeV. This variable is defined as: where m y is the mass of two parent particles that each decay to bW( ν). One of the W decays is assumed to produce a lepton that is not reconstructed. For the W decay that does produce a reconstructed lepton, the neutrino and lepton 4-momenta are denoted p1 and p , respectively. The 4momentum of the W that produces the unreconstructed lepton is denoted p2, while the momenta of the two b candidates are referred to as pb1 and pb2. Assuming perfect measurements, the MTW2 has a kinematic end-point at mtop for t t events, whereas signal events lack this feature because both the neutrino and DM particles contribute to pmiss. T The efficiency of the + jets t t + pTmiss SR event selections for the t t + χ χ process range from 10−4 for mediator masses of the order of 10 GeV, to 10−3 for masses of about 500 GeV. Signal efficiencies are low because of the stringent pTmiss requirement applied. The efficiency improves with increasing mediator mass because of the broadening of the pmiss spectrum. T All-hadronic t t + pmiss Any event with a loosely iden T tified lepton with pT > 10 GeV is vetoed from the allhadronic t t + pmiss SRs. The pmiss value must be larger T T than 200 GeV, and four or more jets are required, at least one of which must satisfy b tagging criteria. Spurious pmiss can T arise in multijet events due to jet energy mismeasurement. In such cases, the reconstructed pmiss tends to align with one T of the jets. Multijet background is suppressed by requiring that min φ (−→p jTeti , −→p Tmiss) > 0.4 or 1 radian (depending on the number of RTT tags, as described below) for all jets in the event. The min φ (−→p jTeti , −→p Tmiss) selections also help to reduce + jets t t background, for which the pTmiss vector is typically aligned with a b jet. Following these selection requirements, the dominant residual background is + jets SM t t production. By contrast, selected signal typically includes events in which both top quarks decay hadronically. The resolved top quark tagger (RTT, introduced in Sect. 2) is employed to suppress the + jets background by identifying potential hadronic top quark decays. The RTT is applied to the all-hadronic search region to define a category of events with two hadronic top quark decays. In this double-tag (2 RTT) category, one or more btagged jets are required and min φ (−→p jTeti , −→p Tmiss) > 0.4 radians is imposed for all jets in the event. The 2 RTT category implicitly requires at least six jets in the event. A second category is defined for events with 0 or 1 top quark tags (0, 1 RTT), four or more jets with at least two b-tagged jets, and a tighter requirement of min φ (−→p jTeti , −→p Tmiss) > 1 radian. MTW2 = min m y consistent with: −→p 1T + −→p 2T = −→p Tmiss, p12 = 0, ( p1 + pl )2 = p2 = M W2, 2 ( p1 + pl + pb1)2 = ( p2 + pb2)2 = m2y (2) The selection efficiency for t t + χ χ events in the allhadronic t t + pmiss SRs ranges from 10−3 for mediator T masses of the order of 10 GeV to 10−1 for masses near 500 GeV. These values are larger than the corresponding efficiencies of the dileptonic and + jets SR selections because of the larger branching fraction to the all-hadronic final state. bb + pmiss Events with pmiss > 200 GeV are selected T T for the SRs of this final state. Events containing identified and isolated electrons or muons with pT larger than 10 GeV or identified τ leptons with pT > 18 GeV are rejected. Multijet background is reduced by requiring min φ (−→p jTeti , −→p Tmiss) > 0.5 radians for all jets in the event. Following these selections, two exclusive event categories are defined using the number of jets and b-tagged jets in the event. The single b-tagged jet category provides high efficiency for bb + χ χ signal and requires at most two jets. At least one of these jets must have pT > 50 GeV, and exactly one must satisfy b tagging requirements. The second category allows exactly two b-tagged jets. This SR selects bb + χ χ signal and partially recovers t t + χ χ events that are not selected in the all-hadronic t t + pmiss categories. At T most three jets are allowed in the 2 b tag SR, and at least two of these jets must have pT > 50 GeV. The efficiency of the bb + pmiss SR event selections for T the bb + χ χ process range from 10−6 for mediator masses of the order of 10 GeV, to 10−2 for masses of 500 GeV. The selection efficiency for the t t + χ χ process is found to be less dependent on the mediator mass, and varies from 10−4 to 10−3 for the same mass range. 4.2 Background control region selections Figure 3 shows the simulated background yields in each of the SRs following the selections of Sect. 4.1. Clearly, the dominant backgrounds in the SRs are from the SM t t , W + jets, and Z + jets processes. The estimation of backgrounds in the SRs is improved through the use of corresponding data CRs enriched in these processes. Independent CRs are defined for each of the + jets t t + pTmiss, all-hadronic t t + pTmiss and bb + pmiss SRs. In some cases, multiple CRs are used to T constrain a given background process in a SR. In this section we describe the main t t , W + jets, and Z + jets backgrounds and the selections used to define the CRs. The CR selections are designed to ensure that these regions are both mutually exclusive and exclusive of the SRs as well. The contributions of multijet, diboson, single t, and t t + Z /W/γ processes in the SRs are either subdominant or insignificant after the SR selections. The residual backgrounds from these processes are modeled with simulation. Dilepton background events from Drell–Yan and processes in which jets are misidentified as leptons are estimated using the sideband techniques described in Ref. [ 58 ]. 2.2 fb-1 (13 TeV) CMS Simulation tt W(lν)+jets Z+jets Single t Other bkg tt, ee tt, eμ tt, μμ tt, e/μ tt, 0,1RTT tt, 2RTTbb, 1b bb, 2b Fig. 3 Simulation-derived background expectations in the tt + pTmiss and bb + pTmiss signal regions The remainder of this section describes how the contributions of SM backgrounds in the SRs are estimated using the CRs. The discussion utilizes the CR labeling convention defined in Table 2, for ease of reference. The CRs for the + jets t t + pmiss SR are denoted slA and slB, those for the T all-hadronic t t + pmiss SRs are hadA–hadG, and those for T the bb + pmiss SRs are bbA–bbJ. T Section 5 describes how the CRs are simultaneously fit with the SRs to constrain the predicted normalization of the t t , W + jets, and Z + jets background processes. Figures 4, 5 and 6 compare the integrated yields in each CR before and after background-only fits to the CR pmiss distributions. T Reasonable agreement is found between the observed and predicted CR yields. In general, the expected and observed pmiss distributions in the CRs also agree. Regions for which T the distributions of data and of the initial (“prefit”) MC disagree are noted in the text. Dileptonic t t Dileptonic t t background in the + jets t t SR consists of events in which only one of the leptons is identified. A dileptonic CR (slA) for the + jets t t + pTmiss search region is defined by requiring an additional lepton with respect to the + jets selection, and by removing the selections on MT, MTW2 , and min φ (−→p jTeti , −→p Tmiss). Both leptons from dileptonic t t decays in the + jets SR are typically within the detector acceptance. The lepton momenta are therefore included in the pT vector sum for this CR, so as to simulate the pmiss distribution expected for the dilep T tonic t t background in the + jets SR. Mutual exclusion with the dileptonic t t and Z + jets CRs of the bb + pTmiss search 1l, 0b 1l, ≥2b Fig. 4 Observed data, and prefit and fitted background-only event yields in the control regions associated with the + jets t t + pTmiss signal region. The 2 lepton, ≥ 0 b tag region (slA in Table 2) is used to constrain the dileptonic t t background in the + jets t t + pTmiss signal region, while the 1 lepton, 0 b tag control region (slB) constrains W + jets background. The lower panel shows the ratios of observed to fitted background yields. In both panels, the statistical uncertainties of the data are indicated as vertical error bars and the fit uncertainties as hatched bands. Prefit yields and the ratios of prefit to fitted background expectations are shown as dashed magenta histograms region (described below) is ensured by vetoing events that additionally satisfy the selection requirements of those CRs. The t t background in the bb + pmiss SRs consists of dilepT tonic and + jets t t events in which no leptons are identified. Dileptonic t t CRs (bbE, bbJ) are formed for the 1 b tag and 2 b tag bb + pmiss SRs by requiring two opposite-charge, T different-flavor leptons with pT > 30 GeV. Tight (loose) identification and isolation criteria are imposed on the leading pT (subleading pT) lepton. In contrast to the dileptonic background in the + jets t t + pTmiss SR, the leptons from t t in the bb + pmiss SRs typically fall outside of the detec T tor acceptance. The momentum of the selected leptons in the bb + pmiss CRs is therefore subtracted from the pmiss observ T T able in order to mimic the pmiss distribution in the SR. The T φ (−→p jTeti , −→p Tmiss), which primar SR requirements on min ily remove multijet background, are not imposed. All other selections from the bb + pmiss SRs are applied. T Dileptonic t t production is the dominant SM background in the dileptonic t t + pmiss SRs. Corresponding CRs are not T employed for this search channel because dileptonic t t events are found to be well-modeled by simulation and are selected with high efficiency in the dileptonic SR. + jets t t The most significant source of background in the hadronic t t + pTmiss SRs is + jets t t production. This process contributes to the hadronic t t + pmiss search when T CMS Control regions for 0,1RTT all-hadronic tt+pTmiss CMS Control regions for 2RTT all-hadronic tt+pTmiss s t en 105 v E 104 103 102 Fig. 5 Observed data, and prefit and fitted background-only event yields in the control regions associated with the 0,1 RTT (upper) and 2 RTT (lower) all-hadronic t t + pmiss signal regions. The 1 lepton, ≥ 2 b T tag control region (hadA in Table 2) constrains + jets t t background in the 0,1 RTT signal region. This process is constrained in the 2 RTT signal region using the 1 lepton, ≥ 1 b tag control region (hadE). The ≤1 lepton, 0 b tag control regions (hadB, hadC, hadF, hadG) constrain W + jets and Z + jets backgrounds, while the 2 lepton, 0 b tag control region (hadD) provides an additional constraint on the Z + jets background. The lower panels show the ratios of observed to fitted background yields. In both panels, the statistical uncertainties of the data are indicated as vertical error bars and the fit uncertainties as hatched bands. Prefit yields and the ratios of prefit to fitted background expectations are shown as dashed magenta histograms the lepton is not identified. Control regions for + jets t t (hadA, hadE) are defined by selecting events with exactly one identified lepton with pT > 30 GeV, and by requiring MT < 160 GeV in order to avoid overlaps with the SR of the + jets channel. All other requirements used to define the hadronic SRs are applied, and the CR is split into 0,1 RTT and 2 RTT categories. The dileptonic t t CRs for the bb + pmiss search (described T above) provide stringent constraints on t t backgrounds in the corresponding SRs. Additional constraints on t t background in this channel are provided through four singlelepton CRs (bbA, bbB, bbF, and bbG). A single-electron (muon) CR for the 1 b tag SR requires exactly one electron (muon) with pT > 30 GeV. The lepton must satisfy tight isolation and identification criteria. The MT observable calculated from the lepton momenta and pmiss must sat T isfy 50 < MT < 160 GeV. Except for the requirement on min φ (−→p jTeti , −→p Tmiss), each of the selection criteria for the 1 b tag signal category must also be satisfied. Analogous CRs for the 2 b tag signal category are formed by applying the corresponding signal selection criteria. As in the dileptonic t t CRs for the bb + pmiss searches, the lepton is removed T from the pmiss calculation. T W + jets A W + jets CR for the + jets t t + pTmiss search (slB) is created by requiring zero b tags. The MT > 160 GeV requirement from the + jets signal selection is maintained, however, the cuts on MTW2 and min φ (−→p jTeti , −→p Tmiss) are removed. Control regions enriched in both W + jets and Z + jets (hadB, hadF) are formed for the all-hadronic t t + pmiss cateT gories by modifying the SR selections to require zero b tags. In addition, dedicated W+jets CRs (hadC, hadG) are defined by requiring the presence of an isolated, identified lepton with pT > 30 GeV and MT < 160 GeV. The W/Z + jets and W+jets CRs are both categorized using the number of RTTs, as in the corresponding SRs. The prefit yields and pmiss disT tributions in the hadB and hadC regions are observed to differ from those of data. The discrepancy is due to a mismodeling of hadronic activity in the simulation, which leads to an overestimation of the selection efficiency for the Z+jets and W+jets processes. Reasonable agreement is achieved through the fit, as is shown in Figs. 7 and 5. The W + jets process contributes the second-largest background in the 1 b tag SR of the bb + pmiss channel. This T background is constrained via the single-lepton CRs (bbA, bbB, bbF, bbG) of the bb + pmiss channel, which were intro T duced previously in the context of constraints on + jets t t backgrounds. Z + jets The Z (νν¯ ) + jets process is a significant source of background in the all-hadronic t t + pmiss SRs. This T background is partially controlled via the W/Z + jets CRs (hadB, hadF) described previously. An additional constraint is derived from a distinct Z ( ) + jets CR (hadD), in which two oppositely-charged, same-flavor leptons are required to CMS Control regions for 1 b tag bb+pTmiss 2.2 fb-1 (13 TeV) Fig. 6 Observed data, and prefit and fitted background-only event yields in the control regions associated with the bb + pmiss signal region T with 1 b tag (upper) and with 2 b tags (lower). The 1 lepton, ≥ 1 b control regions (bbA, bbB, bbF and bbG in Table 2) are used to constrain W + jets and t t backgrounds in the bb + pmiss signal regions. The dilep T tonic control regions (bbC-bbE, bbH-bbJ) are used to constrain Z + jets and t t backgrounds. The lower panels show the ratio of observed to fitted background yields. In both panels, the statistical uncertainties of the data are indicated as vertical error bars and the fit uncertainties as hatched bands. Prefit yields and the ratios of prefit to fitted background expectations are shown as dashed magenta histograms pass tight isolation and identification requirements. The mass of the lepton pair must fall between 60 and 120 GeV. A prediction for the pmiss distribution in the hadronic SRs is T obtained by subtracting the lepton momenta in the pmiss calT CMS 0l,0b control region for 0,1RTT all-hadronic tt+pTmiss 250 300 350 400 CMS 1l,0b control region for 0,1RTT all-hadronic tt+pTmiss n i b / 1200 s t n ve 1000 E 800 600 400 200 Fig. 7 Observed data, and prefit and fitted background-only pmiss disT tributions in two control regions (hadB and hadC in Table 2) for the 0,1 RTT hadronic t t + pmiss signal region with 0 leptons (upper) and with T 1 lepton (lower) and 0 b tags. The 0 lepton control region is used to constrain W + jets and Z + jets backgrounds. The 1 lepton CR provides an additional constraint on W + jets background. The last bin contains overflow events. The lower panels show the ratios of observed data to fitted background yields. In both panels, the statistical uncertainties of the data are indicated as vertical error bars and the fit uncertainties are indicated as hatched bands. Prefit yields and the ratios of prefit to fitted background expectations are shown as dashed magenta histograms culation. The Z ( ) + jets CR is not categorized in the number of RTTs because of the negligible yields obtained with two RTT tags. The selections for jets and pmiss used in the T Fig. 8 Observed data, and prefit and fitted background-only, leptonsubtracted pmiss distributions in the dileptonic control region (hadD in T Table 2) for the all-hadronic t t + pmiss signal regions. This control region T is used to constrain Z (νν¯ ) + jets background. The selections for jets and pmiss used in the 0,1 RTT signal region are applied, with those on T pmiss applied to lepton-subtracted pmiss. The signal region requirements T T on min φ (−→p jTeti , −→p Tmiss) and b tags are removed to increase Z + jets yields. The last bin contains overflow events. The lower panel shows the ratios of observed data to fitted background yields. In both panels, the statistical uncertainties of the data are indicated as vertical error bars and the fit uncertainties are indicated as hatched bands. Prefit yields and the ratios of prefit to fitted background expectations are shown as dashed magenta histograms 0,1 RTT SR are applied in the Z ( ) + jets CR, with those on pmiss applied to lepton-subtracted pmiss. The require T T φ (−→p jTeti , −→p Tmiss) and b tags are removed to ments on min increase Z +jets yields. Figure 8 demonstrates that the leptonsubtracted pmiss distribution observed in the Z ( ) + jets CR T of the all-hadronic channel is not well described by the prefit expectation. Agreement substantially improves following the fit. The Z (νν¯ ) + jets process is also a significant background in the bb + pmiss SRs. This background is con T strained with four distinct CRs: bbC, bbD, bbH, and bbI. The Z (ee) and Z (μμ) CRs require two electrons and two muons with pT > 30 GeV, respectively. The isolation and identification criteria applied on the leading- pT lepton are identical to those used in the W + jets CRs for the bb + pTmiss channel. The subleading lepton is required to satisfy a looser set of isolation and identification criteria, as in the dileptonic CRs. The leptons must be consistent with the decay of a Z boson; oppositecharge, same-flavor requirements are imposed, and the leptons must satisfy a constraint on the dilepton mass of 70 < m < 110 GeV. As in the W + jets and dileptonic t t CRs, events must also satisfy all but the min φ (−→p jTeti , −→p Tmiss) selection criteria of the corresponding 1 b tag or 2 b tag signal category. As in the Z + jets CR for all-hadronic t t channel, lepton momenta are subtracted in the pmiss calculation to approximate the distribu T tion of pmiss from Z (νν¯ ) + jets expected in the bb + pmiss T T SRs. 5 Signal extraction A potential DM signal could be revealed as an excess of events relative to SM expectations in a region of high pmiss. The shape of the observed pmiss distribution pro T T vides additional information that is used in this analysis to improve the sensitivity of the search. A potential signal is searched for via simultaneous template fits to the pmiss disT tributions in the SRs and the associated CRs defined in Sects. 4.1 and 4.2. Signal and background pmiss templates T are derived from simulation and are parameterized to allow for constrained shape and normalization variations in the fits. The fits are performed using the RooStats statistical software package [ 59 ]. The effects of uncertainties in the normalizations and in the pmiss shapes of signal and background pro T cesses are represented as nuisance parameters. Uncertainties that only affect normalization are modeled using nuisance parameters with log-normal probability densities. Uncertainties that affect the shape of the pmiss distribution, which may T also include an overall normalization effect, are incorporated using a template “morphing” technique. These treatments, as well as the approach used to account for MC statistical uncertainties on template predictions, follow the procedures described in Ref. [ 60 ]. Within each search channel, additional unconstrained nuisance parameters scale the normalization of each dominant background process (t t , W + jets, and Z + jets) across the SRs and CRs. For example, a single parameter is associated with the contribution of the + jets t t process in the allhadronic t t + pmiss SRs and CRs. A separate parameter is T associated with the + jets t t background in the bb + pTmiss SRs and CRs. These nuisance parameters allow the data in the background-enriched CRs to constrain the background estimates in the SRs to which they correspond. Because separate nuisance parameters are used for each search channel, a given normalization parameter cannot affect background predictions in unassociated search channels. The yields and pmiss shapes of subdominant backgrounds vary in the fit only T through the constrained nuisance parameters. Signal yields in the SRs and associated CRs are scaled simultaneously by signal strength parameters (μ), defined as the ratio of the signal cross section to the theoretical cross section, μ = σ/σTH. The μ parameters scale signal normalization coherently across regions, and thus account for signal contamination in the CRs. Signal extraction is performed for the individual search channels as well as for their combination. The separate fits to the individual signal and associated CRs provide independent estimates of bb + χ χ and t t + χ χ contributions in each channel. In this fitting scenario, separate signal strength parameters are used for each of the search channels. The bb + χ χ process is considered as a potential signal in the 1 b tag and 2 b tag regions of the bb + pmiss channel. The T t t + χ χ process is searched for in all SRs of the bb + pmiss T and t t + pmiss channels separately. The contribution of the T bb + χ χ process in the all-hadronic t t + pmiss channel is T negligible due to the jet multiplicity requirement. An inclusive fit to all signal and CRs is also performed. This fit uses a single signal strength parameter to extract the combined contribution of t t + χ χ and bb + χ χ in data. Additional details on the per-channel and combined fits are provided in Sect. 7. 6 Systematic uncertainties Table 3 summarizes the uncertainties considered in the signal extraction fits. The procedures used to evaluate the uncertainties are described later in this section. Normalization uncertainties are expressed relative to the predicted central values of the corresponding nuisance parameters. These uncertainties are used to specify the widths of the associated lognormal probability densities. The integrated luminosity, b tagging efficiency, pTmiss trigger efficiency, pileup, and multijet/single t background normalization uncertainties are taken to be fully correlated across SRs and CRs. Shape uncertainties are expressed in Table 3 as the change in the prefit yields of the lowest and highest pmiss bins resulting from T a variation of the corresponding nuisance by ± 1 standard deviation (s.d.). These uncertainties are propagated to the fit by using the full pmiss spectra obtained from ±1 s.d. vari T ations of the corresponding nuisance parameters [ 60 ]. The PDF and jet energy scale shape uncertainties are taken to be fully correlated across SRs and CRs. In general, the uncertainty estimation is performed in the same way for signal and background processes; however, the uncertainty from missing higher-order corrections for signal processes, which is approximately 30% at LO in QCD, is not considered to facilitate a comparison with other CMS DM results. The following sources of uncertainty correspond to constrained normalization nuisance parameters in the fit: – Integrated luminosity An uncertainty of 2.7% is used for the integrated luminosity of the data sample [ 61 ]. 0 la iss v e – 3 i nmT g th ig p s +ted in s e d is tnh bb quo ltae ic mpT ititisaen tea1bg rsaeeng llrrceyo iltepnoD eett()+ .72 .20 – 46 02 – 002 – .22 4 – 1 ..6–122 .6–014 .9–017 .1–412 1–125 3–123 – – – re th th fu c n , n i s e u s ie b tic jte n t t o i a a d +tr e m e r tse .Z cn ied y .e u s s i n f ( e o o n ap c n ) io t n le3brauySmm ilirsacaeegnhgo .ititrrssonobuhF lrrsssaecaehnno ittreacnyn itiltitirr(szaaceaeonunnom% tltiitrIseaedugnnoym liepuP jt/rrfseeaaachvyvoflZW+ .lllitirraeazanbk–gnooYDm .tlilitireazabkggnnoonSm .tjltilitireazabkgunoonmM .ltiilitirseazanpodnoonmM ifceecnyfiTTR itifeacecngggnybfi itfeceecnponyfiL issmitirrfeececggnyfipT titirrfeeececnggponyfiL titir()sceaeaenpunh% sFPD tlrsJeceeaegyn itirreeaghngukopqTwpT isnoobD,μμFR WttZ,/γμμFR+ tt,μμFR jt/seZWμR+ jt/seZWμF+ jt/tirrsececoonEZKWW+ aT in i c d a U N S Table 4 Fitted background yields for a background-only hypothesis in the t t + pmiss and bb+ pmiss signal regions. The yields are obtained from T T separate fits to the bb + pmiss and individual t t + pmiss search channels. T T Prefit yields for DM produced via a pseudoscalar mediator with mass ma = 50 GeV and a scalar mediator with mass mφ = 100 GeV are also shown. Mediator couplings are set to gq = gχ = 1, and a DM particle of mass mχ = 1 GeV is assumed. Uncertainties include both statistical and systematic components Channel Signal region ee t t W + jets Z + jets Single t Diboson Multijets Misid. lepton Background Data ma = 50 GeV t t + χ χ bb + χ χ mφ = 100 GeV t t + χ χ bb + χ χ – Pileup modeling Systematic uncertainties due to pileup modeling are taken into account by varying the total inelastic cross section used to calculate the data pileup distributions by ± 5%. Normalization differences in the range of 0.2–1.4% result from reweighting the simulation accordingly. – W/Z + heavy-flavor fraction The uncertainty in the fraction of W/Z + heavy-flavor jets is assigned to account for the usage of CRs dominated by light-flavor jets in constraining the prediction of W + jets and Z + jets in SRs that require b tags. The flavor fractions for the W + jets and Z + jets processes are allowed to vary independently within 20% [ 62–65 ]. – Drell–Yan background: The uncertainties in the datadriven Drell–Yan background estimates for the dileptonic channels are 64% (ee) and 43% (μμ). These uncertainties are dominated by the statistical uncertainties in quantities used to extrapolate yields from a region near the Z boson mass to regions away from it. Again, these relatively large uncertainties have little effect on the sensitivity of the search. – Multijet background normalization Uncertainties of 50–100% (depending on the SR) are applied in the normalization of multijet backgrounds to cover tail effects that are not well modeled by the simulation. – Misidentified-lepton background The sources of uncertainty in the misidentified-lepton background for the dileptonic search stem from the uncertainty in the measured misidentification rate, and from the statistical uncertainty of the single-lepton control sample to which the rate is applied. The uncertainties per channel are 200% (ee), 48% (eμ), 30% (μμ), and are dominated by the statistical uncertainty associated with the singlelepton control sample. Because the misidentified lepton background is small, these relatively large uncertainties do not significantly degrade the sensitivity of the search. – RTT efficiency Jet energy scale and resolution uncertainties are propagated to the RTT efficiency scale factors by using modified shape templates in the efficiency extraction fit. A systematic uncertainty due to the choice of parton showering scheme is estimated by comparing the efficiencies obtained with default and alternative pmiss templates. The default simulation is showered T using Pythia8.205, which implements dipole-based parton showering. The alternative templates are derived from simulated events that are showered with Herwig [ 66 ], which uses an angular-ordered shower model. Overall, statistical plus systematic uncertainties of 6, 3, and 3% are assigned for the hadronic tag, hadronic mistag, and nonhadronic mistag scale factors, respectively. These corCMS ee dileptonic tt+pTmiss CMS μμ dileptonic tt+pTmiss 50 respond to an overall normalization uncertainty for the t t + pmiss SRs of 4%. T – b tagging efficiency The b tagging efficiency and its uncertainty are measured using independent control samples. Uncertainties from gluon splitting, the b quark fragmentation function, and the selections used to define the control samples are propagated to the efficiency scale facof an example signal (pseudoscalar mediator, ma = 300 GeV and mχ = 1 GeV) is scaled up by a factor of 20. The last bin contains overflow events. The lower panels of each plot show the ratio of observed data to fitted background. The uncertainty bands shown in these panels are the fitted values, and the magenta lines correspond to the ratio of prefit to fitted background expectations tors [ 31 ]. The corresponding normalization uncertainty ranges from 2.2 to 12%. – Lepton identification and trigger efficiency: The uncertainty in lepton identification and triggering efficiency is measured with samples of Z bosons decaying to dielectrons and dimuons [ 34 ]. The corresponding normalization uncertainty ranges from 2 to 4%. CMS 0,1RTT all-hadronic tt+pTmiss CMS 2RTT all-hadronic tt+pTmiss in 160 b t/s 140 n ve 120 E 100 80 60 40 20 ted 1.5 it /F 1.0 Fig. 10 The pmiss distributions in the following signal regions: all T hadronic t t + pmiss with 0 or 1 RTTs (upper left), all-hadronic t t + pmiss T T with 2 RTTs (upper right), bb + pmiss with 1 b tag (lower left), and T bb + pmiss with 2 b tags (lower right). The pmiss distributions of back T T ground correspond to background-only fits to the individual t t + pmiss T and bb+ pTmiss signal regions and associated background control regions. – pmiss trigger Uncertainties of 0.3–2% (depending on the T SR) are associated with the efficiency scale factors of the pmiss trigger. The efficiency of this trigger is measured T using data collected with the single-lepton triggers. For values of pmiss > 200 GeV, these data primarily consist T of W + jets events. The prefit pTmiss distribution of an example signal (pseudoscalar mediator, ma = 300 GeV and mχ = 1 GeV) is scaled up by a factor of 20. The last bin contains overflow events. The lower panels of each plot show the ratio of observed data to fitted background. The uncertainty bands shown in these panels are the fitted values, and the magenta lines correspond to the ratio of prefit to fitted background expectations The following sources of uncertainty correspond to constrained pmiss shape nuisance parameters in the fit: T – PDF uncertainties Uncertainties due to the choice of PDFs are estimated by reweighting the samples with the ensemble of PDF replicas provided by NNPDF3.0 [ 67 ]. mφ , mχ (GeV) μ(t t + φ → t t χ χ ) μ(bb + φ → bbχ χ ) Table 5 Observed and expected 95% CL upper limits on the ratios (μ) of the observed t t + χ χ and bb + χ χ cross sections to the simplified model expectations. The limits correspond to separate fits to the bb + pmiss and individual T t t + pmiss search channels. DM T mediators with scalar couplings of gq = gχ = 1 are assumed Table 6 Same as Table 5, but for DM mediators with pseudoscalar couplings. Again, mediator couplings correspond to gq = gχ = 1 The standard deviation of the reweighted pmiss shapes is T used as an estimate of the uncertainty. – Jet energy scale Reconstructed jet four-momenta in the simulation are simultaneously varied according to the uncertainty in the jet energy scale [ 29 ]. Jet energy scale uncertainties are coherently propagated to all observables including pmiss. T – Top quark pT reweighting Differential measurements of top quark pair production show that the measured pT spectrum of top quarks is softer than that of simulation. Scale factors to cover this effect have been derived in previous CMS measurements [ 68 ] and are applied to all simulated SM t t samples by default. The uncertainty in the top quark pT spectrum is estimated from a comparison with the spectrum obtained without reweighting. – Higher-order QCD corrections The uncertainties due to missing higher-order QCD corrections in the LO samples are estimated by generating alternative event samples in which the factorization and renormalization scale parameters (μF, μR) are simultaneously increased or decreased by a factor of two. These uncertainties are correlated across the bins of the pmiss distribution. Uncertainties T in the NLO K-factors applied to W + jets and Z + jets simulation are determined by recalculating the K-factor with μF and μR independently varied by a factor of two up or down. – EWK corrections Uncertainties in the K-factors applied to W + jets and Z + jets simulation from missing higherorder EWK corrections are estimated by taking the difference in results obtained with and without the EWK correction applied. – Simulation statistics: Shape uncertainties due to the limited sizes of the simulated signal and background samples are included via the method of Barlow and Beeston [ 60, 69 ]. This approach allows each bin of the pmiss distributions to independently fluctuate according T to Poisson statistics. 7 Results and interpretation Separate signal strength parameters are first determined from fits to each of the bb + pmiss and t t + pmiss channels. These T T fits use the predicted cross sections and pmiss shapes from the T LHC DMF signal models with gq = gχ = 1. The fits result in independent upper limits on signal yields for the bb + χ χ and t t + χ χ processes, which are reported in Sect. 7.1. Next, all SRs and CRs are simultaneously fit under the hypothesis of combined t t + χ χ and bb + χ χ contributions. In this case, a single signal strength parameter is used, which results in a combined best fit estimate of the t t + χ χ and bb + χ χ signal yields. Again, cross section predictions for t t + χ χ and bb + χ χ assume gq = gχ = 1. Results from this fit are reported in Sect. 7.2. The most interesting DM scenarios to explore at the LHC involve on-shell mediator decays to χ χ , which corresponds to mφ/a > 2mχ . Kinematic variables and cross sections are independent of mχ in this regime [ 21 ]. The mχ < 10 GeV region is of particular interest because of the strong phenomenological and theoretical motivations for low-mass DM [ 70 ] and the relative strength of collider experiments in this mass range [ 71 ]. For these reasons, the DM mass has been fixed to mχ = 1 GeV in all signal extraction fits. The results obtained with mχ = 1 GeV are valid for other values of mχ < mφ/a/2 provided they are not too near the kinematic threshold. 7.1 Individual search results Table 4 provides the background yields in the SRs obtained from background-only fits to the bb + pmiss and individual T t t + pmiss search channels. Relative nuisance parameter shifts T – defined as (pfit − pprefit)/σp, where p represents the parameter value and σp its fit uncertainty – do not indicate any particular tension in these fits. The largest shifts correspond to the nuisance parameters for the EWK correction for the W + jets and Z + jets processes in the bb + pmiss channel T (+0.8), to the μF , μR scale uncertainty in the t t process in the + jets t t + pTmiss channel (+0.6), and to the lepton efficiency in the all-hadronic t t + pmiss channel (−1.9). The T nuisance parameter shifts account for residual mismodeling of the yields by the simulation in the background-enriched regions. The background-only fitted pmiss distributions in the T eight SRs are shown in Figs. 9 and 10. 10 1 σTH105 / σ = μ104 n o t i lim103 r e p p U102 10 1 10 10 Observed limit 95% CL Expected limit 95% CL bb+pmiss (bb+χχ only) T bb+pmiss T Dileptonic tt+pmiss l+jets tt+pmiss T T All-hadronic tt+pmiss T 102 mφ [GeV] Fig. 11 The ratio (μ) of 95% CL upper limits on the bb + χ χ and t t + χ χ cross sections to simplified model expectations. The limits are obtained from fits to the individual bb + pmiss and t t + pmiss search T T channels for the hypothesis of a scalar mediator (upper) or a pseudoscalar mediator (lower). A fermionic DM particle with a mass of 1 GeV is assumed in both panels. Mediator couplings correspond to gq = gχ = 1 Table 7 Observed and expected 95% CL upper limits on the ratio (μ) of the combined tt + χχ and bb + χχ cross sections to the simplified model expectation. The limits are obtained from a combined fit to all signal and background control regions. DM mediators with scalar or pseudoscalar couplings are assumed. Mediator couplings correspond to gq = gχ = 1 mφ/a, mχ (GeV) μ(tt/bb + φ → ttχχ /bbχχ ) μ(tt/bb + a to ttχχ/bbχχ ) Obs. Exp. The fitted background-only pmiss distributions of the indiT vidual search channels are assessed using the likelihood ratio for the saturated model, which provides a generalization of the χ 2 goodness-of-fit test [ 72,73 ]. Pseudodata are generated from the fitted MC yields to determine the distribution of the likelihood ratio. The p-values obtained are larger than 0.5 for each channel except for the all-hadronic t t + pmiss T channel, for which a low p-value of 0.01 is determined. This value appears to result from the scatter in the 0,1 RTT CRs. No significant excess in the individual search channels is observed. Upper limits are set on the bb+χ χ and t t +χ χ production cross sections. The limits are calculated using a modified frequentist approach (CLs) with a test statistic based on the profile likelihood in the asymptotic approximation [ 74–76 ]. For each signal hypothesis, 95% confidence level (CL) upper limits on the signal strength parameter μ are determined. Tables 5 and 6 list the expected limits on μ obtained for various signal hypotheses. Figure 11 shows the expected and observed limits on μ as a function of the mediator mass for mχ = 1 GeV. The all-hadronic and + jets t t + pTmiss channels provide the highest sensitivity to the t t + χ χ process for all mediator masses considered. Expected limits on the t t + χ χ process from the bb + pmiss channel are comparable with those of T the dileptonic t t + pmiss channel. The only relevant search T channel for the bb + χ χ process is bb + pmiss, from which T observed upper limits of μ ≥ 26 are obtained for the pseudoscalar mediator hypothesis (see Table 6). The relatively weak sensitivity of the bb + pmiss channel in the search is T due, in part, to the specific signal model considered; the performance of this channel would improve in models in which the mediator couplings to up-type quarks are suppressed. In all search channels, the expected sensitivity to lowmass scalar mediators is better than that for low-mass pseudoscalars. This reflects the higher predicted cross section for the low-mass scalar, which is approximately 40 times larger than that of the pseudoscalar for a mediator mass of 10 GeV [ 50 ]. Scalar and pseudoscalar cross sections become comparable at mediator masses of around 200 GeV and above. The expected scalar limits therefore rise quickly with increasing mass, while the limits for the pseudoscalar mediator change less, as can be seen from Tables 5 and 6. 7.2 Combined search results Signal region yields obtained from a simultaneous backgroundonly fit of all of the search channels are similar to those listed in Table 4. Fitted pmiss distributions in the eight SRs T are nearly indistinguishable from those of Figs. 9 and 10. The nuisance parameter shifts in the combined fit are consistent with those of the individual channel fits, while the fit uncertainty in the b tagging efficiency nuisance parameter becomes more tightly constrained. The p value of the saturated likelihood goodness-of-fit test is 0.11, which indicates no significant deviation with respect to background predictions. A simultaneous signal+background fit is performed using all SRs and CRs, and 95% CL upper limits are set on the cross section ratio μ for DM produced in association with heavyflavor quark pairs. Table 7 provides limits obtained for the scalar and pseudoscalar mediator hypotheses. These limits are presented graphically in Fig. 12. The combination of t t + pmiss and bb + pmiss search channels enhances sensitivity to T T both the scalar and the pseudoscalar mediator scenarios. Signal cross sections may be scaled to larger values of gq and gχ using the relationship given in Ref. [ 21 ]. This simple scaling approximation is valid as long as the mediator width remains below 20% of its mass. With gq = gχ = 1.5, the relative width of the 500 GeV scalar (pseudoscalar) mediator is 14% (18%). The relative width decreases with decreasing mediator mass. For coupling values of gq = gχ = 1.5, the pmiss distributions of the various media T tor hypotheses are also unchanged with respect to those obtained with gq = gχ = 1, thus the limits of Fig. 5 may be scaled accordingly [ 21 ]. Assuming coupling values of gq = gχ = 1.5, the observed (expected) 95% CL exclusions are mφ < 124 (105) GeV for a scalar mediator, and ma < 128 (76) GeV for a pseudoscalar mediator. 2.2 fb-1 (13 TeV) Pseudoscalar, Dirac, gq=1, gχ=1, mχ=1 GeV CMS Median expected 95% CL 68% expected 95% expected Observed H T σ/102 σ = μ n o t i m i l r 10 e p p U H T σ/102 σ = μ n o t i m i l r 10 e p p U 1 10 10 102 Fig. 12 The ratios (μ) of the 95% CL upper limits on the combined t t +χ χ and bb+χ χ cross section to simplified model expectations. The limits are obtained from combined fits to the t t + pmiss and bb + pmiss T T signal and background control regions for the hypothesis of a scalar mediator (upper) and a pseudoscalar mediator (lower). A fermionic DM particle with a mass of 1 GeV is assumed in both panels. Mediator couplings correspond to gq = gχ = 1 8 Summary A search for an excess of events with large missing transverse momentum ( pTmiss) produced in association with a pair of heavy-flavor quarks has been performed with a sample 2.2 fb-1 (13 TeV) of proton-proton interaction data at a center-of-mass energy of 13 TeV. The data correspond to an integrated luminosity of 2.2 fb−1 collected with the CMS detector at the CERN LHC. The analysis explores bb + pmiss and the dileptonic, T +jets, and all-hadronic t t + pmiss final states. A resolved top T quark tagger is used to categorize events in the all-hadronic channel. No significant deviation from the standard model background prediction is observed. Results are interpreted in terms of dark matter (DM) production, and constraints are placed on the parameter space of simplified models with scalar and pseudoscalar mediators. The DM search channels are considered both individually and, for the first time, in combination. The combined search excludes production cross sections larger than 1.5 or 1.8 times the values predicted for a 10 GeV scalar mediator or a 10 GeV pseudoscalar mediator, respectively, for couplings of gq = gχ = 1. The limits presented are the first achieved on simplified models of dark matter produced in association with heavy-flavor quark pairs. Acknowledgements 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 centers 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); SENESCYT (Ecuador); 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, RFBR and RAEP (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (UK); DOE and NSF (USA). Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract No. 675440 (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 (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the National Science 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 Priorities Research Program by Qatar National Research Fund; the Programa Clarín-COFUND del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EUESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and 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. Page 22 of 36 CMS Collaboration Yerevan Physics Institute, Yerevan, Armenia A. M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Vienna, Austria W. Adam, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M. Dragicevic, J. Erö, M. Flechl, M. Friedl, R. Frühwirth1, V. M. Ghete, C. Hartl, N. Hörmann, J. Hrubec, M. Jeitler1, A. König, I. Krätschmer, D. Liko, T. Matsushita, I. Mikulec, D. Rabady, N. Rad, B. Rahbaran, H. Rohringer, J. Schieck1, J. Strauss, W. Waltenberger, C.-E. Wulz1 Institute for Nuclear Problems, Minsk, Belarus V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez National Centre for Particle and High Energy Physics, Minsk, Belarus N. Shumeiko Vrije Universiteit Brussel, Brussel, Belgium S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover, S. Lowette, S. Moortgat, L. Moreels, A. Olbrechts, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs 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. Chinellato3, A. Custódio, E. M. Da Costa, G. G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, L. M. Huertas Guativa, H. Malbouisson, C. Mora Herrera, L. Mundim, H. Nogima, A. Santoro, A. Sznajder, E. J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela Pereira Universidade Estadual Paulistaa , Universidade Federal do ABCb, São Paulo, Brazil S. Ahujaa , C. A. Bernardesa , T. R. Fernandez Perez Tomeia , E. M. Gregoresb, P. G. Mercadanteb, C. S. Moona , S. F. Novaesa , Sandra S. Padulaa , D. Romero Abadb, J. C. Ruiz Vargasa Institute for Nuclear Research and Nuclear Energy, Sofia, Bulgaria A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Rodozov, S. Stoykova, G. Sultanov, M. Vutova University of Sofia, Sofia, Bulgaria A. Dimitrov, I. Glushkov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China W. Fang5, X. Gao5 Institute of High Energy Physics, Beijing, China M. Ahmad, J. G. Bian, G. M. Chen, H. S. Chen, M. Chen, Y. Chen, C. H. Jiang, D. Leggat, Z. Liu, F. Romeo, S. M. Shaheen, A. Spiezia, J. Tao, C. Wang, Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China Y. Ban, G. Chen, Q. Li, S. Liu, Y. Mao, S. J. Qian, D. Wang, Z. Xu Universidad de Los Andes, Bogotá, Colombia C. Avila, A. Cabrera, L. F. Chaparro Sierra, C. Florez, J. P. Gomez, C. F. González Hernández, J. D. Ruiz Alvarez6 Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia N. Godinovic, D. Lelas, I. Puljak, P. M. Ribeiro Cipriano, T. Sculac Faculty of Science, University of Split, Split, Croatia Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, T. Susa Charles University, Prague, Czech Republic M. Finger7, M. Finger Jr.7 Universidad San Francisco de Quito, Quito, Ecuador E. Carrera Jarrin Egyptian Network of High Energy Physics, Academy of Scientific Research and Technology of the Arab Republic of Egypt, Cairo, Egypt Y. Assran8,9, M. A. Mahmoud9,10, A. Mahrous11 National Institute of Chemical Physics and Biophysics, Tallinn, Estonia R. K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, A. Tiko, C. Veelken Department of Physics, University of Helsinki, Helsinki, Finland P. Eerola, J. Pekkanen, M. Voutilainen Centre de Calcul de l’Institut National de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne, France S. Gadrat Georgian Technical University, Tbilisi, Georgia A. Khvedelidze7 Tbilisi State University, Tbilisi, Georgia Z. Tsamalaidze7 RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany C. Autermann, S. Beranek, L. Feld, M. K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C. Schomakers, J. Schulz, T. Verlage Institut für Experimentelle Kernphysik, Karlsruhe, Germany M. Akbiyik, C. Barth, S. Baur, C. Baus, J. Berger, E. Butz, R. Caspart, T. Chwalek, F. Colombo, W. De Boer, A. Dierlamm, B. Freund, R. Friese, M. Giffels, A. Gilbert, D. Haitz, F. Hartmann14, S. M. Heindl, U. Husemann, F. Kassel14, S. Kudella, H. Mildner, M. U. Mozer, Th. Müller, M. Plagge, G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H. J. Simonis, R. Ulrich, 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 S. Kesisoglou, A. Panagiotou, N. Saoulidou MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary M. Csanad, N. Filipovic, G. Pasztor Wigner Research Centre for Physics, Budapest, Hungary G. Bencze, C. Hajdu, D. Horvath18, F. Sikler, V. Veszpremi, G. Vesztergombi19, A. J. Zsigmond Institute of Nuclear Research ATOMKI, Debrecen, Hungary N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi Institute of Physics, University of Debrecen, Debrecen, Hungary M. Bartók19, P. Raics, Z. L. Trocsanyi, B. Ujvari Indian Institute of Science (IISc), Bangalore, India S. Choudhury, J. R. Komaragiri National Institute of Science Education and Research, Bhubaneswar, India S. Bahinipati21, S. Bhowmik, P. Mal, K. Mandal, A. Nayak22, D. K. Sahoo21, N. Sahoo, S. K. Swain Saha Institute of Nuclear Physics, HBNI, Kolkata, India R. Bhattacharya, S. Bhattacharya, K. Chatterjee, S. Dey, S. Dutt, S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S. Mukhopadhyay, S. Nandan, A. Purohit, A. Roy, D. Roy, S. Roy Chowdhury, S. Sarkar, M. Sharan, S. Thakur 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. Mohanty14, P. K. Netrakanti, L. M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A, Mumbai, India T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G. B. Mohanty, B. Parida, N. Sur, B. Sutar Indian Institute of Science Education and Research (IISER), Pune, India S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S. Sharma 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, A. Sharmaa , L. Silvestrisa ,14, R. Vendittia , 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 , M. Cuffiania ,b, G. M. Dallavallea , F. Fabbria , A. Fanfania ,b, D. Fasanellaa ,b, P. Giacomellia , L. Guiduccia ,b, S. Marcellinia , G. Masettia , F. L. Navarriaa ,b, A. Perrottaa , A. M. Rossia ,b, T. Rovellia ,b, G. P. Sirolia ,b, N. Tosia ,b,14 INFN Sezione di Cataniaa , Università di Cataniab, Catania, Italy S. Albergoa ,b, S. Costaa ,b, A. Di Mattiaa , F. Giordanoa ,b, R. Potenzaa ,b, A. Tricomia ,b, C. Tuvea ,b INFN Sezione di Firenzea , Università di Firenzeb, Florence, Italy G. Barbaglia , V. Ciullia ,b, C. Civininia , R. D’Alessandroa ,b, E. Focardia ,b, P. Lenzia ,b, M. Meschinia , S. Paolettia , L. Russoa ,28, G. Sguazzonia , D. Stroma , L. Viliania ,b,14 INFN Laboratori Nazionali di Frascati, Frascati, Italy L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera14 INFN Sezione di Genovaa , Università di Genovab, Genoa, Italy V. Calvellia ,b, F. Ferroa , M. R. Mongea ,b, E. Robuttia , S. Tosia ,b INFN Sezione di Milano-Bicoccaa , Università di Milano-Bicoccab, Milan, Italy L. Brianzaa ,b,14, F. Brivioa ,b, V. Ciriolo, M. E. Dinardoa ,b, S. Fiorendia ,b,14, S. Gennaia , A. Ghezzia ,b, P. Govonia ,b, M. Malbertia ,b, S. Malvezzia , R. A. Manzonia ,b, D. Menascea , L. Moronia , M. Paganonia ,b, K. Pauwels, D. Pedrinia , S. Pigazzinia ,b, S. Ragazzia ,b, T. Tabarelli de Fatisa ,b INFN Sezione di Napolia , Università di Napoli ’Federico II’b, Napoli, Italy, Università della Basilicatac, Potenza, Italy , Università G. Marconid , Rome, Italy S. Buontempoa , N. Cavalloa ,c, S. Di Guidaa ,d ,14, F. Fabozzia ,c, F. Fiengaa ,b, A. O. M. Iorioa ,b, L. Listaa , S. Meolaa ,d ,14, P. Paoluccia ,14, C. Sciaccaa ,b, F. Thyssena INFN Sezione di Padovaa , Università di Padovab, Padova, Italy, Università di Trentoc, Trento, Italy P. Azzia ,14, N. Bacchettaa , S. Badoera , M. Bellatoa , L. Benatoa ,b, M. Benettonia , D. Biselloa ,b, A. Bolettia ,b, R. Carlina ,b, A. Carvalho Antunes De Oliveiraa ,b, P. Checchiaa , P. De Castro Manzanoa , T. Dorigoa , U. Gasparinia ,b, A. Gozzelinoa , S. Lacapraraa , M. Margonia ,b, A. T. Meneguzzoa ,b, N. Pozzobona ,b, P. Ronchesea ,b, R. Rossina ,b, F. Simonettoa ,b, E. Torassaa , M. Zanettia ,b, P. Zottoa ,b INFN Sezione di Perugiaa , Università di Perugiab, Perugia, Italy L. Alunni Solestizia ,b, G. M. Bileia , D. Ciangottinia ,b, L. Fanòa ,b, P. Laricciaa ,b, R. Leonardia ,b, G. Mantovania ,b, V. Mariania ,b, M. Menichellia , A. Sahaa , A. Santocchiaa ,b, D. Spiga INFN Sezione di Pisaa , Università di Pisab, Scuola Normale Superiore di Pisac, Pisa, Italy K. Androsova , P. Azzurria ,14, G. Bagliesia , J. Bernardinia , T. Boccalia , L. Borrello, R. Castaldia , M. A. Cioccia ,b, R. Dell’Orsoa , G. Fedia , 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 Romaa , Sapienza Università di Romab, Rome, Italy L. Baronea ,b, F. Cavallaria , M. Cipriania ,b, D. Del Rea ,b,14, M. Diemoza , S. Gellia ,b, E. Longoa ,b, F. Margarolia ,b, B. Marzocchia ,b, P. Meridiania , G. Organtinia ,b, R. Paramattia ,b, F. Preiatoa ,b, S. Rahatloua ,b, C. Rovellia , F. Santanastasioa ,b INFN Sezione di Torinoa , Università di Torinob, Torino, Italy, Università del Piemonte Orientalec, Novara, Italy N. Amapanea ,b, R. Arcidiaconoa ,c,14, S. Argiroa ,b, M. Arneodoa ,c, N. Bartosika , R. Bellana ,b, C. Biinoa , N. Cartigliaa , F. Cennaa ,b, M. Costaa ,b, R. Covarellia ,b, A. Deganoa ,b, N. Demariaa , B. Kiania ,b, C. Mariottia , S. Masellia , E. Migliorea ,b, V. Monacoa ,b, E. Monteila ,b, M. Montenoa , M. M. Obertinoa ,b, L. Pachera ,b, N. Pastronea , M. Pelliccionia , G. L. Pinna Angionia ,b, F. Raveraa ,b, A. Romeroa ,b, M. Ruspaa ,c, R. Sacchia ,b, K. Shchelinaa ,b, V. Solaa , A. Solanoa ,b, A. Staianoa , P. Traczyka ,b INFN Sezione di Triestea , Università di Triesteb, Trieste, Italy S. Belfortea , M. Casarsaa , F. Cossuttia , G. Della Riccaa ,b, A. Zanettia Kyungpook National University, Daegu, Korea D. H. Kim, G. N. Kim, M. S. Kim, J. Lee, S. Lee, S. W. Lee, Y. D. Oh, S. Sekmen, D. C. Son, Y. C. Yang Chonbuk National University, Jeonju, Korea A. Lee Institute for Universe and Elementary Particles, Chonnam National University, Kwangju, Korea H. Kim, D. H. Moon Hanyang University, Seoul, Korea J. A. Brochero Cifuentes, J. Goh, T. J. Kim Korea University, Seoul, 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 Seoul National University, Seoul, Korea J. Almond, J. Kim, H. Lee, S. B. Oh, B. C. Radburn-Smith, S. h. Seo, U. K. Yang, H. D. Yoo, G. B. Yu University of Seoul, Seoul, Korea M. Choi, H. Kim, J.H. Kim, J. S. H. Lee, I. C. Park, G. Ryu Sungkyunkwan University, Suwon, Korea Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia I. Ahmed, Z. A. Ibrahim, M. A. B. Md Ali30, F. Mohamad Idris31, W. A. T. Wan Abdullah, M. N. Yusli, Z. Zolkapli 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, A. Saddique, M. A. Shah, M. Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K. Nawrocki, K. Romanowska-Rybinska, M. Szleper, P. Zalewski Laboratório de Instrumentação e Física Experimental de Partículas, Lisbon, Portugal P. Bargassa, C. Beirão Da Cruz E Silva, B. Calpas, A. Di Francesco, P. Faccioli, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M. V. Nemallapudi, J. Seixas, O. Toldaiev, D. Vadruccio, J. Varela Joint Institute for Nuclear Research, Dubna, Russia S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev, V. Karjavin, A. Lanev, A. Malakhov, V. Matveev34,35, V. Palichik, V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin, A. Zarubin Moscow Institute of Physics and Technology, Moscow, Russia T. Aushev, A. Bylinkin35 National Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI), Moscow, Russia R. Chistov38, M. Danilov38, S. Polikarpov P.N. Lebedev Physical Institute, Moscow, Russia V. Andreev, M. Azarkin35, I. Dremin35, M. Kirakosyan, 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 Novosibirsk State University (NSU), Novosibirsk, Russia V. Blinov40, Y. Skovpen40, D. Shtol40 State Research Center of Russian Federation, Institute for High Energy Physics, Protvino, Russia I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, V. Kachanov, A. Kalinin, D. Konstantinov, V. Krychkine, V. Petrov, R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov Faculty of Physics and Vinca Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia P. Adzic41, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic, V. Rekovic Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain J. Alcaraz Maestre, M. Barrio Luna, M. Cerrada, 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, B. Chazin Quero, 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, P. Baillon, A. H. Ball, D. Barney, M. Bianco, P. Bloch, A. Bocci, C. Botta, T. Camporesi, R. Castello, M. Cepeda, G. Cerminara, Y. Chen, D. d’Enterria, A. Dabrowski, V. Daponte, A. David, M. De Gruttola, A. De Roeck, E. Di Marco42, M. Dobson, B. Dorney, T. du Pree, M. Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, G. Franzoni, J. Fulcher, W. Funk, D. Gigi, K. Gill, F. Glege, D. Gulhan, S. Gundacker, M. Guthoff, P. Harris, J. Hegeman, V. Innocente, P. Janot, J. Kieseler, H. Kirschenmann, V. Knünz, A. Kornmayer14, M. J. Kortelainen, C. Lange, P. Lecoq, C. Lourenço, M. T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F. Meijers, J. A. Merlin, S. Mersi, E. Meschi, P. Milenovic43, F. Moortgat, M. Mulders, H. Neugebauer, S. Orfanelli, L. Orsini, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, A. Racz, T. Reis, G. Rolandi44, M. Rovere, H. Sakulin, J. B. Sauvan, C. Schäfer, C. Schwick, M. Seidel, A. Sharma, P. Silva, P. Sphicas45, J. Steggemann, M. Stoye, M. Tosi, D. Treille, A. Triossi, A. Tsirou, V. Veckalns46, G. I. Veres19, M. Verweij, N. Wardle, A. Zagozdzinska33, W. D. Zeuner Institute for Particle Physics ETH Zurich, Zurich, Switzerland F. Bachmair, L. Bäni, L. Bianchini, B. Casal, G. Dissertori, M. Dittmar, M. Donegà, C. Grab, C. Heidegger, D. Hits, J. Hoss, G. Kasieczka, W. Lustermann, B. Mangano, M. Marionneau, P. Martinez Ruiz del Arbol, M. Masciovecchio, M. T. Meinhard, D. Meister, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pandolfi, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, M. Quittnat, M. Rossini, M. Schönenberger, A. Starodumov47, V. R. Tavolaro, K. Theofilatos, R. Wallny National Central University, Chung-Li, Taiwan V. Candelise, T. H. Doan, Sh. Jain, R. Khurana, M. Konyushikhin, C. M. Kuo, W. Lin, A. Pozdnyakov, S. S. Yu National Taiwan University (NTU), Taipei, Taiwan Arun Kumar, P. Chang, Y. H. Chang, Y. Chao, K. F. Chen, P. H. Chen, F. Fiori, W.-S. Hou, Y. Hsiung, Y. F. Liu, R.-S. Lu, M. Miñano Moya, E. Paganis, A. Psallidas, J. F. Tsai Department of Physics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Physics Department, Science and Art Faculty, Cukurova University, Adana, Turkey A. Adiguzel, M. N. Bakirci49, F. Boran, S. Cerci50, S. Damarseckin, Z. S. Demiroglu, C. Dozen, I. Dumanoglu, S. Girgis, Physics Department, Middle East Technical University, Ankara, Turkey B. Bilin, G. Karapinar55, K. Ocalan56, M. Yalvac, M. Zeyrek Bogazici University, Istanbul, Turkey E. Gülmez, M. Kaya57, O. Kaya58, E. A. Yetkin59 Istanbul Technical University, Istanbul, Turkey A. Cakir, K. Cankocak 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. Belyaev61, 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, 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 Catholic University of America, Washington, USA R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA A. Buccilli, S. I. Cooper, C. Henderson, P. Rumerio, C. West Boston University, Boston, USA D. Arcaro, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J. Rohlf, L. Sulak, D. Zou University of California Riverside, Riverside, USA E. Bouvier, K. Burt, R. Clare, J. Ellison, J. W. Gary, S. M. A. Ghiasi Shirazi, G. Hanson, J. Heilman, P. Jandir, E. Kennedy, F. Lacroix, O. R. Long, M. Olmedo Negrete, M. I. Paneva, A. Shrinivas, W. Si, H. Wei, S. Wimpenny, B. R. Yates Department of Physics, University of California Santa Barbara, Santa Barbara, USA N. Amin, R. Bhandari, J. Bradmiller-Feld, C. Campagnari, A. Dishaw, V. Dutta, M. Franco Sevilla, C. George, F. Golf, L. Gouskos, J. Gran, R. Heller, J. Incandela, S. D. Mullin, A. Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez, J. Yoo Carnegie Mellon University, Pittsburgh, USA M. B. Andrews, T. Ferguson, M. Paulini, J. Russ, M. Sun, H. Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA J. P. Cumalat, W. T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, T. Mulholland, K. Stenson, S. R. Wagner Cornell University, Ithaca, USA J. Alexander, J. Chaves, J. Chu, S. Dittmer, K. Mcdermott, N. Mirman, J. R. Patterson, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S. M. Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fairfield University, Fairfield, USA D. Winn Fermi National Accelerator Laboratory, Batavia, USA S. Abdullin, M. Albrow, G. Apollinari, A. Apresyan, S. Banerjee, L. A. T. Bauerdick, A. Beretvas, J. Berryhill, P. C. Bhat, G. Bolla, K. Burkett, J. N. Butler, A. Canepa, H. W. K. Cheung, F. Chlebana, M. Cremonesi, J. Duarte, V. D. Elvira, I. Fisk, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D. Green, S. Grünendahl, O. Gutsche, R. M. Harris, S. Hasegawa, J. Hirschauer, Z. Hu, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima, B. Kreis, S. Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J. Lykken, K. Maeshima, N. Magini, J. M. Marraffino, S. Maruyama, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L. Ristori, B. Schneider, E. Sexton-Kennedy, A. Soha, W. J. Spalding, L. Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N. V. Tran, L. Uplegger, E. W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H. A. Weber, A. Whitbeck 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. Colafranceschi, M. Hohlmann, D. Noonan, T. Roy, F. Yumiceva Johns Hopkins University, Baltimore, USA B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A. V. Gritsan, P. Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The University of Kansas, Lawrence, USA A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, J. Castle, S. Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Royon, S. Sanders, R. Stringer, J. D. Tapia Takaki, Q. Wang Kansas State University, Manhattan, USA A. Ivanov, K. Kaadze, Y. Maravin, A. Mohammadi, L. K. Saini, N. Skhirtladze, S. Toda Lawrence Livermore National Laboratory, Livermore, USA 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, N. J. Hadley, S. Jabeen, G. Y. Jeng, R. G. Kellogg, J. Kunkle, A. C. Mignerey, F. Ricci-Tam, Y. H. Shin, A. Skuja, S. C. Tonwar University of Minnesota, Minneapolis, USA A. C. Benvenuti, R. M. Chatterjee, A. Evans, P. Hansen, S. Kalafut, S. C. Kao, Y. Kubota, Z. Lesko, J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack, N. Tambe, J. Turkewitz University of Mississippi, Oxford, USA J. G. Acosta, S. Oliveros State University of New York at Buffalo, Buffalo, USA M. Alyari, J. Dolen, A. Godshalk, C. Harrington, I. Iashvili, A. Kharchilava, A. Parker, S. Rappoccio, B. Roozbahani University of Notre Dame, Notre Dame, USA N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D. J. Karmgard, N. Kellams, K. Lannon, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y. Musienko34, M. Planer, A. Reinsvold, R. Ruchti, N. Rupprecht, G. Smith, S. Taroni, M. Wayne, M. Wolf, A. Woodard The Ohio State University, Columbus, USA J. Alimena, L. Antonelli, B. Bylsma, L. S. Durkin, S. Flowers, B. Francis, A. Hart, C. Hill, W. Ji, B. Liu, W. Luo, D. Puigh, B. L. Winer, H. W. Wulsin Princeton University, Princeton, USA A. Benaglia, S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, D. Lange, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, D. Stickland, A. Svyatkovskiy, C. Tully Purdue University, West Lafayette, USA A. Barker, V. E. Barnes, S. Folgueras, L. Gutay, M. K. Jha, M. Jones, A. W. Jung, A. Khatiwada, D. H. Miller, N. Neumeister, J. F. Schulte, J. Sun, F. Wang, W. Xie Purdue University Northwest, Hammond, USA T. Cheng, N. Parashar, J. Stupak Rice University, Houston, USA A. Adair, B. Akgun, Z. Chen, K. M. Ecklund, F. J. M. Geurts, M. Guilbaud, W. Li, B. Michlin, M. Northup, B. P. Padley, J. Roberts, J. Rorie, Z. Tu, J. Zabel The Rockefeller University, New York, USA R. Ciesielski, K. Goulianos, C. Mesropian Rutgers, The State University of New Jersey, Piscataway, USA A. Agapitos, J. P. Chou, Y. Gershtein, T. A. Gómez Espinosa, E. Halkiadakis, M. Heindl, E. Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath, R. Montalvo, K. Nash, M. Osherson, 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 Texas Tech University, Lubbock, USA N. Akchurin, J. Damgov, F. De Guio, C. Dragoiu, P. R. Dudero, J. Faulkner, E. Gurpinar, S. Kunori, K. Lamichhane, S. W. Lee, T. Libeiro, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang Wayne State University, Detroit, USA C. Clarke, R. Harr, P. E. Karchin, J. Sturdy, S. Zaleski 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 Universidade Estadual de Campinas, Campinas, Brazil 4: Also at Universidade Federal de Pelotas, Pelotas, Brazil 5: Also at Université Libre de Bruxelles, Bruxelles, Belgium 6: Also at Universidad de Antioquia, Medellin, Colombia 7: Also at Joint Institute for Nuclear Research, Dubna, Russia 8: Also at Suez University, Suez, Egypt 9: Now at British University in Egypt, Cairo, Egypt 10: Also at Fayoum University, El-Fayoum, Egypt 11: Now at Helwan University, Cairo, Egypt 12: Also at Université de Haute Alsace, Mulhouse, France 13: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia 14: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland 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 Institute of Physics, University of Debrecen, Debrecen, Hungary 21: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, 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 Yazd University, Yazd, 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 Budker Institute of Nuclear Physics, Novosibirsk, Russia 41: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia 42: Also at INFN Sezione di Roma; Sapienza Università di Roma, Rome, Italy 43: Also at University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia 44: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy 45: Also at National and Kapodistrian University of Athens, Athens, Greece 46: Also at Riga Technical University, Riga, Latvia 47: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia 48: Also at Albert Einstein Center for Fundamental Physics, Bern, Switzerland 49: Also at Gaziosmanpasa University, Faculty of Science, Tokat, Turkey 50: Also at Adiyaman University, Adiyaman, Turkey 51: Also at Istanbul Aydin University, Istanbul, Turkey 52: Also at Mersin University, Mersin, Turkey 53: Also at Cag University, Mersin, Turkey 54: Also at Piri Reis University, Istanbul, Turkey 55: Also at Izmir Institute of Technology, Izmir, Turkey 56: Also at Necmettin Erbakan University, Konya, Turkey 57: Also at Marmara University, Istanbul, Turkey 58: Also at Kafkas University, Kars, Turkey 59: Also at Istanbul Bilgi University, Istanbul, Turkey 60: Also at Rutherford Appleton Laboratory, Didcot, UK 61: Also at School of Physics and Astronomy, University of Southampton, Southampton, UK 62: Also at Instituto de Astrofísica de Canarias, La Laguna, Spain 63: Also at Utah Valley University, Orem, USA 64: Also at BEYKENT UNIVERSITY, Istanbul, Turkey 65: Also at Bingol University, Bingol, Turkey 66: Also at Erzincan University, Erzincan, Turkey 67: Also at Sinop University, Sinop, Turkey 68: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey 69: Also at Texas A&M University at Qatar, Doha, Qatar 70: Also at Kyungpook National University, Daegu, Korea 1. G. Bertone , D. Hooper , J. Silk , Particle dark matter: evidence, candidates and constraints . Phys. Rept . 405 , 279 ( 2005 ). https://doi. org/10.1016/j.physrep. 2004 . 08 .031. arXiv: hep-ph/0404175 2. J.L. Feng , Dark matter candidates from particle physics and methods of detection . Ann. Rev. Astron. Astrophys . 48 , 495 ( 2010 ). https://doi.org/10.1146/annurev-astro- 082708 - 101659 . arXiv: 1003 . 0904 3. T.A. Porter , R.P. Johnson , P.W. Graham, Dark matter searches with astroparticle data . Ann. Rev. Astron. Astrophys . 49 , 155 ( 2011 ). https://doi.org/10.1146/annurev-astro- 081710 - 102528 . arXiv: 1104 . 2836 4. G. D'Ambrosio , G.F. Giudice , G. Isidori , A. Strumia , Minimal flavor violation: an effective field theory approach . Nucl. Phys . 645 , 155 ( 2002 ). https://doi.org/10.1016/S0550- 3213 ( 02 ) 00836 - 2 . arXiv: hep-ph/0207036 5. G. Isidori , D.M. Straub , Minimal flavour violation and beyond . Eur. Phys. J. C 72 , 2103 ( 2012 ). https://doi.org/10.1140/epjc/ s10052-012-2103-1. arXiv: 1202 . 0464 6. U. Haisch , F. Kahlhoefer , J. Unwin, The impact of heavy-quark loops on LHC dark matter searches . JHEP 07 , 125 ( 2013 ). https:// doi.org/10.1007/JHEP07( 2013 ) 125 . arXiv: 1208 . 4605 7. T. Lin , E.W. Kolb , L.-T. Wang, Probing dark matter couplings to top and bottom quarks at the LHC . Phys. Rev. D 88 , 063510 ( 2013 ). https://doi.org/10.1103/PhysRevD.88.063510. arXiv: 1303 . 6638 8. M.R. Buckley , D. Feld , D. Gonçalves , Scalar simplified models for dark matter . Phys. Rev. D 91 , 015017 ( 2015 ). https://doi.org/ 10.1103/PhysRevD.91.015017. arXiv: 1410 . 6497 9. U. Haisch , E. Re, Simplified dark matter top-quark interactions at the LHC . JHEP 06 , 078 ( 2015 ). https://doi.org/10.1007/ JHEP06( 2015 ) 078 . arXiv: 1503 . 00691 10. C. Arina et al., A comprehensive approach to dark matter studies: exploration of simplified top-philic models . JHEP 11 , 111 ( 2016 ). https://doi.org/10.1007/JHEP11( 2016 ) 111 . arXiv: 1605 . 09242 11. ATLAS Collaboration, Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √s = 8 TeV with the ATLAS detector . Eur. Phys. J. C 75 , 299 ( 2015 ). https://doi.org/10.1140/epjc/s10052-015-3517-3. arXiv: 1502 .01518 [Erratum: 10 .1140/epjc/s10052-015-3639-7] 12. CMS Collaboration, Search for dark matter in proton-proton collisions at 8 TeV with missing transverse momentum and vector boson tagged jets . JHEP 12 , 083 ( 2016 ). https://doi.org/10.1007/ JHEP12( 2016 ) 083 . arXiv: 1607 . 05764 13. ATLAS Collaboration, Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √s = 13 TeV using the ATLAS detector . Phys. Rev. D 94 , 032005 ( 2016 ). https://doi.org/10.1103/PhysRevD.94.032005. arXiv: 1604 . 07773 14. CMS Collaboration, Search for dark matter produced with an energetic jet or a hadronically decaying W or Z boson at √s = 13 TeV . JHEP 07 , 04 ( 2017 ). https://doi.org/10.1007/JHEP07( 2017 ) 014 . arXiv: 1703 . 01651 15. G.C. Branco et al., Theory and phenomenology of two-Higgsdoublet models . Phys. Rept . 516 , 001 ( 2012 ). https://doi.org/10. 1016/j.physrep. 2012 . 02 .002. arXiv: 1106 . 0034 16. M. Beltran et al., Maverick dark matter at colliders . JHEP 09 , 037 ( 2010 ). https://doi.org/10.1007/JHEP09( 2010 ) 037 . arXiv: 1002 . 4137 17. J. Goodman et al., Constraints on dark matter from colliders . Phys. Rev. D 82 , 116010 ( 2010 ). https://doi.org/10.1103/PhysRevD.82. 116010. arXiv: 1008 . 1783 18. K. Cheung et al., The top window for dark matter . JHEP 10 , 081 ( 2010 ). https://doi.org/10.1007/JHEP10( 2010 ) 081 . arXiv: 1009 . 0618 19. CMS Collaboration, Search for the production of dark matter in association with top-quark pairs in the single-lepton final state in proton-proton collisions at √s = 8 TeV . JHEP 06 , 121 ( 2015 ). https://doi.org/10.1007/JHEP06( 2015 ) 121 . arXiv: 1504 . 03198 20. ATLAS Collaboration, Search for dark matter in events with heavy quarks and missing transverse momentum in pp collisions with the ATLAS detector . Eur. Phys. J. C 75 , 92 ( 2015 ). https://doi.org/10. 1140/epjc/s10052-015-3306-z. arXiv: 1410 . 4031 21. D. Abercrombie et al., Dark matter benchmark models for early LHC run-2 searches: report of the ATLAS/CMS Dark Matter Forum ( 2015 ). arXiv: 1507 . 00966 22. CMS Collaboration, The CMS experiment at the CERN LHC . JINST 3, S08004 ( 2008 ). https://doi.org/10.1088/ 1748 -0221/3/08/ S08004 23. CMS Collaboration, Particle-Flow Event Reconstruction in CMS and Performance for Jets, Taus, and MET . CMS Physics Analysis Summary CMS-PAS-PFT-09-001 ( 2009 ). https://cds.cern.ch/ record/1194487 24. CMS Collaboration, Commissioning of the Particle-flow Event Reconstruction with the first LHC collisions recorded in the CMS detector” , CMS Physics Analysis Summary CMS-PAS-PFT-10- 001 ( 2010 ). https://cds.cern.ch/record/1247373 25. CMS Collaboration, Description and performance of track and primary-vertex reconstruction with the CMS tracker . JINST 9 , 10009 ( 2014 ). https://doi.org/10.1088/ 1748 -0221/9/10/P10009. arXiv: 1405 . 6569 26. M. Cacciari , G.P. Salam , G. Soyez, The anti-kt jet clustering algorithm . JHEP 04 , 063 ( 2008 ). https://doi.org/10.1088/ 1126 - 6708 / 2008 /04/063. arXiv: 0802 . 1189 27. M. Cacciari , G.P. Salam , G. Soyez, FastJet user manual . Eur. Phys. J. C 72 , 1896 ( 2012 ). https://doi.org/10.1140/epjc/ s10052-012 -1896-2 . arXiv: 1111 . 6097 28. M. Cacciari , G.P. Salam , G. Soyez, The catchment area of jets . JHEP 04 , 005 ( 2008 ). https://doi.org/10.1088/ 1126 - 6708 / 2008 / 04/005. arXiv: 0802 . 1188 29. CMS Collaboration, Determination of jet energy calibration and transverse momentum resolution in CMS . JINST 6, P11002 ( 2011 ). https://doi.org/10.1088/ 1748 -0221/6/11/P11002 30. CMS Collaboration, Identification of b-quark jets with the CMS experiment . J. Instrum. 8 , P04013 ( 2013 ). https://doi.org/10.1088/ 1748 -0221/8/04/P04013 31. CMS Collaboration, Identification of b quark jets at the CMS experiment in the LHC run 2 . CMS Physics Analysis Summary CMSPAS-BTV-15-001 ( 2016 ). https://cds.cern.ch/record/2138504 32. CMS Collaboration, Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at √s = 8 TeV . JINST 10 , 6005 ( 2015 ). https://doi.org/10.1088/ 1748 -0221/10/06/P06005. arXiv: 1502 . 02701 33. CMS Collaboration, Performance of CMS muon reconstruction in pp collision events at √s = 7 TeV . JINST 7, 10002 ( 2012 ). https:// doi.org/10.1088/ 1748 -0221/7/10/P10002. arXiv: 1206 . 4071 34. CMS Collaboration, Measurement of the inclusive W and Z production cross sections in pp collisions at √s = 7 TeV . JHEP 10 , 132 ( 2011 ). https://doi.org/10.1007/JHEP10( 2011 ) 132 . arXiv: 1107 . 4789 35. CMS Collaboration , V tagging observables and correlations . CMS Physics Analysis Summary CMS-PAS-JME-14-002 ( 2014 ). https://cdsweb.cern.ch/record/1754913 36. CMS Collaboration, Fitting of event topologies with external kinematic constraints in CMS , CMS Physics Analysis Note CMSNOTE-2006-023 , CMS-NOTE-2006- 023 ( 2006 ). https://cds.cern. ch/record/926540 37. H. Voss , A. Höcker , J. Stelzer , F. Tegenfeldt, TMVA, the toolkit for multivariate data analysis with ROOT” , in XIth International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT) ( 2007 ), p. 40 . arXiv:physics/0703039 38. CMS Collaboration, Performance of tau-lepton reconstruction and identification in CMS . JINST 7, P01001 ( 2012 ). https://doi.org/10. 1088/ 1748 -0221/7/01/P01001. arXiv: 1109 . 6034 39. P. Nason , A New method for combining NLO QCD with shower Monte Carlo algorithms . JHEP 11 , 040 ( 2004 ). https://doi.org/10. 1088/ 1126 - 6708 / 2004 /11/040. arXiv: hep-ph/0409146 40. S. Frixione, P. Nason , C. Oleari , Matching NLO QCD computations with Parton Shower simulations: the POWHEG method . JHEP 11 , 070 ( 2007 ). https://doi.org/10.1088/ 1126 - 6708 / 2007 /11/070. arXiv: 0709 .2092 41. S. Alioli, P. Nason , C. Oleari , E. Re, A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX . JHEP 06 , 043 ( 2010 ). https://doi.org/10.1007/ JHEP06( 2010 ) 043 . arXiv: 1002 . 2581 42. T. Sjöstrand , S. Mrenna , P.Z. Skands , A brief introduction to PYTHIA 8.1 . Comput . Phys. Commun . 178 , 852 ( 2008 ). https:// doi.org/10.1016/j.cpc. 2008 . 01 .036. arXiv: 0710 . 3820 43. CMS Collaboration, Event generator tunes obtained from underlying event and multiparton scattering measurements . Eur. Phys. J. C 76 , 155 ( 2016 ). https://doi.org/10.1140/epjc/s10052-016-3988-x. arXiv: 1512 . 00815 44. J. Alwall et al., The automated computation of tree-level and nextto-leading order differential cross sections, and their matching to parton shower simulations . JHEP 07 , 079 ( 2014 ). https://doi.org/ 10.1007/JHEP07( 2014 ) 079 . arXiv: 1405 . 0301 45. M.L. Mangano , M. Moretti , F. Piccinini , M. Treccani , Matching matrix elements and shower evolution for top-quark production in hadronic collisions . JHEP 01 , 013 ( 2007 ). https://doi.org/10.1088/ 1126 - 6708 / 2007 /01/013. arXiv: hep-ph/0611129 46. R. Frederix , S. Frixione , Merging meets matching in MC@NLO . JHEP 12 , 061 ( 2012 ). https://doi.org/10.1007/JHEP12( 2012 ) 061 . arXiv: 1209 . 6215 47. G. Busoni et al., Recommendations on presenting LHC searches for missing transverse energy signals using simplified s-channel models of dark matter ( 2016 ). arXiv: 1603 . 04156 48. M. Bauer et al., Towards the next generation of simplified dark matter models ( 2016 ). arXiv: 1607 . 06680 49. F. Boudjema , D. Guadagnoli , R.M. Godbole , K.A. Mohan , Laboratory-frame observables for probing the top-higgs boson interaction . Phys. Rev. D 92 , 015019 ( 2015 ). https://doi.org/10. 1103/PhysRevD.92.015019. arXiv: 1501 . 03157 50. M. Backovic ´ et al., Higher-order qcd predictions for dark matter production at the lhc in simplified models with s-channel mediators . Eur. Phys. J. C 75 , 482 ( 2015 ). https://doi.org/10.1140/epjc/ s10052-015-3700-6. arXiv: 1508 . 05327 51. P. Artoisenet , R. Frederix , O. Mattelaer , R. Rietkerk , Automatic spin-entangled decays of heavy resonances in Monte Carlo simulations . JHEP 03 , 015 ( 2013 ). https://doi.org/10.1007/ JHEP03( 2013 ) 015 . arXiv: 1212 . 3460 52. P. Harris , V.V. Khoze , M. Spannowsky , C. Williams , Constraining dark sectors at colliders: Beyond the effective theory approach . Phys. Rev. D 91 , 055009 ( 2015 ). https://doi.org/10.1103/ PhysRevD.91.055009. arXiv: 1411 . 0535 53. F. Maltoni , G. Ridolfi, M. Ubiali, b-initiated processes at the LHC: a reappraisal . JHEP 07 , 022 ( 2012 ). https://doi.org/10.1007/ JHEP07( 2012 ) 022 . arXiv: 1203 .6393 [Erratum: http://dx.doi.org/ 10.1007/JHEP04( 2013 )095] 54. NNPDF Collaboration, Parton distributions for the LHC Run II . JHEP 04 , 040 ( 2015 ). https://doi.org/10.1007/JHEP04( 2015 ) 040 . arXiv: 1410 . 8849 55. GEANT4 Collaboration, GEANT4-a simulation toolkit . Nucl. Instrum. Meth. A 506 , 250 ( 2003 ). https://doi.org/10.1016/ S0168- 9002 ( 03 ) 01368 - 8 56. Particle Data Group, Review of particle physics. Chin. Phys. C 40 , 100001 ( 2016 ). https://doi.org/10.1088/ 1674 -1137/40/10/100001 57. Y. Bai , H.-C. Cheng, J. Gallicchio, J. Gu , Stop the top background of the stop search . JHEP 07 , 110 ( 2012 ). https://doi.org/10.1007/ JHEP07( 2012 ) 110 . arXiv: 1203 . 4813 58. CMS Collaboration, Search for the standard model Higgs boson decaying to W +W − in the fully leptonic final state in pp collisions at √s = 7 TeV . Phys. Lett. B 710 , 91 ( 2012 ). https://doi.org/10. 1016/j.physletb. 2012 . 02 .076. arXiv: 1202 . 1489 59. L. Moneta et al., The RooStats project , in 13th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT2010) (SISSA , 2010 ). arXiv: 1009 .1003 [PoS( ACAT2010 )057] 60. J.S. Conway , Incorporating nuisance parameters in likelihoods for multisource spectra . in Proceedings, PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding ( 2011 ), p. 115 . https://doi.org/10.5170/ CERN-2011- 006 .115. arXiv: 1103 . 0354 61. CMS Collaboration, CMS Luminosity Measurement for the 2015 Data Taking Period . CMS Physics Analysis Summary CMS-PASLUM-15-001 , CMS-PAS-LUM- 15- 001 ( 2016 ). https://cds.cern. ch/record/2138682 62. CMS Collaboration, Differential cross section measurements for the production of a W boson in association with jets in protonproton collisions at √s = 7TeV . Phys. Lett. B 741 , 12 ( 2014 ). https://doi.org/10.1016/j.physletb. 2014 . 12 .003 63. CMS Collaboration, Measurement of the production cross section for a W boson and two b jets in pp collisions at √s = 7TeV . Phys. Lett. B 735 , 204 ( 2014 ). https://doi.org/10.1016/j.physletb. 2014 . 06 .041 64. CMS Collaboration, Measurements of jet multiplicity and differential production cross sections of Z +jets events in proton-proton collisions at √s = 7TeV . Phys. Rev. D 91 , 052008 ( 2015 ). https:// doi.org/10.1103/PhysRevD.91.052008 65. CMS Collaboration, Measurement of the production cross sections for a Z boson and one or more b jets in pp collisions at √s = 7 TeV . JHEP 06 , 120 ( 2014 ). https://doi.org/10.1007/JHEP06( 2014 ) 120 . arXiv: 1402 . 1521 66. M. Bähr et al., Herwig++ physics and manual. Eur. Phys. J. C 58 , 639 ( 2008 ). https://doi.org/10.1140/epjc/s10052-008-0798-9. arXiv: 0803 . 0883 67. J. Butterworth et al., PDF4LHC recommendations for LHC Run II . J. Phys. G 43 , 023001 ( 2016 ). https://doi.org/10.1088/ 0954 -3899/ 43/2/023001. arXiv: 1510 . 03865 68. CMS Collaboration, Measurement of differential top-quark pair production cross sections in pp colisions at √s = 7 TeV . Eur. Phys. J. C 73 , 2339 ( 2013 ). https://doi.org/10.1140/epjc/ s10052-013-2339-4. arXiv: 1211 . 2220 69. R. Barlow , C. Beeston , Fitting using finite Monte Carlo samples . Comput. Phys. Commun . 77 , 219 ( 1993 ). https://doi.org/10.1016/ 0010 - 4655 ( 93 ) 90005 -W 70. T. Lin , H.-B. Yu , K.M. Zurek , On symmetric and asymmetric light dark matter . Phys. Rev. D 85 , 063503 ( 2012 ). https://doi.org/10. 1103/PhysRevD.85.063503. arXiv: 1111 . 0293 71. D. Bauer et al., Dark matter in the coming decade: Complementary paths to discovery and beyond . Phys. Dark Univ. 7-8 , 16 ( 2015 ). https://doi.org/10.1016/j.dark. 2015 . 04 .001. arXiv: 1305 . 1605 72. S. Baker , R.D. Cousins , Clarification of the use of chi-square and likelihood functions in fits to histograms . Nucl. Instrum. Methods 221 , 437 ( 1984 ). https://doi.org/10.1016/ 0167 - 5087 ( 84 ) 90016 - 4 73. J.K. Lindsey , Parametric Statistical Inference (Oxford University Press, New York, 1996 ). ISBN 9780198523598 74. T. Junk, Confidence level computation for combining searches with small statistics . Nucl. Instrum. Methods A 434 , 435 ( 1999 ). https:// doi.org/10.1016/S0168- 9002 ( 99 ) 00498 - 2 75. A.L. Read , Presentation of search results: the C Ls technique . J. Phys. G 28 , 2693 ( 2002 ). https://doi.org/10.1088/ 0954 -3899/28/ 10/313 76. G. Cowan, K. Cranmer , E. Gross , O. Vitells , Asymptotic formulae for likelihood-based tests of new physics . Eur. Phys. J. C 71 , 1554 ( 2011 ). https://doi.org/10.1140/epjc/s10052-011-1554-0. arXiv: 1007 .1727 [Erratum: 10 .1140/epjc/s10052-013-2501-z] Laboratoire Leprince-Ringuet , Ecole polytechnique, CNRS/IN2P3 , Université Paris-Saclay, Palaiseau, France A. Abdulsalam , I. Antropov, S. Baffioni , F. Beaudette , P. Busson , L. Cadamuro , E. Chapon , C. Charlot , O. Davignon , R. Granier de Cassagnac, M. Jo , S. Lisniak , A. Lobanov , P. Miné , M. Nguyen , C. Ochando , G. Ortona, P. Paganini , P. Pigard , S. Regnard , R. Salerno , Y. Sirois , A. G. Stahl Leiton , T. Strebler , Y. Yilmaz , A. Zabi , A . Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178 , 67000 Strasbourg, France J.-L. Agram12 , J. Andrea , D. Bloch , J.-M. Brom , M. Buttignol , E. C. Chabert , N. Chanon , C. Collard , E. Conte12, X. Coubez , J.-C. Fontaine12 , D. Gelé , U. Goerlach , A. -C. Le Bihan , P. Van Hove S. Chenarani25 , E. Eskandari Tadavani , S. M. Etesami25 , M. Khakzad , M. Mohammadi Najafabadi , M. Naseri , S. Paktinat Mehdiabadi26 , F. Rezaei Hosseinabadi , B. Safarzadeh27 , M. Zeinali G. Gokbulut , Y. Guler , I. Hos51 , E. E. Kangal52 , O. Kara , A. Kayis Topaksu , U. Kiminsu , M. Oglakci , G. Onengut53, K. Ozdemir54 , B. Tali50 , S. Turkcapar , I. S. Zorbakir , C. Zorbilmez


This is a preview of a remote PDF: https://link.springer.com/content/pdf/10.1140%2Fepjc%2Fs10052-017-5317-4.pdf

A. M. Sirunyan, A. Tumasyan, W. Adam, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M. Dragicevic, J. Erö, M. Flechl, M. Friedl, R. Frühwirth, V. M. Ghete, C. Hartl, N. Hörmann, J. Hrubec, M. Jeitler, A. König, I. Krätschmer, D. Liko, T. Matsushita, I. Mikulec, D. Rabady, N. Rad, B. Rahbaran, H. Rohringer, J. Schieck, J. Strauss, W. Waltenberger, C.-E. Wulz, V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez, N. Shumeiko, S. Alderweireldt, E. A. De Wolf, X. Janssen, J. Lauwers, M. Van De Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel, A. Van Spilbeeck, S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover, S. Lowette, S. Moortgat, L. Moreels, A. Olbrechts, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, G. Karapostoli, T. Lenzi, J. Luetic, T. Maerschalk, A. Marinov, A. Randle-conde, T. Seva, C. Vander Velde, P. Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni, F. Zhang, A. Cimmino, T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov, D. Poyraz, S. Salva, R. Schöfbeck, M. Tytgat, W. Van Driessche, W. Verbeke, N. Zaganidis, H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, A. Caudron, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, A. Jafari, M. Komm, G. Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K. Piotrzkowski, L. Quertenmont, M. Vidal Marono, S. Wertz, N. Beliy, W. L. Aldá Júnior, F. L. Alves, G. A. Alves, L. Brito, C. Hensel, A. Moraes, M. E. Pol, P. Rebello Teles, E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato, A. Custódio, E. M. Da Costa, G. G. Da Silveira, D. De Jesus Damiao, S. Fonseca De Souza, L. M. Huertas Guativa, H. Malbouisson, C. Mora Herrera, L. Mundim, H. Nogima, A. Santoro, A. Sznajder, E. J. Tonelli Manganote, F. Torres Da Silva De Araujo, A. Vilela Pereira, S. Ahuja, C. A. Bernardes, T. R. Fernandez Perez Tomei, E. M. Gregores, P. G. Mercadante, C. S. Moon, S. F. Novaes, Sandra S. Padula, D. Romero Abad, J. C. Ruiz Vargas, A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Rodozov, S. Stoykova, G. Sultanov, M. Vutova, A. Dimitrov, I. Glushkov, L. Litov, B. Pavlov, P. Petkov, W. Fang, X. Gao, M. Ahmad, J. G. Bian, G. M. Chen, H. S. Chen, M. Chen, Y. Chen, C. H. Jiang, D. Leggat, Z. Liu, F. Romeo, S. M. Shaheen, A. Spiezia, J. Tao, C. Wang, Z. Wang, E. Yazgan, H. Zhang, J. Zhao, Y. Ban, G. Chen, Q. Li, S. Liu, Y. Mao, S. J. Qian, D. Wang, Z. Xu, C. Avila, A. Cabrera, L. F. Chaparro Sierra, C. Florez, J. P. Gomez, C. F. González Hernández, J. D. Ruiz Alvarez, N. Godinovic, D. Lelas, I. Puljak, P. M. Ribeiro Cipriano, T. Sculac, Z. Antunovic, M. Kovac, V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, T. Susa, M. W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P. A. Razis, H. Rykaczewski, M. Finger, M. Finger Jr., E. Carrera Jarrin, Y. Assran, M. A. Mahmoud, A. Mahrous, R. K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, A. Tiko, C. Veelken, P. Eerola, J. Pekkanen, M. Voutilainen, J. Härkönen, T. Järvinen, V. Karimäki, R. Kinnunen, T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, P. Luukka, E. Tuominen, J. Tuominiemi, E. Tuovinen, J. Talvitie, T. Tuuva, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J. L. Faure, F. Ferri, S. Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, I. Kucher, E. Locci, M. Machet, J. Malcles, J. Rander, A. Rosowsky, M. Ö. Sahin, M. Titov, A. Abdulsalam, I. Antropov, S. Baffioni, F. Beaudette, P. Busson, L. Cadamuro, E. Chapon, C. Charlot, O. Davignon, R. Granier de Cassagnac, M. Jo, S. Lisniak, A. Lobanov, P. Miné, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, P. Pigard, S. Regnard, R. Salerno, Y. Sirois, A. G. Stahl Leiton, T. Strebler, Y. Yilmaz, A. Zabi, A. Zghiche, J.-L. Agram, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E. C. Chabert, N. Chanon, C. Collard, E. Conte, X. Coubez, J.-C. Fontaine, D. Gelé, U. Goerlach, A.-C. Le Bihan, P. Van Hove, S. Gadrat, S. Beauceron, C. Bernet, G. Boudoul, R. Chierici, D. Contardo, B. Courbon, P. Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille, F. Lagarde, I. B. Laktineh, M. Lethuillier, L. Mirabito, A. L. Pequegnot, S. Perries, A. Popov, V. Sordini, M. Vander Donckt, S. Viret, A. Khvedelidze, Z. Tsamalaidze, C. Autermann, S. Beranek, L. Feld, M. K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C. Schomakers, J. Schulz, T. Verlage, A. Albert, M. Brodski, E. Dietz-Laursonn, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer, A. Güth, M. Hamer, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, M. Olschewski, K. Padeken, T. Pook, M. Radziej, H. Reithler, M. Rieger, F. Scheuch, L. Sonnenschein, D. Teyssier, S. Thüer, G. Flügge, B. Kargoll, T. Kress, A. Künsken, J. Lingemann, T. Müller, A. Nehrkorn, A. Nowack, C. Pistone, O. Pooth, A. Stahl. Search for dark matter produced in association with heavy-flavor quark pairs in proton-proton collisions at \(\sqrt{s}= 13\,\text{TeV} \), The European Physical Journal C, 2017, 845, DOI: 10.1140/epjc/s10052-017-5317-4