Search for heavy neutral leptons in top quark decays with same-sign, same-flavor dilepton final states in proton-proton collisions at $\sqrt{s}$ = 13 TeV

Introduction

The observation of neutrino oscillations has confirmed the nonzero masses for at least two mass eigenstates of the standard model (SM) neutrinos ($\nu_\ell$) [1]. The mass-squared differences measured from neutrino oscillation experiments have set lower limits on the mass values of the two heavier neutrinos at 8.7 and 50 meV [1]. Cosmological measurements of the cosmic microwave background and baryon acoustic oscillations also place an upper limit on the sum of the neutrino masses at 64 meV [2]. An important question is the origin of the smallness of the neutrino masses. Since neutrinos are assumed to be massless in the SM, several beyond-the-SM (BSM) scenarios have been suggested to resolve this issue. For example, in the type-I seesaw mechanism [3, 4, 5, 6], Majorana mass ($m_{\mathrm{M}}$) terms are introduced for right-handed chiral neutrino fields, in addition to Dirac masses ($m_{\mathrm{D}}$) originating from the Yukawa couplings of neutrino fields to the Higgs field. This results in two physical states for each neutrino flavor: a light SM-like neutrino $\nu_\ell$ and a heavy neutral lepton (HNL) $N$. The mixing between $\nu_\ell$ and $N$ can be quantified by a mixing parameter $V_{\ell N}$, which—along with the physical masses—is determined by $m_{\mathrm{D}}$ and $m_{\mathrm{M}}$. This mixing parameter determines the HNL component of the neutrino flavor eigenstates and hence determines the interaction between the HNL and the W boson. In the simplest seesaw realization, the light-neutrino masses scale approximately as $m_{\mathrm{D}}^2/m_{\mathrm{M}}$, where $m_{\mathrm{M}}$ is effectively the HNL mass ($m_N$). More general realizations can allow larger HNL-SM mixing for a given $m_N$ [7].

Searches for HNL span a broad $m_N$ range. At low masses of $\mathcal{O}(100)$ eV, beta-decay spectra have been examined for spectral kinks that would indicate the presence of an HNL [8, 9]. At collider-accessible $m_N$ values up to $\mathcal{O}(1)$ TeV, the CERN LHC experiments searched for the decay of the on- and off-shell W bosons, directly produced in proton-proton (pp) collisions, into an HNL ($\mathrm{pp} \to\mathrm{W}^{\pm} \to N\ell^{\pm}$), where the hadronic ($N \to\ell^{\pm}q\bar{q}'$) and leptonic ($N \to\ell^{\pm}\ell'^{\mp}\nu$) decay chains of an HNL into quarks ($q$), charged leptons ($\ell$) including the tau leptons ($\tau$), and $\nu_\ell$ lead to dilepton and trilepton final states, respectively [10, 11, 12, 13]. The LHCb experiment searched for HNLs produced in B meson decays and decaying into a muon ($\mu$) and a pion [14]. Throughout the paper, $\ell$ indicates electrons (e) and muons, excluding $\tau$ leptons unless mentioned otherwise. These direct searches for HNLs have focused on analyzing their decay products or the energy spectrum of the charged lepton produced with the HNL.

The hypothetical production of HNLs via top quark (t) decays has recently received attention in the theory community and provides a complementary probe of HNLs below the W boson mass ($m_{\mathrm{W}}$) [15, 16, 17, 18, 19]. In some BSM scenarios, such as supersymmetric models with $R$-parity violation or models with leptoquarks, destructive interference can reduce the sensitivity of direct-search channels, increasing the complementarity of HNL production via top quark decays. In addition, the presence of both $\mathrm{W}^{+}$ and $\mathrm{W}^{-}$ bosons in top quark pair ($\mathrm{t}\bar{\mathrm{t}}$) events makes this production mode potentially sensitive to charge-parity violating effects in the HNL sector [19]. The ATLAS Collaboration recently searched for Majorana HNLs produced in top quark decays in the same-sign (SS), same-flavor (SF) dilepton final state, using 140 fb$^{-1}$ of data at $\sqrt{s} = 13$ TeV, and set limits for HNL masses between 15 and 75 GeV [20]. In this article, we present a search for HNLs in top quark decays using SS, SF dilepton events in the pp collision data at $\sqrt{s} = 13$ TeV, collected using the CMS detector between 2016 and 2018, corresponding to an integrated luminosity of 138 fb$^{-1}$.²

The search targets lepton number violating (LNV) processes in $\mathrm{t}\bar{\mathrm{t}}$ production, where the top quark decays into an HNL, which further decays into SM particles, $\mathrm{t} \to\mathrm{bW}^{+} \to\mathrm{b}\ell^{+}N \to\mathrm{b}\ell^{+}\ell^{+}\mathrm{W}^{-} \to\mathrm{b}\ell^{+}\ell^{+}\mathrm{f}\bar{\mathrm{f}}'$, while the top antiquark decays via the SM process $\bar{\mathrm{t}} \to\bar{\mathrm{bW}}^{-} \to\bar{\mathrm{b}}\mathrm{f}\bar{\mathrm{f}}'$, where f denotes an SM fermion. Charge-conjugated processes are implied throughout the text.

If $m_{\mathrm{N}} < m_{\mathrm{W}}$, the W boson produced in the decay of the HNL is off-shell; if $m_{\mathrm{N}} > m_{\mathrm{W}}$, the W boson from which the HNL originates is off-shell. Representative Feynman diagrams of these processes are depicted in Figure 1. In this search, only mixings of an HNL with the electron and muon neutrinos are considered, with strengths $|V_{\mathrm{eN}}|^2$ and $|V_{\mu\mathrm{N}}|^2$, respectively. Each mixing parameter is considered separately, with one mixing parameter nonzero and the other set to zero.

Searching for LNV decay modes is of paramount interest, as they provide a characteristic signature of Majorana HNLs. The discovery of a particle with a Majorana mass would provide evidence for the existence of different mass generation mechanisms in nature. In the context of an LNV search, the hadronic decay mode $\mathrm{N} \to\ell\mathrm{q}\overline{\mathrm{q}}'$ is the most powerful one, as there is no ambiguity between the lepton number conserving and violating decays. In the case of the leptonic decay mode, $\mathrm{N} \to\ell\ell'\nu$, it is impossible to conclusively determine whether LNV occurred, since the lepton number of the undetected neutrino through a decay involving a Z boson, $\mathrm{N} \to\mathrm{Z}\nu\to\ell\ell\nu$, cannot be known. The possibility of an HNL mixing with more than one generation of SM neutrinos also complicates the interpretation of events in the fully leptonic decay modes, even for the W boson mediated cases.

In this search, $m_{\mathrm{N}}$ values between 20 and 100 GeV are considered. For masses below $m_{\mathrm{W}}$, the HNL decay width scales approximately as $m_{\mathrm{N}}^5$ for a fixed mixing parameter, so a smaller $m_{\mathrm{N}}$ results in a longer lifetime of the HNL [16]. For $m_{\mathrm{N}}$ values below 20 GeV, the decay length of an HNL cannot be neglected within the allowed mixing parameter region and must be addressed using specialized search techniques optimized for long-lived particles. For masses above $m_{\mathrm{W}}$, this HNL production channel cannot be mediated by an on-shell W boson from a top quark decay. The production rate decreases rapidly with increasing $m_{\mathrm{N}}$ values, and this analysis therefore restricts the search to $m_{\mathrm{N}} < 100$ GeV. A machine-learning technique is used to classify events as background or signal, based on their distinguishing characteristics. The backgrounds are composed of nonprompt-lepton, conversion, charge mismeasurement, and prompt-lepton components, as discussed in § 5.

This paper is structured as follows. The CMS detector and object reconstruction are described in § 2, and the data and simulation samples are discussed in § 3. Event reconstruction and selection are presented in § 4. The background estimation techniques are summarized in § 5. The systematic uncertainties considered in this analysis are described in § 6. Lastly, the results and interpretations are presented in § 8, with an overall summary in § 9. Tabulated results are available in the HEPData record [21] for this analysis.

The CMS detector and event reconstruction

The CMS apparatus [22, 23] is a multipurpose, nearly hermetic detector, designed to trigger on [24, 25, 26] and identify e, $\mu$, photons ($\gamma$), and (charged and neutral) hadrons [27, 28, 29]. Its central feature is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3.8 T. Within the solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass and scintillator hadron calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward calorimeters extend the pseudorapidity ($\eta$) coverage provided by the barrel and endcap detectors. Muons are reconstructed using gas-ionization detectors interleaved with the layers of the steel flux-return yoke outside the solenoid. More detailed descriptions of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in Refs. [22, 23].

Events of interest are selected using a two-tiered trigger system. The first level (L1), composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100 kHz within a fixed latency of $4\,\mu\mathrm{s}$ [24]. The second level, known as the high-level trigger, consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to a few kHz before data storage [25, 26].

The global event description with the particle-flow (PF) algorithm [30] aims to reconstruct and identify each individual particle in an event, with an optimized combination of all subdetector information. In this process, the identification of the particle type (photon, electron, muon, charged hadron, neutral hadron) plays an important role in the determination of the particle direction and energy. Photons are identified as ECAL energy clusters not linked to the extrapolation of any charged-particle trajectory to the ECAL. Electrons are identified as a primary charged-particle track and potentially many ECAL energy clusters corresponding to this track extrapolation to the ECAL and to possible bremsstrahlung photons emitted along the way through the tracker material. Muons are identified as tracks in the central tracker consistent with either a track or several hits in the muon system, and associated with calorimeter deposits compatible with the muon hypothesis. Charged hadrons are identified as charged-particle tracks neither identified as electrons, nor as muons. Finally, neutral hadrons are identified as HCAL energy clusters not linked to any charged-hadron trajectory, or as a combined ECAL and HCAL energy excess with respect to the expected charged-hadron energy deposit. The primary vertex (PV) of the interaction is taken to be the vertex corresponding to the hardest scattering in the event, evaluated using tracking information alone, as described in Section 9.4.1 of Ref. [31].

Electrons reconstructed within the tracker coverage ($|\eta| < 2.5$), except for the calorimeter gap region ($1.442 < |\eta| < 1.566$), are used in the analysis. We consider electrons with transverse momentum $p_{\mathrm{T}} > 15\,\mathrm{GeV}$ and require them to be isolated from other particles in the event to reject nonprompt leptons from decays in jets. Two electron identification (ID) working points are used in the analysis, referred to as loose and tight ID in the following. Electron ID is based on track quality criteria, matching between the electron trajectory and the associated ECAL cluster, and shower shape observables [27]. The relative isolation ($I_{\mathrm{rel}}$) is defined as the scalar $p_{\mathrm{T}}$ sum of all PF particles reconstructed within a cone around the lepton direction divided by the lepton $p_{\mathrm{T}}$. The cone radius is defined by $\Delta R = \sqrt{(\Delta\eta)^2 + (\Delta\phi)^2}$, where $\phi$ is the azimuthal angle in radians. We use a $p_{\mathrm{T}}$-dependent cone radius of 0.2 for leptons with $p_{\mathrm{T}} < 50\,\mathrm{GeV}$, of $10\,\mathrm{GeV}/p_{\mathrm{T}}$ for $50 < p_{\mathrm{T}} < 200\,\mathrm{GeV}$, and of 0.05 for $p_{\mathrm{T}} > 200\,\mathrm{GeV}$, which improves the efficiency for high-$p_{\mathrm{T}}$ leptons by removing the accidental overlap with other particles [32]. The loose and tight electron IDs require $I_{\mathrm{rel}} < 0.4$ and 0.1, respectively. Furthermore, we require that there be at most one tracker layer that contributes no hit on the track, to reduce contributions from photon conversions [27]. To pass the tight ID selection, an electron must also be compatible with originating from the PV, requiring the ratio of the three-dimensional impact parameter of the associated track to its uncertainty (SIP$_{3D}$) to be less than 4. The loose electron ID is used to veto additional leptons and to estimate the nonprompt-lepton background. The tight electron ID is used for the signal region (SR) and background validation region (VR) definitions. The tight electron ID efficiency for the signal event selection varies between 40 and 90%, depending on the data-taking era, and on the electron $p_\mathrm{T}$ and $\eta$. Because the electron charge is crucial and has a higher mismeasurement rate than that of muons, we use a tight charge ID criterion requiring agreement in the charge measurements from three different methods. These methods are based on the Gaussian sum filter (GSF) track curvature [33], the closest Kalman filter track curvature [30], and the $\Delta\phi$ between the ECAL cluster and the GSF track extrapolated to the vertex. An event is discarded if it contains an electron for which the three charge measurements do not agree.

Muons are reconstructed in the range $|\eta| < 2.4$, and we consider muons with $p_\mathrm{T} > 15$ GeV. Two muon ID working points are defined in the analysis, referred to as loose and tight ID in the following, serving the same purposes as the electron ID working points. Muon ID is based on the number of reconstructed energy deposits in the tracker and in the muon system, and on the fit quality of the muon track [28]. Additionally, the loose (tight) muon ID requires $I_\mathrm{rel} < 0.6$ (0.1) and $\mathrm{SIP}_{3\mathrm{D}} < 5$ (3). The muon tight ID efficiency for the signal event selection varies between 70 and 98% depending on the data-taking era, and on the muon $p_\mathrm{T}$ and $\eta$.

For each event, hadronic jets are clustered from the PF particles using the infrared- and collinear-safe anti-$k_\mathrm{T}$ algorithm [34, 35] with a distance parameter of 0.4. Jet momentum is determined as the vectorial sum of all particle momenta in the jet, and is found from simulation to be, on average, within 5–10% of the true momentum over the entire $p_\mathrm{T}$ spectrum and detector acceptance. The jet energy resolution amounts typically to 15–20% at 30 GeV, 10% at 100 GeV, and 5% at 1 TeV [36]. Additional pp interactions within the same or nearby bunch crossings (pileup) can contribute extra tracks and calorimetric energy depositions, increasing the apparent jet momentum. To mitigate this effect, tracks identified to be originating from pileup vertices are discarded, and an offset correction is applied to correct for the remaining pileup contributions [37]. Jet energy corrections are derived from simulation studies so that the average measured energy of jets becomes identical to that of particle-level jets. In situ measurements of the momentum balance in dijet, $\gamma$ + jet, Z + jet, and multijet events are used to determine any residual differences between the jet energy scale in data and simulation, and appropriate corrections are made [36]. Jets reconstructed with $p_\mathrm{T}$ larger than 25 GeV and $|\eta|$ smaller than 2.4 are considered in the analysis. Additional selection criteria are applied to each jet to remove jets potentially dominated by instrumental effects or reconstruction failures [37]. Lastly, for jets with $p_\mathrm{T} < 50$ GeV, an additional identification algorithm based on a boosted decision tree technique, referred to as the pileup jet ID, is used to remove jets likely originating from pileup interactions [38].

To identify jets originating from the hadronization of b quarks, the DEEPJET [39, 40] b tagging algorithm is applied to each reconstructed jet in an event. The threshold on the discriminant score of the DEEPJET algorithm for a jet to be considered a b-tagged jet is chosen such that the tagging efficiency for b quark initiated jets is approximately 80% with a misidentification probability of 1% for jets initiated by light-flavor quarks or gluons. The tagging efficiencies measured in simulation are corrected as functions of the $p_\mathrm{T}$, $\eta$, and flavor of the jet to match those measured in data [41].

The missing transverse momentum vector $\vec{p}_\mathrm{T}^{\,\mathrm{miss}}$ is computed as the negative vector $p_\mathrm{T}$ sum of all the PF candidates in an event, and its magnitude is denoted as $p_\mathrm{T}^{\mathrm{miss}}$ [42]. The $\vec{p}_\mathrm{T}^{\,\mathrm{miss}}$ is modified to account for corrections to the energy scale of the reconstructed jets, muons, and electrons in the event.

Data and simulated samples

The analysis is performed using a sample of pp collision data at $\sqrt{s}=13$ TeV, collected with the CMS detector in 2016–2018 and corresponding to a total integrated luminosity of 138 fb$^{-1}$ [43, 44, 45]. Events used in this analysis are selected by SF dilepton triggers. Each trigger has $p_{\mathrm{T}}$ threshold requirements for the leading ($p_{\mathrm{T}}(\ell_{1})$) and subleading ($p_{\mathrm{T}}(\ell_{2})$) leptons. For each lepton in the trigger, a track-based $I_{\mathrm{rel}}$ threshold requirement is implemented with a fixed cone radius of 0.2 for electrons and 0.3 for muons. Depending on the data-taking era, the trigger also requires the two leptons to originate from the PV (via a requirement on the distance of closest approach, $d_{z}$, along the beam axis) and/or to pass a minimum invariant mass ($m(\ell\ell)$) threshold. Additionally, two single-lepton triggers for each lepton generation are used for the nonprompt-lepton background estimation. The thresholds are summarized in Table 1.

Trigger type	$p_{\mathrm{T}}(\ell_{1})$ (GeV)	$p_{\mathrm{T}}(\ell_{2})$ (GeV)	Other
Dilepton triggers
Dielectron	$>23$	$>12$	$I_{\mathrm{rel}}<0.20$; $d_{z}<0.2$ cm (2016)
Dimuon	$>17$	$>8$	$I_{\mathrm{rel}}<0.40$; $m(\mu\mu)>3.8$ GeV (2017, 2018); $d_{z}<0.2$ cm
Single-lepton triggers
Single electron	$>12$, $p_{\mathrm{T}}(e)<35$ GeV; $>23$, $p_{\mathrm{T}}(e)>35$ GeV	—	$I_{\mathrm{rel}}<0.20$, $p_{\mathrm{T}}(j_{1})>30$ GeV
Single muon	$>8$, $p_{\mathrm{T}}(\mu)<30$ GeV; $>17$, $p_{\mathrm{T}}(\mu)>30$ GeV	—	$I_{\mathrm{rel}}<0.40$

The signal events are simulated with MADGRAPH5_aMC@NLO [46] version 2.6.5 using the Majorana HNL model [47, 48, 49] at leading order (LO) in the four-flavor scheme. The $\mathrm{t}\overline{\mathrm{t}}$ cross section is calculated with the TOP++ version 2.0 program to next-to-next-to-leading order (NNLO) in perturbative quantum chromodynamics (QCD), including soft-gluon resummation to next-to-next-to-leading log (NNLL) order (as discussed in Ref. [50] and references therein), and assuming a top quark mass of 172.5 GeV. The signal events from $\mathrm{t}\overline{\mathrm{t}}$ production with subsequent top quark decays into an HNL are generated with up to one additional parton in the matrix element calculations. Subsequently, jets from parton shower are merged with those from the matrix element calculations using the MLM algorithm [51]. Decays of the top quarks, W bosons, and HNLs are modeled with the same generator, considering the spin correlations among the decay products. The signal samples are simulated for $m_{N}$ values of 20, 30, 40, 50, 60, 70, 75, 85, 95, and 100 GeV. The decay width of the HNL in the considered mass range is less than 0.1 MeV. The HNL is assumed to decay promptly. The nonzero HNL mixing parameter $|V_{\ell N}|$ is set to 0.01. In addition to the decay mode introduced in § 1, the generated signal samples include all HNL decay modes leading to final states with more than two charged leptons. We exclude $\tau$ leptons from the primary HNL vertices ($W\to\tau N$ and $N\to\tau W$), while allowing for $\tau$ lepton production via W and Z boson decays. The full decay modes considered are:

where $f$ and $f'$ can be any fermion and $N$ decay modes include both charged- and neutral-current. The inclusive signal cross section is set to 10 fb, and the fitted signal strength limit directly scales this cross section. The branching fraction limit of a top quark into an HNL is then obtained by dividing the fitted signal cross section limit by $2\sigma(\mathrm{t\bar{t}})$. This branching fraction limit is then compared to the branching fraction calculated in simulation with $|V_{\ell N}| = 0.01$ and converted into the mixing parameter limit using the relation $\mathcal{B}(\mathrm{t} \to\mathrm{b}\ell\mathrm{N}) \propto|V_{\ell N}|^2$.

Background processes involving leptons produced promptly and from photon conversions are estimated using simulation. Additionally, simulation is used to model the background from lepton charge mismeasurement, causing opposite-sign (OS) lepton pairs to be reconstructed as SS lepton pairs. These processes are generated at next-to-leading order (NLO), and their cross sections are calculated at the same order, unless a specific other order is mentioned. The following processes are simulated using MADGRAPH5_aMC@NLO: Drell–Yan (DY) production, WZ diboson production with decays into three charged leptons and a neutrino, triboson production with all possible combinations of vector bosons (VVV, V = W or Z), W boson production in association with a photon (W$\gamma$), Higgs boson production in association with a vector boson (VH), vector boson production in association with a $\mathrm{t\bar{t}}$ pair ($\mathrm{t\bar{t}}$V), single top quark production via the $s$ channel, and single top quark production in association with a Z boson (tZ). The DY processes with $m(\ell\ell)$ below 50 GeV and W$\gamma$ production are generated at LO. The decays in VH and $\mathrm{t\bar{t}}$W processes are simulated using MADSPIN [52]. The following processes are generated using POWHEG v2.0 [53, 54, 55]: production of a pair of Z bosons (ZZ) decaying into four leptons [56], Higgs boson production in association with a $\mathrm{t\bar{t}}$ pair ($\mathrm{t\bar{t}}$H) [57], Higgs boson production via gluon fusion (ggH) [58] and via vector boson fusion (qqH) [59], single top quark production in the $t$ channel and in association with a W boson [60], and $\mathrm{t\bar{t}}$ production [61]. For the normalization of the ZZ process, the production cross section is calculated at NNLO [62]. The decays of the Higgs boson in ggH and qqH production are simulated with JHUGEN [63], while MADSPIN is used for the top quark decay in the single top quark $t$-channel production. Lastly, the diboson processes, except for the Z boson pair production with decays into four leptons and WZ diboson production with decays into three charged leptons and a neutrino, are generated at LO using PYTHIA 8.240 [64].

All simulated samples are interfaced with PYTHIA to perform parton shower and hadronization. For all simulated samples, the description of the underlying event is provided by the CP5 tune [65]. The NNPDF 3.1 NNLO [66] parton distribution function (PDF) set is used for all simulated samples. Pileup effects are included via simulated minimum bias pp collisions superimposed on the primary simulated interaction, with the multiplicity matching that in data. Simulation of the CMS detector response is performed with GEANT4 [67].

Event selection and analysis strategy

The offline selection requires an electron pair of $p_{\mathrm{T}} > (25, 15)$ GeV or a muon pair of $p_{\mathrm{T}} > (20, 15)$ GeV, where the $p_{\mathrm{T}}$ thresholds are chosen such that the selected events lie at the trigger efficiency plateau. The baseline selection of the SR requires an event to have exactly two SF SS leptons to target the leptons from the production and decay of the HNL and vetoes the event if there is a third lepton that passes the loose lepton ID. To remove events involving the Z boson resonance with a charge-mismeasured electron, the reconstructed dielectron invariant mass ($m(ee)$) must be outside a 10 GeV window around the Z boson mass ($m_{\mathrm{Z}}$) of 91.2 GeV [1]. However, this selection is not required for the dimuon channel because of the significantly lower charge mismeasurement rate for muons. The dimuon mass must be greater than 4 GeV, so that it is above the $m(\ell\ell)$ threshold of the trigger. The signal events are expected to have four jets from the W boson decays and two b-tagged jets from the top quark decays. However, the events are required to have at least three jets, which must include at least one b-tagged jet. Because signal jets tend to be more energetic and likely to pass the acceptance for $m_{\mathrm{N}} > m_{\mathrm{W}}$, we define two baseline selections. When testing a signal hypothesis with $m_{\mathrm{N}} < m_{\mathrm{W}}$, we require at least one non-b-tagged jet; when testing a hypothesis with $m_{\mathrm{N}} > m_{\mathrm{W}}$, we require at least two. The selected data and background event sets for the two mass regimes can overlap. After the baseline selection, the signal acceptance times efficiency ranges from 0.987 (2.71) to 8.58 (7.54)%, depending on $m_{\mathrm{N}}$, in the electron (muon) channel. The dominant source of background events passing the SR selection comes from events with nonprompt leptons. This background is estimated by measuring the misidentification rate of a nonprompt lepton in the background measurement region (BMR) and applying it to the data in the application region (AR), as is further explained in § 5.

Because of the high multiplicity of partons in the signal process, only a small fraction of signal events has all the final-state partons within the $p_{\mathrm{T}}$ and $\eta$ acceptances. For the simulated signal events passing the baseline selection, the efficiency ranges from 0.02 to 4% for matching all six partons at the generator level to the reconstructed jets within a $\Delta R$ of 0.4, depending on $m_{\mathrm{N}}$. Therefore, instead of reconstructing the full signal decay products, we search for the signal by inspecting partial decay products using a multivariate analysis based on the gradient boosted decision tree (BDT) technique. Since the $t\bar{t}$ background is the main contribution to the nonprompt-lepton background, only signal samples and dilepton and lepton+jets $t\bar{t}$ background simulation samples are used for the training. As discussed in § 8, a training based only on $t\bar{t}$ events still separates other backgrounds from the signal. Adding $t\bar{t}V$ and $t\bar{t}H$ backgrounds to the BDT training did not improve the expected sensitivity and led to instabilities in the nuisance parameter fit because of the small number of events in the SR. Despite the imbalance between the number of signal and background events, we used all $t\bar{t}$ background and signal events, splitting each of them evenly: half for training and the other half for testing. Since the number of signal events passing the baseline selection varies for each $m_{\mathrm{N}}$ and lepton flavor scenario, the total number of events used in the training process differs among the ten classifier models (for $m_{\mathrm{N}} = 20$, 50, 75, 85, and 100 GeV, each for dielectron and dimuon final states). Each model is applied to the signal sample with $m_{\mathrm{N}}$ closest to the training signal sample $m_{\mathrm{N}}$.

The BDT classifier utilizes a set of input variables designed to exploit the kinematic differences between signal and background, specifically focusing on the energy scales, angular separations, and reconstructed masses associated with the final-state leptons and jets, as summarized in Table 2. For the reconstructed mass variables, jet candidates from the decays of a W boson ($j_{\mathrm{W}}$) and an HNL ($j_{\mathrm{N}}$) are determined using the combination of jets and leptons that best reconstructs the W boson and top quark masses. Applying the classifier model yields a score in the range [$-1,1$], where signal-like (background-like) events have scores closer to 1 ($-1$). Although the exact order of importance of the input variables differs across different $m_{\mathrm{N}}$ and final states, the $m(\ell\ell)$ and angular separation of the two leptons are among the most important variables in all training models. These variables, along with the $p_{\mathrm{T}}$ of the leading lepton and $p_{\mathrm{T}}^{\mathrm{miss}}$ are shown in Figure 2–Figure 5. The backgrounds shown in these figures are explained in § 5. The performance of the training models is slightly better in the dimuon case, because of the larger number of events from higher muon ID efficiency and lower $p_{\mathrm{T}}$ threshold. For both dielectron and dimuon final states, the performance slightly worsens as $m_{\mathrm{N}}$ approaches $m_{\mathrm{W}}$, resulting in the area under the receiver operating characteristic curve decreasing from 0.90–0.93 to 0.82–0.88. As $m_{\mathrm{N}}$ approaches $m_{\mathrm{W}}$, one of the two leptons becomes soft and is likely to fail the selection. As a result, the lepton that passes the selection would pair with a lepton from the SM top quark decay or with a nonprompt lepton, leading to background-like BDT outputs.

Variable	$m_N < m_W$	$m_N > m_W$	Definition
$N(\mathrm{j})$	✓	✓	jet multiplicity
$N(\mathrm{b})$	✓	✓	b-tagged jet multiplicity
$p_T(\ell_a)$	✓	✓	$a^{\mathrm{th}}$ lepton $p_T$ ($a = 1,2$)
$p_T^{\mathrm{miss}}$	✓	✓	missing transverse momentum
$H_T$	✓	✓	scalar sum of jet $p_T$
$\Delta R(\ell_1,\ell_2)$	✓	✓	$\Delta R$ between two leptons
$\Delta R(\ell_a,j_b)$	✓	✓	$\Delta R$ between $a^{\mathrm{th}}$ lepton and $b^{\mathrm{th}}$ jet ($a = 1,2$, $b = 1,2,3$)
$m(\ell_1\ell_2)$	✓	✓	dilepton invariant mass
$m(\ell_a j_{N,1})$	✓	✓	invariant mass of $a^{\mathrm{th}}$ lepton and leading HNL jet candidate ($a = 1,2$)
$m(\ell_1\ell_2j_{N,1})$	✓		invariant mass of two leptons and leading HNL jet candidate
$m(b_1\ell_1\ell_2j_{N,1})$	✓	✓	invariant mass of two leptons, leading b-tagged jet, and leading HNL jet candidate
$m(\ell_a j_{N,1}j_{N,2})$		✓	invariant mass of $a^{\mathrm{th}}$ lepton and two HNL jet candidates ($a = 1,2$)
$m(b_1\ell_1\ell_2j_{N,1}j_{N,2})$		✓	invariant mass of two leptons, leading b-tagged jet, and two HNL jet candidates
$m_T(\ell_1\vec{p}_T^{\mathrm{miss}})$	✓	✓	transverse mass of leading lepton and $\vec{p}_T^{\mathrm{miss}}$
$m_T(b_1\ell_1\vec{p}_T^{\mathrm{miss}})$	✓	✓	transverse mass of leading lepton leading b-tagged jet, and $\vec{p}_T^{\mathrm{miss}}$
$m(\ell_2j_{L,1})$	✓	✓	invariant mass of subleading lepton and leading non-b-tagged jet
$m(\ell_2j_{W,1}j_{W,2})$		✓	invariant mass of subleading lepton and two W boson jet candidates

Background estimation

Backgrounds are classified into four categories: nonprompt leptons, lepton charge mismeasurement, conversion, and prompt SS dileptons, as listed below. Each background is discussed in detail after the list. The SR and the VRs used to validate the background estimates are summarized in Table 3.

Selection	SR	Nonprompt VR	Conversion VR	ChgMis VR
$N(\ell)$	2	3	3	2
Lepton charge	SS	—	—	SS
Lepton $p_{\mathrm{T}}$ (GeV)	$e_{1,2}>25,15$; $\mu_{1,2}>20,15$	$e_{1,2}>25,15$; $\mu_{1,2}>20,15$	$e_{1,2}>25,15$; $\mu_{1,2}>20,15$	$e_{1,2}>25,15$
Lepton flavor	ee; $\mu\mu$	$ee\mu$ or eee; $\mu\mu e$ or $\mu\mu\mu$	$ee\mu$ or eee; $\mu\mu e$ or $\mu\mu\mu$	ee
$N(j)$	$\geq3$	—	—	—
$N(b)$	$\geq1$	0	0	—
$N(J_{\mathrm{L}})$	$\geq1$ if $m_{\mathrm{N}}<m_{\mathrm{W}}$; $\geq2$ if $m_{\mathrm{N}}>m_{\mathrm{W}}$	—	—	—
Mass window (GeV)	$\lvert m(\mathrm{ee})-m_{\mathrm{Z}}\rvert>10$; $m(\mu\mu)>4$	OS SF pair; mass $\in[81.2,101.2]$	$m(\ell\ell\ell)\in[81.2,101.2]$	$m(\mathrm{ee})\in[76.2,106.2]$

1. Nonprompt-lepton backgrounds: the contribution of nonprompt leptons originating from jets is estimated using data in the AR and the matrix method [68], which uses the lepton misidentification rate as a function of lepton $p_{\mathrm{T}}$ and $\eta$, as measured in the BMR.

2. Lepton charge mismeasurement backgrounds (ChgMis): a lepton charge can be mismeasured, causing an OS lepton pair to appear as an SS pair. The lepton charge mismeasurement rate (CMR) is measured using simulation and corrected to match that of the data as a function of the lepton $p_{\mathrm{T}}$ and $\eta$.

3. Conversion backgrounds: leptons from photon conversion can pass the ID requirements and pair with a prompt lepton of the same charge. The contribution from this background source is estimated using simulations.

4. Prompt-lepton backgrounds: backgrounds that contain only the prompt leptons from W or Z bosons decays are estimated using simulation.

Nonprompt-lepton backgrounds

Nonprompt-lepton backgrounds refer to processes with fewer than two prompt leptons that still pass the baseline selection by including at least one jet-induced nonprompt lepton. This background constitutes the dominant contribution, accounting for approximately 74 (87)% of the total background in the electron (muon) final state. The nonprompt-lepton backgrounds are estimated with the matrix method. This method extrapolates the number of events in the SR from AR, defined by the same baseline selection as the SR but using the loose lepton ID requirements instead of the tight ID ones. The AR thus serves as the sideband region in the lepton ID variables. The extrapolation from the AR to the SR is done through an extrapolation factor, defined as the fraction of leptons that pass the tight ID requirements relative to those that pass the loose ID ones. The extrapolation factor is measured in an independent BMR enriched with dijet events.

The extrapolation factor for nonprompt leptons is called the “misidentification rate”, while that for prompt leptons is called the “prompt rate”. The composition of the observed events ($\vec{N}_{\mathrm{obs}}$) is categorized by how many leptons pass the tight ID requirement (T) or fail the tight ID requirements but pass the loose ID requirements (L). The composition of the underlying events ($\vec{N}_{\mathrm{origin}}$) is categorized by the origin of each lepton as prompt (p) or nonprompt (n). The composition of the observed events and that of the underlying events are connected by a connection matrix (M) constructed from prompt rates ($\varepsilon(\mathrm{p})$) and misidentification rates ($\varepsilon(\mathrm{n})$). Each element of the connection matrix represents the probability that a specific lepton origin configuration is observed in a given ID configuration:

The matrix elements of M, which connect the observed ID composition $N_{\mathrm{ID}_1\mathrm{ID}_2}$ (with $\mathrm{ID}_k = \mathrm{T}$ or $\mathrm{L}$) and the underlying origin composition $N_{o_1o_2}$ (with $o_k = \mathrm{p}$ or $\mathrm{n}$), are given by

The underlying composition can be obtained by applying the inverse of the matrix ($\mathrm{M}^{-1}$) to the observed ID composition of events. Because the contribution from the prompt background is small, $\varepsilon(\mathrm{p})$ is assumed to be unity, as its effect on the nonprompt background is negligible.

Lepton misidentification rates in data are measured in single-lepton events passing single-lepton triggers. Two different single-lepton triggers described in Table 1 are used depending on the lepton $p_{\mathrm{T}}$. Events containing only jets produced through the strong interaction, referred to as QCD multijet events, are selected for the measurement, requiring exactly one loose ID lepton and at least one jet with $p_{\mathrm{T}} > 40$ GeV and $\Delta R(\ell,j) > 1$. To reduce the contamination from processes with prompt leptons, such as W boson production in association with jets, $p_{\mathrm{T}}^{\mathrm{miss}} < 25$ GeV and $m_{\mathrm{T}}(\vec{p}_{\mathrm{T}}^{\ell}\vec{p}_{\mathrm{T}}^{\mathrm{miss}}) < 25$ GeV are also required. Residual prompt-lepton contributions after the selection are estimated using simulation and are subtracted in the calculation of the misidentification rates. The misidentification rate ranges from 0.18 (0.10) to 0.46 (0.25) for an electron (muon), depending on the lepton $p_{\mathrm{T}}$ and $\eta$. A closure test is performed on the $\mathrm{t\bar{t}}$ simulation. For this closure test, the nonprompt-lepton events estimated using misidentification rates obtained from simulation and the true nonprompt-lepton events identified using generator-level information are compared in the SR. The result of the closure test is presented in Figure 6. The closure test shows agreement within 10 (15)% for dielectron (dimuon) events. The nonprompt-lepton background estimation is validated by comparing it to the data in a separate VR with three leptons, where an OS SF lepton pair from the Z boson decay is selected by requiring $|m(\ell\ell)-m_{\mathrm{Z}}| < 10$ GeV, and the third lepton is assumed to be the nonprompt lepton. In the case of three SF leptons, among two possible combinations of OS lepton pairs, the lepton pair from Z boson is defined as the pair with $m(\ell\ell)$ closer to $m_{\mathrm{Z}}$. The $p_{\mathrm{T}}$ distribution of the nonprompt lepton in the VR is shown in Figure 7, with good agreement between data and the background prediction.

Lepton charge mismeasurement backgrounds

Lepton charge mismeasurement backgrounds are processes with two OS leptons in which the reconstructed charge of one of the leptons is mismeasured. From the earlier measurement in CMS, the CMR for muons is known to be less than $10^{-5}$ and does not produce a measurable amount of background, so it is ignored [69]. The SM processes that contribute to this background include DY, $\mathrm{t\bar{t}}$, diboson, and single top quark production processes. A direct estimate of the events with two SS leptons from the respective simulated samples suffers from low event counts and results in large statistical uncertainties. Consequently, we estimate this background by measuring the CMR in data and applying it to events in the simulated samples that pass the SR selection, except for the SF OS leptons requirement. The electron CMR is determined from simulated Z $\to$ ee events as a function of the electron $p_{\mathrm{T}}$ and $\eta$, and is subsequently corrected to match the observation in data. The electron CMR in simulation is measured by comparing the number of charge-mismeasured electrons and the number of electrons with the correct charge using the generator-level information. The electron CMR in data is measured using the Z $\to$ ee resonance. Events must have exactly two electrons, with $p_{\mathrm{T}}$ larger than 25 and 15 GeV, respectively, and pass the tight ID with the $m(\mathrm{ee})$ between 60 and 120 GeV. The number of SS and OS Z $\to$ ee events is extracted by fitting the $m(\mathrm{ee})$ distributions to the template shape from simulation.

An iterative approach is used to correct the CMR in simulation to match that in data. At each step of the iteration, the CMR ratio between data and simulation is measured and used to scale the number of simulated events with a charge-mismeasured electron. Because both electrons in each event contribute to their corresponding CMR bin depending on whether their charge is mismeasured or not, the new CMR for the simulation does not perfectly agree with the data CMR. The iteration of scaling the CMR in simulation stops when the magnitude of the corrections for the CMR falls below 1.5% for all $p_{\mathrm{T}}$-$\eta$ bins. After the iteration, the product of the correction factors at each step becomes the total correction factor. The total correction factor ranges from 0.74 to 1.61, depending on the electron $p_{\mathrm{T}}$, $\eta$, and the data-taking era. The electron charge mismeasurement background is validated in data using charge-mismeasured DY events with tighter selection on the $m(\mathrm{ee})$ than the one used in the data CMR measurement region: the two electrons from the Z boson decay are measured to have the same charge, and their invariant mass is within 15 GeV of $m_{\mathrm{Z}}$. The invariant mass distribution shows good agreement between data and the background prediction within the uncertainty, as shown in Figure 8.

Conversion backgrounds

The conversion backgrounds include internal and external photon conversions, depending on whether the conversion occurs at the collision point or within the detector, respectively. Since the rate of external conversion is proportional to the inverse of the produced lepton mass squared [70], it is only significant for electrons. For the validation of this background, we select events with three leptons: two from a Z boson decay and an additional lepton from a photon conversion. The invariant mass of the three leptons is required to be within a 10 GeV window around $m_{\mathrm{Z}}$. In this three-lepton VR, we observe reasonable agreement between data and the background prediction within the uncertainty, as shown in Figure 9. Therefore, the conversion backgrounds are taken from the simulation.

Prompt-lepton backgrounds

Prompt-lepton backgrounds originate from processes involving at least two SS leptons produced in the decays of V bosons, including leptons from decays of $\tau$ leptons originating from these bosons. These contributions are estimated using simulated samples. Major contributions come from $\mathrm{t\bar{t}}$V, $\mathrm{t\bar{t}}$H, WZ, and ZZ production. Processes with small expected contributions are combined as “rare” background, dominated by the production of tZ, VVV, ggH, and qqH.

Systematic uncertainties

Various sources of systematic uncertainties are considered in the analysis, categorized as background, experimental, and theoretical uncertainties. Experimental and theoretical uncertainties are applied to both signal and simulated backgrounds. The systematic uncertainties are implemented as nuisance parameters in the analysis. Depending on their nature, the uncertainties are treated as correlated or uncorrelated between the data-taking eras. The summary of the uncertainties is shown in Table 4.

Table 4: Summary of systematic uncertainties. Each uncertainty is implemented as either a rate or shape uncertainty. Uncertainties affecting only the normalization are marked as “Rate” in the table. Those affecting BDT template shapes are indicated as “Shape.” For shape uncertainties, “—” indicates that its effect is propagated to the BDT score distribution through the corresponding up and down templates and may depend on the process, channel, and HNL mass hypothesis.

Source	Assigned variation	Type
Background uncertainties
Nonprompt leptons	20%	Rate
CMR fitting model	—	Shape
Charge-mismeasurement closure test	6%	Rate
Electron energy shifts	—	Shape
Conversions	20%	Rate
Experimental uncertainties
Lepton ID efficiency	—	Shape
Electron reconstruction	—	Shape
Electron energy correction	—	Shape
Muon momentum scale	—	Shape
Jet energy scale	—	Shape
Jet energy resolution	—	Shape
Pileup jet identification	—	Shape
b tagging efficiency corrections	—	Shape
Integrated luminosity	1.6%	Rate
Inelastic pp cross section	4.6%	Rate
Trigger efficiencies	1%	Rate
L1-prefiring inefficiency	—	Shape
Theoretical uncertainties
Signal PDF and $\alpha_{\mathrm{S}}$ variations	—	Shape
Signal $\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ variations	—	Shape
Signal parton shower modeling	—	Shape
$\mathrm{t}\bar{\mathrm{t}}$ cross section	$^{+3.5\%}_{-4.4\%}$	Rate
$\mathrm{t}\bar{\mathrm{t}}\mathrm{Z}$ cross section	11.9%	Rate
$\mathrm{t}\bar{\mathrm{t}}\mathrm{W}$ cross section	13.3%	Rate
$\mathrm{t}\bar{\mathrm{t}}\mathrm{H}$ cross section	10.0%	Rate
WZ cross section	12%	Rate
ZZ cross section	6.4%	Rate
tZ cross section	5.2%	Rate
VVV cross section	50%	Rate
ggH cross section	50%	Rate
qqH cross section	50%	Rate
VH cross section	50%	Rate

Background systematic uncertainties

The misidentification rates in nonprompt-lepton backgrounds contribute the largest uncertainty in the total background estimation. We use two methods for assessing the uncertainties in the nonprompt-lepton background. First, from the closure test described in § 5, an uncertainty of 10 (15)% is set in the dielectron (dimuon) channel. Secondly, the uncertainty is assessed by analyzing three sources of uncertainty in the misidentification rate itself. The normalization of the prompt-lepton background is varied up and down by the theoretical cross section uncertainty when subtracting it from data. The minimum jet $p_{\mathrm{T}}$ requirement for the BMR selection is varied up and down by 10 or 5 GeV, depending on the single-lepton trigger threshold. The dependence of the measured misidentification rate on the jet flavor is quantified by requiring at least one b-tagged jet among the jets. The relative contributions of these three sources of uncertainty vary with the lepton $p_{\mathrm{T}}$ and $\eta$. In the SR, the combined uncertainty in the nonprompt-lepton background after propagating the effects of the three uncertainty sources is within a 20% band around the nominal BDT score distribution. To conservatively account for the uncertainties assessed from the closure test and the misidentification rate, a flat 20% uncertainty is assigned to the nonprompt-lepton background. This uncertainty is treated as uncorrelated between the data-taking eras.

Three sources of uncertainty are considered in the estimation of the charge-mismeasured background. ground. The first uncertainty arises from the measurement of the correction factors for the CMR. This uncertainty is quantified by using different fitting models for data CMR extraction, and it is found to vary between 2 and 7% across the $p_{\mathrm{T}}$-$\eta$ bins. Since this uncertainty is dominated by the statistical component, it is treated as uncorrelated between the data-taking eras. The second uncertainty accounts for an imperfect parameterization of the CMR due to the limited sample size, and it is estimated to be 6% by performing a closure test on $t\bar{t}$ simulated events. The test consists of estimating the number of SS dielectron events by applying the measured CMR to the OS dielectron events and comparing the result to the observed number of SS dielectron events. This uncertainty is treated as correlated between the data-taking eras. The last source of uncertainty originates from the electron energy measurement. Highly energetic bremsstrahlung can deflect the electron path, leading to a mismeasurement of the electron charge. Additionally, the emitted radiation may become spatially separated from the electron and not be taken into account in the electron energy reconstruction. To account for this, we emulate the energy underestimation in the simulated events with OS dielectron pairs by shifting the electron $p_{\mathrm{T}}$ in the VR. The nominal shift is optimized by minimizing the $\chi^2$ difference between the OS and SS $m(ee)$ distributions over a $\pm3\%$ energy shift scan. We evaluate the systematic uncertainty by repeating this optimization in two kinematic regions, using events with both electrons $p_{\mathrm{T}} > 40$ GeV or both $p_{\mathrm{T}} < 40$ GeV. The maximum absolute deviation from the nominal shift is assigned as a symmetric systematic uncertainty. The uncertainty in the electron $p_{\mathrm{T}}$ from this correction is between 0.7 and 1.5%, depending on the electron $\eta$ and data-taking era. This uncertainty is treated as correlated between the data-taking eras.

For the conversion background, a 20% uncertainty is assigned to the rate based on the agreement in the three-lepton VR. The simulation underestimates the conversion event rate in the three-lepton VR by approximately 20% in all data-taking eras for dimuon events, while the data-to-simulation difference for electron conversions varies between 0 and 20%, depending on the data-taking eras. Consequently, the muon conversion background uncertainty is treated as correlated, while the electron one is treated as uncorrelated, between the data-taking eras.

Experimental systematic uncertainties

The uncertainty in lepton ID is included, ranging from 1 to 4% depending on the lepton $p_{\mathrm{T}}$ and $\eta$, and is treated as correlated between the data-taking eras. Lepton ID correction factors are measured using the “tag-and-probe” method [71], where the efficiency is measured as the fraction of the reconstructed leptons from DY events that pass the ID requirement. The uncertainty is then determined by changing the minimum $p_{\mathrm{T}}$ requirement on the leptons, the fit function for the $m(\ell\ell)$ distribution, and the Monte Carlo generator.

For electrons, there are uncertainties associated with the efficiency of electron reconstruction and energy correction [27]. These uncertainties are treated as correlated between different data-taking eras. In the case of muons, the reconstruction efficiency is close to unity, and no statistically significant evidence for the difference between data and simulation is observed. The muon energy scale uncertainty is considered, and it is treated as correlated between the data-taking eras [28].

Uncertainties in the jet energy scale and resolution are assessed by reevaluating observables in simulated events with the jet energy shifted by its uncertainty. The systematic uncertainties associated with the jet energy scale and resolution corrections are considered uncorrelated between the data-taking eras. These uncertainties are propagated to the $p_{\mathrm{T}}^{\mathrm{miss}}$ uncertainty. Additionally, the uncertainty in the energy scale of PF particles not associated with jets, leptons, and photons, known as unclustered energy, is propagated to the $p_{\mathrm{T}}^{\mathrm{miss}}$ uncertainty [42]. The uncertainties in the b tagging efficiency correction are evaluated by varying the correction factors for light-quark and gluon, c quark, and b quark jets in simulation [40]. Each b tagging uncertainty has both correlated and uncorrelated sources between the data-taking eras. The uncertainty in the efficiency correction factor corresponding to the pileup jet identification is also applied and treated as uncorrelated between the data-taking eras [38].

Other experimental uncertainties have small impacts. The uncertainty in the integrated luminosity [43, 44, 45] has both correlated and uncorrelated components between the data-taking eras. The overall integrated luminosity uncertainty for the 2016–2018 data set is 1.6%. The uncertainty in the number of pileup interactions is estimated by applying a $\pm4.6\%$ variation to the total inelastic pp cross section used to reweight simulated events to match the pileup distribution observed in data [72] and is treated as correlated between the data-taking eras. The uncertainties in the trigger efficiencies are measured using the tag-and-probe method described earlier and are treated as correlated between the data-taking eras. During the 2016 and 2017 data-taking periods, a gradual shift in the timing of the inputs of the ECAL L1 trigger caused a trigger inefficiency, referred to as the “L1-prefiring”. An uncertainty related to the inefficiency from the L1-prefiring in 2016 and 2017 is applied and treated as uncorrelated between the data-taking eras [73].

The statistical uncertainty of the data used for the nonprompt-lepton background estimations, as well as that of the simulated samples used in all background estimations, is considered.

Theoretical systematic uncertainties

An uncertainty of $^{+3.5}_{-4.4}\%$ is assigned to the signal production cross section [50]. Theoretical uncertainties in the signal acceptance from the uncertainties in the PDFs and in the renormalization ($\mu_\mathrm{R}$) and factorization ($\mu_\mathrm{F}$) scales are considered. For the uncertainty arising from PDFs, the changes in the acceptance for 100 replicas of the PDF set are added in quadrature. The uncertainties in the signal acceptance arising from $\mu_\mathrm{R}$ and $\mu_\mathrm{F}$ are studied by varying the scales by factors of 0.5 and 2 individually or simultaneously, excluding the two variations in the opposite directions, and taking the envelope of all variations. The uncertainty in the parton shower modeling is estimated by varying initial- and final-state radiation scales separately by factors of 0.5 and 2 and is defined by the envelope obtained from these variations. Among the signal theoretical uncertainties, the ones from initial- and final-state radiation, and from the $\mathrm{t\bar{t}}$ cross section are dominant.

Three theoretical uncertainties in the cross sections of the background processes are taken into account: $\mu_\mathrm{R}$ and $\mu_\mathrm{F}$, PDFs, and strong coupling constant ($\alpha_\mathrm{S}$), added in quadrature. Because the uncertainties resulting from higher and lower scale choices are comparable in magnitude, the larger deviation is assigned as a symmetric uncertainty. For WZ production, the radiation zero effect is considered, whose magnitude decreases in the diagrams with additional partons in higher-order QCD calculations [74]. We assign an 11% uncertainty due to the radiation zero effect, resulting in a total uncertainty of 12%. For ZZ production, we apply a cross section uncertainty of 6.4% [62]. For $\mathrm{t\bar{t}}$ + V/H we apply the uncertainty from Ref. [75]. For $\mathrm{t\bar{t}}$H processes, the uncertainty in the Higgs boson branching fraction is also included. The total uncertainty values obtained are 11.9% for $\mathrm{t\bar{t}}$Z, 13.3% for $\mathrm{t\bar{t}}$W, and 10.0% for $\mathrm{t\bar{t}}$H. For the tZ process, the total uncertainty is 5.2% [76]. For VVV, ggH, qqH, and V H production, a conservative 50% uncertainty is assigned. All uncertainty sources with theoretical origins are considered as fully correlated between the data-taking eras.

Theoretical systematic uncertainties

Results

To extract the final results, a simultaneous binned maximum likelihood fit with profiling of nuisance parameters is performed over the 2016–2018 data set, separately for each $m_{\mathrm{N}}$ hypothesis in the SR, using the BDT classifier score distribution. The following results have been determined using the CMS statistical analysis tool COMBINE [77], which is based on the ROOFIT [78] and ROOSTATS [79] frameworks. Nuisance parameters are categorized into two groups [80]: rate uncertainties are implemented with log-normal nuisance parameters, while shape uncertainties are incorporated through variations of the BDT templates with Gaussian-constrained nuisance parameters. A simplified Barlow–Beeston approach [81], with a single nuisance parameter per bin of the distribution, is used to treat the statistical uncertainties due to the limited size of the simulated samples. The uncertainties with the greatest impact for all mass hypotheses are the statistical uncertainties of the background templates in the individual bins. With its conservative approach, the nonprompt-lepton background uncertainty gets reduced by a factor of 2–4, depending on the data-taking era, lepton flavor, and $m_{\mathrm{N}}$ hypothesis. To assess the compatibility between the observed data and the background-only hypothesis, a goodness-of-fit test is performed using the $\chi^{2}$ test statistic. The expected distribution of the test statistic is derived from 2000 background-only pseudo-experiments generated from the post-fit nuisance parameter configurations. The observed data yield a $p$-value [82] of 0.3 or above for all signal models, indicating that the data are compatible with the background model. The fit is performed for each mass hypothesis to assess its dependence on the HNL mass. The observed and expected BDT score distributions after the fit in the SR are shown in Figure 10–Figure 12 for various $m_{\mathrm{N}}$ hypotheses. The score distributions in data are consistent with those in the background. The figures show that the BDT classifier trained on the $\mathrm{t}\overline{\mathrm{t}}$ process also yields low-score values for the majority of other minor backgrounds. Therefore, a classifier trained on dilepton and lepton+jets $\mathrm{t}\overline{\mathrm{t}}$ events as the only background sample is sufficient to distinguish other backgrounds from the signal.

Based on the classifier score distribution, an upper limit at 95% confidence level (CL) on the signal strength is determined using the CL$_{\mathrm{s}}$ criterion [83, 84] in a modified frequentist approach. The test statistic used is the ratio of profiled likelihoods defined according to the LHC-style prescription from Ref. [77]. In the conversion to the branching fraction $\mathcal{B}(\mathrm{t}\to\mathrm{b}\ell\mathrm{N})$, the $\mathrm{t}\overline{\mathrm{t}}$ production cross section at NNLO+NNLL QCD of 833.9 pb is used, and the signal decay rates are calculated at LO. The observed and expected limits on $\mathcal{B}(\mathrm{t}\to\mathrm{b}\ell\mathrm{N})$ for the $m_{\mathrm{N}}$ values considered in the search are shown in Figure 13. The expected upper limits on $\mathcal{B}(\mathrm{t}\to\mathrm{b}\ell\mathrm{N})$ vary from $[7.1\ (3.4)]\times10^{-6}$ to $[3.0\ (1.3)]\times10^{-5}$ in the dielectron (dimuon) channel. The observed upper limits vary from $[11\ (3.7)]\times10^{-6}$ to $[4.8\ (2.4)]\times10^{-5}$ in the dielectron (dimuon) channel, and the results are consistent with the background-only hypothesis. The upper limits increase rapidly when the $m_{\mathrm{N}}$ value approaches the $m_{\mathrm{W}}$ value because of the vanishing lepton signal acceptance and the challenging signal classification. For this reason, we do not present an interpolation of the measured limits in the $m_{\mathrm{N}}$ range [75] [85] GeV. The decreasing lepton acceptance is also responsible for the increase in the expected limits shown for $m_{\mathrm{N}}<50$ GeV. The upper limits on the branching fraction for the electron neutrino mixing scenario are larger than those for the muon neutrino mixing scenario by approximately a factor of two. This originates from the lower trigger acceptance and ID efficiency of electrons compared to those of muons.

For a straightforward comparison with earlier type-I seesaw model search results, the upper limits on the $\mathcal{B}(\mathrm{t}\to\mathrm{b}\ell\mathrm{N})$ are converted into the upper limits on the model parameters, $\lvert V_{\mathrm{eN}}\rvert^{2}$ and $\lvert V_{\mu\mathrm{N}}\rvert^{2}$, as shown in Figure 14. The expected upper limits on $\lvert V_{\mathrm{eN}}\rvert^{2}$ ($\lvert V_{\mu\mathrm{N}}\rvert^{2}$) vary between $[12\ (5.6)]\times10^{-5}$ and $[9.9\ (4.3)]\times10^{-2}$. The observed upper limits on $\lvert V_{\mathrm{eN}}\rvert^{2}$ ($\lvert V_{\mu\mathrm{N}}\rvert^{2}$) vary between

between $[16\ (4.2)] \times10^{-5}$ and $[11\ (3.9)] \times10^{-2}$. The existing results from the direct search channels in CMS and from the ATLAS search for HNLs produced in top quark decays are compared with those from this search. The expected results in this search are comparable to the existing results from the dilepton channel and weaker by approximately a factor of ten than those from the trilepton channel for $m_N < m_W$. At higher mass values, this search becomes less sensitive compared to results from the direct search channels. This is because the signal decay process, $t \to b\ell N$, is no longer mediated by an on-shell $W$ boson, i.e., $t \to bW$ and $W \to\ell N$, meaning the corresponding partial decay width, $\Gamma(t \to b\ell N)$, is significantly reduced. For $20 \leq m_N \leq75\ \mathrm{GeV}$, the expected limits on $|V_{eN}|^2$ and $|V_{\mu N}|^2$ from this search are stronger by approximately a factor of two than those from the ATLAS search for HNLs produced in top quark decays [20]. The size of the difference varies with $m_N$.

Summary

A search for heavy neutral leptons (HNLs) produced in top quark decays in proton-proton collisions at $\sqrt{s} = 13\ \mathrm{TeV}$, corresponding to an integrated luminosity of $138\ \mathrm{fb}^{-1}$ collected with the CMS detector at the LHC, has been presented. The analysis targets HNL masses between 20 and 100 GeV in the type-I seesaw framework. Events are selected by requiring jets and a pair of same-sign, same-flavor leptons. A multivariate classifier is used to enhance the sensitivity of the analysis. No significant excess over the background prediction is observed. Observed (expected) upper limits at 95% confidence level are set on the branching fraction of the top quark decay into an HNL, a charged lepton, and a bottom quark, varying between $[1.1\ (0.71)] \times10^{-5}$ and $[4.8\ (3.0)] \times10^{-5}$ in the dielectron channel, and between $[3.7\ (3.4)] \times10^{-6}$ and $[2.4\ (1.3)] \times10^{-5}$ in the dimuon channel. The results are also interpreted as upper limits on the squared mixing amplitudes between the HNL and the standard model electron and muon neutrinos, denoted $|V_{eN}|^2$ and $|V_{\mu N}|^2$, respectively. The observed upper limits range from $16 \times10^{-5}$ to $11 \times10^{-2}$ for $|V_{eN}|^2$ and from $4.2 \times10^{-5}$ to $3.9 \times10^{-2}$ for $|V_{\mu N}|^2$.

Acknowledgments

We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid and other centres 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, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMFWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG and PSG, TARISTU24-TK10 and MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMFTR, DFG, and HGF (Germany); GSRI (Greece); MATE and NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIT and NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE, NSC, and NAWA (Poland); FCT (Portugal); MESTD (Serbia); MICIU/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); NSTC (Taiwan); MHESI (Thailand); TUBITAK and TENMAK (Türkiye); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).

Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, 101001205, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA) and Fonds voor Wetenschappelijk Onderzoek contract No. 1228724N (Belgium); the Beijing Municipal Science & Technology Commission, No. Z191100007219010, the Fundamental Research Funds for the Central Universities, the Ministry of Science and Technology of China under Grant No. 2023YFA1605804, the Natural Science Foundation of China under Grant No. 12535004, and USTC Research Funds of the Double First-Class Initiative No. YD2030002017 (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation (Georgia); the Deutsche Forschungsgemeinschaft (DFG), among others, under Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and under project number 400140256 - GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program - ÚNKP, the NKFIH research grants K 131991, K 138136, K 143460, K 143477, K 147557, K 146913, K 146914, K 147048, TKP2021-NKTA-64, 2026-4.1.1-MISSZIÓK-2026-00005 and 2025-1.1.5-NEMZ_KI-2025-00004, and MATE KKP and KKPcs Research Excellence and Flagship Research Groups grants (Hungary); the Council of Science and Industrial Research, India; ICSC – National Research Centre for High Performance Computing, Big Data and Quantum Computing, FAIR – Future Artificial Intelligence Research, and CUP I53D23001070006 (Mission 4 Component 1), funded by the NextGenerationEU program, the Italian Ministry of University and Research (MUR) under Bando PRIN 2022 – CUP I53C24002390006, PRIN PRIMULA 2022RBYK7T (Italy); the Latvian Council of Science; the Ministry of Science and Higher Education, project no. 2022/WK/14, and the National Science Centre, contracts Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552, 2023/49/B/ST2/03273, and the NAWA contract BPN/PPO/2021/1/00011 (Poland); the Fundaçao para a Ciência e a Tecnologia (Portugal); the National Priorities Research Program by Qatar National Research Fund; MICIU/AEI/10.13039/501100011033, ERDF/EU, “European Union NextGenerationEU/PRTR”, projects PID2022-142604OB-C21, PID2022-139519OB-C21, PID2023-147706NB-I00, PID2023-148896NB-I00, PID2023-146983NB-I00, PID2023-147115NB-I00, PID2023-148418NB-C41, PID2023-148418NB-C42, PID2023-148418NB-C43, PID2023-148418NB-C44, PID2024-158190NB-C22, RYC2021-033305-I, RYC2024-048719-I, CNS2023-144781, CNS2024-154769 and Plan de Ciencia, Tecnología e Innovación de Asturias, Spain; the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, the National Science, Research and Innovation Fund program IND FF 68 369 2300 097, and the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B39G680009 (Thailand); the Eric & Wendy Schmidt Fund for Strategic Innovation through the CERN Next Generation Triggers project under grant agreement number SIF-2023-004; the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

Data availability

Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS data preservation, re-use and open access policy.

The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia

A. Gevorgyan, A. Hayrapetyan, V. Makarenko, A. Tumasyan¹

Marietta Blau Institute for Particle Physics, Vienna, Austria

W. Adam, L. Benato, T. Bergauer, M. Dragicevic, S. Gundacker, A.K. Guven, P.S. Hussain, M. Jeitler², N. Krammer, A. Li, D. Liko, M. Matthewman, J. Schieck², R. Schöfbeck², M. Shooshtari, M. Sonawane, N. Van Den Bossche, W. Waltenberger, C.E. Wulz²

Universiteit Antwerpen, Antwerpen, Belgium

T. Janssen, D. Ocampo Henao, T. Van Laer, P. Van Mechelen

Vrije Universiteit Brussel, Brussel, Belgium

D. Ahmadi, J. Bierkens, N. Breugelmans, S. Dansana, A. De Moor, M. Delcourt, S. Duponcheel, C. Gupta, F. Heyen, Y. Hong, K. Kang, P. Kashko, S. Lowette, I. Makarenko, S. Nandakumar, J. Niedziela, S. Tavernier, M. Tytgat³, G.P. Van Onsem, S. Van Putte, T. Wybouw

Université Libre de Bruxelles, Bruxelles, Belgium

A. Beshr, B. Bilin, F. Caviglia Roman, B. Clerbaux, A.K. Das, I. De Bruyn, G. De Lentdecker, E. Ducarme, H. Evard, L. Favart, I. Kalaitzidou, A. Khalilzadeh, A. Malara, A. Potrebko, M.A. Shahzad, L. Thomas, M. Vanden Bemden, C. Vander Velde, P. Vanlaer, C. Yuan, F. Zhang

Ghent University, Ghent, Belgium

A. Cauwels, M. De Coen, D. Dobur⁴, C. Giordano, G. Gokbulut, K. Kaspar, D. Kavtaradze, D. Marckx, A. Mehta, K. Skovpen, A.M. Tomaru, J. van der Linden, J. Vandenbroeck

Université Catholique de Louvain, Louvain-la-Neuve, Belgium

H. Aarup Petersen, A. Benecke, A. Bethani, G. Bruno, A. Cappati, J. De Favereau De Jeneret, C. Delaere, F. Gameiro Casalinho, A. Giammanco, A.O. Guzel, M. Hussain, Z. Lawrence, V. Lemaitre, J. Lidrych, P. Malek, S. Turkcapar

Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil

G. Alves, E. Coelho, M.V. Gonçalves Sales, C. Hensel, D. Matos Figueiredo, T. Menezes De Oliveira, C. Mora Herrera, P. Rebello Teles, M. Soeiro, E.J. Tonelli Manganote⁵, A. Vilela Pereira

Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil

W.L. Aldá Júnior, M. Barroso Ferreira Filho, H. Brandao Malbouisson, W. Carvalho, J. Chinellato⁶, G. Correia Silva, M. Costa Reis, E.M. Da Costa, D. Da Silva Dalto, G.G. Da Silveira⁷, D. De Jesus Damiao, S. Fonseca De Souza, R. Gomes De Souza, S. Jesus, T. Laux Kuhn⁷, K. Maslova Gioseffi Defante, K. Mota Amarilo, L. Mundim, H. Nogima, J.P. Pinheiro, A. Santoro, A. Sznajder, M. Thiel, F. Torres Da Silva De Araujo⁸, D. Torres Machado

Universidade Estadual Paulista (a), Universidade Federal do ABC (b), São Paulo, Brazil

C.A. Bernardes, L. Calligaris, J. Carvalho Leite, F. Damas, E. De Moraes Gregores, B. Lopes Da Costa, I. Maietto Silverio, P.G. Mercandante, S.F. Novaes, S. Padula, M. Pereira Coelho, V. Scheurer, T. Tomei

Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia, Bulgaria

A. Aleksandrov, G. Antchev, P. Danev, R. Hadjiiska, P. Iaydjiev, M. Shopova, G. Sultanov

University of Sofia, Sofia, Bulgaria

A. Dimitrov, L. Litov, B. Pavlov, P. Petkov, A. Petrov

Instituto de Alta Investigación, Universidad de Tarapacá, Arica, Chile

S. Keshri, D.N. Laroze Navarrete, M. Meena, S. Thakur

Universidad Técnica Federico Santa María, Valparaiso, Chile

W. Brooks

Beihang University, Beijing, China

T. Cheng, L. Tan, L. Wang, L. Yuan

Department of Physics, Tsinghua University, Beijing, China

J. Gu, Z. Hu, Z. Liang, J. Liu, Y. Wang, H. Yang, S. Zhang, Y. Zhao

Institute of High Energy Physics, Beijing, China

N. Bi{sup}9, G.M. Chen{sup}9, H.S. Chen{sup}9, M. Chen{sup}9, Y. Chen, B. Hou{sup}9, Q. Hou, F. Iemmi, C.H. Jiang, P.z. Lai, H. Liao, G. Liu, Z. Liu{sup}10, S. Song{sup}9, J. Tao, C. Wang{sup}9, J. Wang, A. Zada{sup}9`, H. Zhang, J. Zhao

State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China

Y. Ban, A. Carvalho Antunes De Oliveira, S. Deng, X. Geng, B. Guo, Q. Guo, Z. He, C. Jiang, A. Levin, C. Li, L. Li, Q. Li, Y. Mao, S. Qian, S.J. Qian, X. Qin, C. Quaranta, X. Sun, D. Wang, J. Wang, T. Yang, M. Zhang, M. Zhang, Y. Zhao, C. Zhou

State Key Laboratory of Nuclear Physics and Technology, Institute of Quantum Matter, South China Normal University, Guangzhou, China, Guangzhou, China

X. Hua, S. Yang

Sun Yat-Sen University, Guangzhou, China

Z. You

University of Science and Technology of China, Hefei, China

N. Lu

Nanjing Normal University, Nanjing, China

G. Bauer{sup}11,12, L. Chen, Z. Cui{sup}12, B. Li{sup}13, H. Wang, X. Wang, K. Yi{sup}14, J. Zhang, F. Zhu

Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao, China

C. Li

Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University, Shanghai, China

Y. Li, Z. Wang, Y. Zhou{sup}15`

Zhejiang University - Department of Physics, Zhejiang, China

Z. Lin, C. Lu, M. Xiao{sup}16`

Universidad de Los Andes, Bogota, Colombia

C. Avila, A. Cabrera, C. Florez, J.A. Reyes Vega

Universidad de Antioquia, Medellin, Colombia C. Rendón, M. Rodriguez, A.A. Ruales Barbosa, J.D. Ruiz Alvarez

University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia

N. Godinovic, D. Lelas, I. Puljak, A. Sculac

University of Split, Faculty of Science, Split, Croatia

M. Kovac, A. Petkovic, T. Sculac

Institute Rudjer Boskovic, Zagreb, Croatia

P. Bargassa, V. Brigljevic, D. Ferencek, K. Jakovcic, A. Starodumov, T. Susa

University of Cyprus, Nicosia, Cyprus

A. Attikis, S. Konstantinou, C. Leonidou, L. Paizanos, F. Ptochos, P.A. Razis, H. Saka, A. Stepennov

Charles University, Prague, Czech Republic

M. Finger Jr., A. Kveton

Escuela Politecnica Nacional, Quito, Ecuador

E. Acurio

Universidad San Francisco de Quito, Quito, Ecuador

E. Carrera Jarrin

Academy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt

H. Abdalla{sup}17, Y. Assran{sup}18,19, B. El-mahdy{sup}17`

Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt

A. Lotfy, M. Mahmoud, H. Mohammed, Y. Mohammed

National Institute of Chemical Physics and Biophysics, Tallinn, Estonia

K. Jaffel, M. Kadastik, T. Lange, C. Nielsen, J. Pata, M. Raidal, N. Seeba, L. Tani

Department of Physics, University of Helsinki, Helsinki, Finland

E. Brü̈cken, A. Milieva, K. Osterberg, M. Voutilainen

Helsinki Institute of Physics, Helsinki, Finland

F.I. Garcia Fuentes, T. Hilden, P. Inkaew, K.T.S. Kallonen, R. Kumar Verma, T. Lampén, K. Lassila-Perini, B. Lehtela, S. Lehti, T. Lindén, N.R. Mancilla Xinto, M. Myllymäki, M.m. Rantanen, S. Saariokari, N.T. Toikka, J. Tuominiemi, E. Veikkola

Lappeenranta-Lahti University of Technology, Lappeenranta, Finland

N. Bin Norjoharuddeen, H. Kirschenmann, P.R. Luukka, H. Petrow

IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France

M. Besancon, F. Couderc, M. Dejardin, D. Denegri, P. Devouge, J.L. Faure, F. Ferri, P. Gaigne, S. Ganjour, P. Gras, F. Guilloux, G. Hamel de Monchenault, M. Kumar, V. Lohezic, Y. Maidannyk, J. Malcles, F. Orlandi, L. Portales, S. Ronchi, M.Ö. Sahin, P. Simkina, M. Titov

Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France

R. Amella Ranz, F. Beaudette, K. Biriukov, P. Busson, F. Cetorelli, C. Charlot, M. Chiusi, T.D. Cuisset, O. Davignon, A. De Wit, T. Debnath, I.T. Ehle, S. Ghosh, A. Gilbert, R. Granier de Cassagnac, M. Manoni, M. Nguyen, S. Obraztsov, C. Ochando, L.m. Rabour, R. Salerno, J.B. Sauvan, Y. Sirois, G. Sokmen, Y. Song, L. Urda Gómez, B. Voirin, A. Zabi, A. Zghiche

Institut Pluridisciplinaire Hubert Curien (IPHC), Université de Strasbourg, CNRS/IN2P3, Strasbourg, France

J.L. Agram²⁰, J. Andrea, D. Bloch, E.C. Chabert, C. Collard, G. Coulon, C. Eschenlauer, S. Falke, U. Goerlach, A.C. Le Bihan, G. Saha, A. Savoy-Navarro²¹, P. Vaucelle

Institut de Physique des 2 Infinis de Lyon (IP2I), Villeurbanne, France

D. Amram, S. Beauceron, B. Blancon, G. Boudoul, N. Chanon, D. Contardo, J. Daniel, P. Depasse, H. El Mamouni, J. Fay, E. Fillaudeau, S. Gascon, M. Gouzevitch, C. Greenberg, B. Ille, E. Jourd’Huy, M. Lethuillier, K. Long, B. Massoteau, L. Mirabito, A. Purohit, M. Vander Donckt, C. Verollet

Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne, France

A. Di Florio, G. Mauceri, B. Orzari

Georgian Technical University, Tbilisi, Georgia

A. Khvedelidze²², I. Lomidze, Z. Tsamalaidze²²

RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany

K.F. Adamowicz, V. Botta, S. Consuegra Rodríguez, L. Feld, K. Klein, M. Lipinski, P. Nattland, V. Oppenländer, A. Pauls, D. Pérez Adán

RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany

C. Daumann, S. Diekmann, E. Ehlert, N. Eich, D. Eliseev, F. Engelke, J. Erdmann, M. Erdmann, M.Z. Farkas, B. Fischer, T. Hebbeker, K. Hoepfner, A. Jung, N. Kumar, F. Mausolf, M. Merschmeyer, A. Meyer, A. Pozdnyakov, H. Reithler, U. Sarkar, V. Sarkisovi, A. Schmidt, J.G. Schulz, C. Seth, A. Sharma, J.L. Spah, V. Vaulin, U. Willemsen, S. Zaleski, F.P. Zinn

RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany

M.R. Beckers, G. Flügge, N. Hoeflich, T. Kress, A. Nowack, O. Pooth, A. Stahl

University of Hamburg, Hamburg, Germany

A.R. Alves Andrade, M. Antonello, S. Bollweg, M. Bonanomi, L. Ebeling, K. El Morabit, Y. Fischer, M. Frahm, P.P. Gadow, E. Garutti, A. Grohsjean, A.A. Guvenli, J. Haller, D. Hundhausen, M. Jalalvandi, G. Kasieczka, P. Keicher, R. Klanner, W. Korcari, T. Kramer, C.c. Kuo, J. Lange, M.y. Lee, A. Lobanov, J. Matthiesen, L. Moureaux, K. Nikolopoulos, K.J. Pena Rodriguez, N. Prouvost, B. Raciti, M. Rieger, D. Savoiu, P. Schleper, M. Schröder, J. Schwandt, M. Sommerhalder, H. Stadie, G. Steinbrück, J. Sun, T. von Schwartz, R. Ward, B. Wiederspan, M. Wolf, C. Yede

Deutsches Elektronen-Synchrotron, Hamburg, Germany

A. Abel, A. Akhkil, M. Aldaya Martin, J. Alimena, Y. An, I. Andreyev, J. Bach, S. Baxter, H. Becerril Gonzalez, O. Behnke, F. Blekman²³, K. Borras²⁴, L. Braga Da Rosa, A. Campbell, C. Cazzaniga, S. Chatterjee, L.X. Coll Saravia, G. Eckerlin, D. Eckstein, E. Gallo²³, A. Geiser, M. Guthoff, A. Hinzmann, U. Husemann, M. Kasemann, C. Kleinwort, R. Kogler, M. Komm, D. Krücker, F. Labe, W. Lange, D. Leyva Pernia, J.h. Li, K.y. Lin, K. Lipka²⁵, W. Lohmann²⁶, J. Malvaso, R. Mankel, I.A. Melzer-Pellmann, M. Mendizabal Morentin, A.B. Meyer, G. Milella, M.N.J. Momed, K. Moral Figueroa, A. Mussgiller, L.P. Nair, A. Nürnberg, J. Park, F. Preau, E. Ranken, A. Raspereza, D. Rastorguev, L. Rygaard, M. Scham^27,24, C. Schwanenberger²³, D. Schwarz, P. Schütze, D. Selivanova, K. Sharko, M. Shchedrolosiev, A. Sritharan, D. Stafford, M. Torkian, S. Vashishtha, R. Walsh, D. Wang, Q. Wang, K. Wichmann, C. Wissing, S. Zakharov, A. Zimmermane Castro Santos

Institut für Experimentelle Teilchenphysik, Karlsruhe, Germany

J. Ahäuser, A. Brusamolino, E. Butz, Y.M. Chen, T. Chwalek, A. Dierlamm, G.G. Dincer, U. Elicabuk, N. Faltermann, M. Giffels, A. Gottmann, F. Hartmann²⁸, F. Hummer, J. Kieseler, M. Klute, H.A. Krause, R. Kunnilan Muhammed Rafeek, O. Lavoryk, J.M. Lawhorn, S. Maier, N. Meenamthuruthil Radhakrishnan, T. Mehner, M. Molch, A.A. Monsch, M. Mormile, T. Müller, M. Presilla, G. Quast, K. Rabbertz, B. Regnery, R. Schmieder, T. Selezneva, N. Shadskiy, L. Sowa, L. Stockmeier, M. Toms, B. Topko, N. Trevisani, C. Verstege, T. Voigtländer, R.F. Von Cube, J. Von Den Driesch, J.H. Voss, C. Winter, R. Wolf, W.D. Zeuner, X. Zuo

Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece

G. Anagnostou, G. Daskalakis, A. Kyriakis

National and Kapodistrian University of Athens, Athens, Greece

P. Iosifidou, P. Katris, M. Kotsarini, G. Melachroinos, Z. Painesıs, N. Plastiras, N. Saoulidou, K. Theofilatos, E. Tzovara, K. Vellidis, I. Zisopoulos

National Technical University of Athens, Athens, Greece

T. Chatzistavrou, G. Karapostoli, K. Kousouris, K. Paschos, L.P. Rouseliotaki, E. Siamarkou, A. Taxeidi, G. Tsipolitis

University of Ioánnina, Ioánnina, Greece

I. Evangelou, C. Foudas, P. Katsoulis, P. Kokkas, P.G. Kosmoglou Kioseoglou, N. Manthos, I. Papadopoulos, J. Strologas

Department of Physics, School of Sciences Democritus, University of Thrace, Kavala, Greece

E. Tziaferi

HUN-REN Wigner Research Centre for Physics, Budapest, Hungary

C. Hajdu, D. Horvath^29,30, Á. Kadlecsik, C. Lee, K. Márton, A.J. Rád³¹, F. Sikler, V. Veszpremi

MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary

G. Balint, M. Csanad, K. Farkas, A. Fehérkuti³², M.M.A. Gadallah³³, M. León Coello, G. Pasztor, G.I. Veres

Faculty of Informatics, University of Debrecen, Debrecen, Hungary, Debrecen, Hungary

B. Ujvari, G. Zilizi

HUN-REN ATOMKI - Institute of Nuclear Research, Debrecen, Hungary

G. Bencze, S. Czellar, J. Molnar, Z. Szillasi

Karoly Robert Campus, MATE Institute of Technology, Gyongyos, Hungary

T.F. Csorgo³², F. Nemes³², T. Novak, I. Szanyi³⁴ Indian Institute of Science (IISC), Bangalore, India J.R. Komaragiri

Indian Institute of Technology Bhubaneswar, Bhubaneswar, India S. Bahinipati, R. Raturi

Panjab University, Chandigarh, India S. Bansal, V. Bhatnagar, B. Chauhan, S. Chauhan, N. Dhingra³⁵, A. Kaur, H. Kaur, S. Kumar, T. Sheokand, A. Singla, K. Verma

University of Delhi, Delhi, India A. Bhardwaj, A. Chhetri, B.C. Choudhary, A. Kumar, A. Kumar, M. Naimuddin, S. Phor, C. Prakash, K. Ranjan, M.K. Saini

Indian Institute of Technology Mandi (IIT-Mandi), Himachal Pradesh, India M. Kumari, N. Neeraj, P. Palni, S. Rana, A. Rathore, A. Sarkar

University of Hyderabad, Hyderabad, India S. Acharya³⁶, B. Gomber, S.K. Satapathy

Indian Institute of Technology Kanpur, Kanpur, India S. Mukherjee

Saha Institute of Nuclear Physics, HBNI, Kolkata, India S. Bhattacharya, S. Das Gupta, S. Dutta, S. Dutta, S. Sarkar

Indian Institute of Technology Madras, Madras, India M.M. Ameen, P.K. Behera, S. Chatterjee, G. Dash, A. Dattamunsi, P. Jana, P. Kalbhor, S. Kamble, P.R. Pujahari, A.K. Sikdar, R.K. Singh, A. Swain, P. Verma, S. Verma, A. Vijay

Indian Institute of Science Education and Research Mohali, Mohali, India A. Chauhan, S. Nayak, H. Rajpoot, B.K. Sirasva

Tata Institute of Fundamental Research-A, Mumbai, India L. Bhatt, S. Dugad, T. Mishra, G.B. Mohanty, M. Shelake, P. Suryadevara

Tata Institute of Fundamental Research-B, Mumbai, India A. Bala, S. Banerjee, S. Barman³⁷, R.M. Chatterjee, J. Chhikara, M. Guchait, S. Jain, A. Jaiswal, S. Kumar, M. Maity³⁷, G. Majumder, K. Mazumdar, L. Panwar³⁸, R. Pramanik, R. Saxena, P. Sharma, A. Thachayath

National Institute of Science Education and Research, Jatni, Khorda, Odisha 752050, India Homi Bhabha National Institute, Training School Complex, Anushakti Nagar, Mumbai 400094, India, Odisha, India R. Kumar Agrawal, D. Maity³⁹, P. Mal, K. Naskar³⁹, A. Nayak³⁹, K. Pal, P. Sadangi, S. Shuchi, S.K. Swain, S. Varghese³⁹, D. Vats³⁹

Indian Institute of Science Education and Research (IISER), Pune, India S. Dube, P. Hazarika, A. Laha, R. Sharma, S. Sharma, K.Y. Vaish

Indian Institute of Technology Hyderabad, Telangana, India C. Agrawal, B. Babu, S. Ghosh

Isfahan University of Technology, Isfahan, Iran H. Bakhshiansohi⁴⁰, A. Jafari⁴¹, V. Sedighzadeh Dalavi, M. Zeinali⁴² Institute for Research in Fundamental Sciences (IPM), Tehran, Iran S. Bashiri, S. Chenarani⁴³, S.M. Etesami, Y. Hosseini, M. Khakzad, E. Khazaie, M. Mohammadi Najafabadi, M. Nourbakhsh, S. Tizchang⁴⁴

University College Dublin, Dublin, Ireland M. Felcini, M. Grunewald

INFN Sezione di Bari$^{a}$, Università di Bari$^{b}$, Politecnico di Bari$^{c}$, Bari, Italy M. Abbrescia$^{a,b}$, M. Buonsante$^{a,b}$, A. Colaleo$^{a,b}$, D. Creanza$^{a,c}$, N. De Filippis$^{a,c}$, M. De Palma$^{a,b}$, W. Elmetenawee$^{a,b}$⁴⁵, N. Ferrara$^{a,c}$, L. Fiore$^{a}$, L. Generoso$^{a,b}$, L. Longo$^{a}$, M. Louka$^{a,b}$, G. Maggi$^{a,c}$, M. Maggi$^{a}$, S. My$^{a,b}$, F. Nenna$^{a,b}$, S. Nuzzo$^{a,b}$, A. Pellecchia$^{a,b}$, A. Pompili$^{a,b}$, F.M. Procacci$^{a,b}$, G. Pugliese$^{a,c}$, R. Radogna$^{a,b}$, D. Ramos$^{a}$, A. Ranieri$^{a}$, L. Silvestris$^{a}$, F.M. Simone$^{a,b}$, A. Stamerra$^{a,b}$, Ü. Sözbilir$^{a}$⁴⁶, F. Tenchini$^{a,b}$, D. Troiano$^{a,b}$, R. Venditti$^{a,b}$, P. Verwilligen$^{a}$, A. Zaza$^{a,b}$

INFN Sezione di Bologna$^{a}$, Università di Bologna$^{b}$, Bologna, Italy G. Abbiendi$^{a}$, S. Balducci$^{a,b}$, C. Battilana$^{a,b}$, D. Bonacorsi$^{a,b}$, P. Capiluppi$^{a,b}$, M. Cruciani$^{a,b}$, M. Cuffiani$^{a,b}$, G.M. Dallavalle$^{a}$, T. Diotalevi$^{a,b}$, F. Fabbri$^{a}$, A. Fanfani$^{a,b}$, R. Farinelli$^{a}$, D. Fasanella$^{a}$, L. Ferragina$^{a,b}$, P. Giacomelli$^{a}$, C. Grandi$^{a}$, L. Guiducci$^{a,b}$, M. Lorusso$^{a,b}$, L. Lunerti$^{a}$, S. Marcellini$^{a}$, G. Masetti$^{a}$, F. Navarria$^{a,b}$, G. Paggi$^{a,b}$, A. Perrotta$^{a}$, A. Rossi$^{a,b}$, S. Rossi Tisbeni$^{a,b}$, T. Rovelli$^{a,b}$

INFN Sezione di Catania$^{a}$, Università di Catania$^{b}$, Catania, Italy S. Costa$^{a,b}$⁴⁷, A. Di Mattia$^{a}$, A. Lapertosa$^{a}$, R. Potenza$^{a,b}$, A. Tricomia,b⁴⁷

INFN Sezione di Firenze$^{a}$, Università di Firenze$^{b}$, Firenze, Italy J. Altork$^{a,b}$, G. Barbagli$^{a}$, A. Calandra$^{a,b}$, B. Camaiani$^{a,b}$, A. Cassese$^{a}$, R. Ceccarelli$^{a}$, V. Ciulli$^{a,b}$, C. Civinin$^{a}$, R. D’Alessandro$^{a,b}$, L. Damenti$^{a,b}$, E. Focardi$^{a,b}$, T. Kello$^{a}$, G. Latino$^{a,b}$, P. Lenzi$^{a,b}$, M. Lizzo$^{a}$, M. Meschini$^{a}$, S. Paoletti$^{a}$, A. Papanastassiou$^{a,b}$, G. Sguazzoni$^{a}$, L. Viliani$^{a}$

INFN Laboratori Nazionali di Frascati, Frascati, Italy L. Benussi, S. Colafranceschi⁴⁸, S. Meola⁴⁹, D. Piccolo

INFN Sezione di Genova$^{a}$, Università di Genova$^{b}$, Genova, Italy M. Alves Gallo Pereira$^{a}$, F. Ferro$^{a}$, E. Robutti$^{a}$, S. Tosi$^{a,b}$

INFN Sezione di Milano-Bicocca$^{a}$, Università di Milano-Bicocca, Milano$^{b}$, Milano-Bicocca, Italy A. Benaglia$^{a}$, F. Brivio$^{a}$, V. Camagni$^{a,b}$, F. De Guio$^{a,b}$, M.E. Dinardo$^{a,b}$, P. Dini$^{a}$, S. Gennaia$^{a}$, R. Gerosa$^{a,b}$, A. Ghezzia$^{a,b}$, P. Govoni$^{a,b}$, L. Guzzi$^{a}$, G. Lavizzaria,b, M.T. Lucchini$^{a,b}$, M. Malbertia, S. Malvezzia, A. Massironia, L. Moronia, M. Paganonia,b, S. Palluottoa,b, D. Pedrinia, A. Peregoa,b, T. Tabarelli de Fatis$^{a,b}$

INFN Sezione di Napoli$^{a}$, Università di Napoli “Federico II”$^{b}$, Università della Basilicata (Potenza)$^{c}$, Scuola Superiore Meridionale (SSM)$^{d}$, Napoli, Italy S. Buontempo$^{a}$, F. Confortini$^{a,b}$, C. Di Fraia$^{a,b}$, F. Fabozzi$^{a,c}$, A.O.M. Iorio$^{a,b}$, L. Lista$^{a,b}$⁵⁰, P. Paolucci$^{a}$²⁸, B. Rossi$^{a}$

INFN Sezione di Padova$^{a}$, Università di Padova$^{b}$, Università degli Studi di Cagliari$^{c}$, Padova, Italy P. Azzi$^{a}$⁵¹, N. Bacchetta$^{a}$, D. Bisello$^{a,b}$, L. Borella$^{a}$, P. Bortignon$^{a,c}$ G. Bortolato^a,b, A.C.M. Bulla^a,c, R. Carlin^a,b, P. Checchia^a, T. Dorigo^a,52, F. Gasparini^a,b, U. Gasparini^a,b, P. Grutta^a, N. Lai^a, E. Lusiani^a, M. Margoni^a,b, A.T. Meneguzzo^a,b, M. Missiroli^a, J. Pazzini^a,b, F. Primavera^a,b, P. Ronchese^a,b, R. Rossin^a,b, F. Simonetto^a,b, M. Toffano^a, M. Tosi^a,b, A. Triossi^a,b, S. Ventura^a, M. Zanetti^a,b, P. Zotto^a,b, A. Zucchetta^a,b, G. Zumerle^a,b

INFN Sezione di Pavia^a, Università di Pavia^b, Pavia, Italy

S. A. AbuZeid^a,53, C. Aimè^a, A. Braghieri^a, M. Brunoldi^a,b, P. Montagna^a,b, M. Pelliccioni^a,b, V. Re^a, C. Riccardi^a,b, P. Salvini^a, I. Vaia^a,b, P. Vitulo^a,b

INFN Sezione di Perugia^a, Università di Perugia^b, Perugia, Italy

S. Ajmal^a,b, M.E. Ascioti^a,b, G.M. Bilei^a, W.D. Buitrago Ceballos^a,b, C. Carrivale^a,b, D. Ciangottini^a,b, L. Della Penna^a,b, L. Fanò^a,b, V. Mariani^a,b, M. Menichelli^a, F. Moscatelli^a,54, F. Napolitano^a, A. Rossi^a,b, A. Santocchia^a,b, D. Spiga^a, T. Tedeschi^a,b

INFN Sezione di Pisa^a, Università di Pisa^b, Scuola Normale Superiore di Pisa^c, Università di Siena^d, Pisa, Italy

C.A. Alexe^a,c, P. Asenova^a,b, P. Azzurri^a, G. Bagliesi^a, L. Bianchini^a,b, T. Boccalai^a, E. Bossini^a, D. Bruschini^a,c, R. Castaldi^a, F. Cattafesta^a,c, M.A. Cioccia^d, M. Cipriani^a,b, R. Dell’Orso^a, S. Dhania^a,d, S. Donato^a,b, A. Feliziani^a,d, R. Forti^a,b, A. Giassi^a, F. Ligabue^a,c, A.C. Marini^a,b, A. Messineo^a,b, S. Mishra^a, V.K. Muraleedharan Nair Bindhu^a,b, S. Nandana^a, F. Palla^a, M. Riggirello^a,c, A. Rizzi^a,b, G. Rolandi^a,c, A. Scribano^a, P. Solanki^a,b, P. Spagnolo^a, R. Tenchini^a, G. Tonelli^a,b, N. Turini^a,d, F. Vaselli^a,c, A. Venturi^a, P.G. Verdini^a

INFN Sezione di Roma^a, Sapienza Università di Roma^b, Roma, Italy

P. Akrap^a,b, S.C. Behera^a, F. Cavallari^a, L. Cunqueiro Mendez^a,b, F. De Riggi^a,b, D. Del Re^a,b, M. Del Vecchio^a,b, E. Di Marco^a, M. Diemoz^a, F. Errico^a, L. Frosina^a,b, R. Gargiulo^a,b, B. Harikrishnan^a,b, F. Lombardi^a,b, L. Martikainen^a,b, G. Organtini^a,b, N. Palmeri^a,b, R. Paramatti^a,b, T. Pauletto^a,b, S. Rahatlou^a,b, C. Rovelli^a, F. Santanastasio^a,b, L. Soffi^a, V. Vladimirov^a,b

INFN Sezione di Torino^a, Università di Torino^b, Università del Piemonte Orientale (Novara)^c, Torino, Italy

N. Amapane^a,b, R. Arcidiacono^a,c, S. Argiro^a,b, M. Arneodo^a,c, N. Bartosik^a,c, F. Bashir^a,b, R. Bellan^a,b, A. Bellora^a,b, C. Biino^a, C. Borca^a,b, L. Bulaja^a,b, N. Cartiglia^a, M. Costa^a,b, R. Covarelli^a,b, N. Demaria^a, E. Ferrando^a,b, L. Finco^a, M. Grippo^a,b, B. Kiani^a,b, L. Lanteri^a,b, F. Luongo^a,b, C. Mariotti^a,55, S. Maselli^a, A. Mecca^a,b, L. Menzio^a,b, P. Meridiani^a, E. Migliore^a,b, M. Monteno^a, M.M. Obertino^a,b, G. Ortona^a, L. Pacher^a,b, N. Pastrone^a, M. Ruspa^a,c, F. Siviero^a,b, V. Sola^a,b, A. Solano^a,b, A. Staiano^a, C. Tarricone^a,b, M. Tornago^a,b, D. Trocino^a, G. Umoret^a,b, E. Vlasov^b, R. White^a,b

INFN Sezione di Trieste^a, Università di Trieste^b, Trieste, Italy

J. Babbar^a,b,56, S. Belforte^a, V. Candelise^a,b, M. Casarsa^a, F. Cossutti^a, K. De Leo^a, G. Della Ricca^a,b, R. Delli Gatti^a,b, C. Giraldin^a,b

Kyungpook National University, Daegu, Korea

S. Dogra, J. Hong, J. Kim, J. Kim, T. Kim, D. Lee, H. Lee, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S. Sekmen, B. Tae, Y.C. Yang

Department of Mathematics and Physics - Gangneung-Wonju National University, Gangneung, Korea

M.S. Kim

Chonnam National University, Institute for Universe and Elementary Particles, Kwangju, Korea

G. Bak, P. Gwak, H. Kim, H. Lee, S. Lee, D.H. Moon, J. Seo

Department of Physics, Chung-Ang University, Seoul, Korea

K. Lee, Y. Lee

Hanyang University, Seoul, Korea

E. Asilar, F. Carnevali, J. Choi⁵⁷, T.J. Kim, Y. Ryou, J. Song, T. Yang

Korea University, Seoul, Korea

S. Ha, B.S. Hong, J. Kim, K. Lee, K. Lee, S. Lee, J. Padmanaban, B.A.N. Putra, J. Yoo

Kyung Hee University, Department of Physics, Seoul, Korea

J. Goh, J. Shin, S. Yang

Sejong University, Seoul, Korea

L. Kalipoliti, Y. Kang, H. Kim, Y. Kim, B. Ko, S. Lee

Seoul National University, Seoul, Korea

J. Choi, J. Choi, W. Jun, H. Kim, J. Kim, J. Kim, T. Kim, Y. Kim, Y.W. Kim, S. Ko, H. Lee, J. Lee, J. Lee, B.H. Oh, J. Shin, U. Yang, I. Yoon

University of Seoul, Seoul, Korea

W. Heo, W. Jang, D. Kim, S. Kim, Y. Roh, I. J. Watson

Yonsei University, Department of Physics, Seoul, Korea

S. Calzaferri, G. Cho, Y. Eo, K. Hwang, H. Jang, B. Kim, D. Kim, S. Kim, J.S.H. Lee, G. Mocellin, H.D. Yoo

Sungkyunkwan University, Suwon, Korea

Y. Lee, I. Yu

College of Engineering and Technology, American University of the Middle East (AUM), Dasman, Kuwait

T. Beyrouthy, Y. Gharbia

Kuwait University - College of Science - Department of Physics, Safat, Kuwait

F. Alazemi

Riga Technical University, Riga, Latvia

K. Dreimanis, O.M. Eberlins, A. Gaile, J.K. Heikkilä, M. Klevs, C. Munoz Diaz, D. Osite, G. Pikurs, R. Plese, M. Seidel, D. Sidiropoulos Kontos

University of Latvia (LU), Riga, Latvia

N.R. Strautnieks

Vilnius University, Vilnius, Lithuania

M. Ambrozas, A. Juodagalvis, S. Nargelas, S. Nayak, G. Tamulaitis

National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia

I. Yusuff⁵⁸, Z. Zolkapli

University of Sonora (UNISON), Hermosillo, Mexico J.P. Barajas Ibarria, J.F. Benitez, A. Castaneda Hernandez, A. Cota Rodriguez, L.E. Cuevas Picos, H.A. Encinas Acosta, L.G. Gallegos Mariñez, J.A. Murillo Quijada, L. Valencia Palomo

Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico

H. Castilla-Valdez, H. Crotte Ledesma, R. Lopez-Fernandez, J. Mejia Guisao, R. Reyes-Almanza, A. Sánchez Hernández

Universidad Iberoamericana, Mexico City, Mexico

C. Oropeza Barrera, D.L. Ramirez Guadarrama, M. Ramírez García

Benemerita Universidad Autonoma de Puebla, Puebla, Mexico

I. Bautista, F.E. Neri Huerta, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada

University of Montenegro, Podgorica, Montenegro

I. Bubanja, J. Mijuskovic, N. Raicevic

National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan

A. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan, W.A. Khan, I. Sohail

AGH University of Krakow, Krakow, Poland

Z. Abdy, V. Avati, L. Forthomme, L. Grzanka, M. Malawski, K. Piotrzkowski

National Centre for Nuclear Research, Swierk, Poland

H. Awedikian, M. Bluj, M. Ghimiray, M. Górski, M. Kazana, M. Szleper, P. Zalewski

Institute of Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland

K. Bunkowski, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, W. Matyszkiewicz, A. Muhammad, S. Slawinski

Warsaw University of Technology, Warsaw, Poland

P. Fokow, K. Pozniak, W. Zabolotny

Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa, Portugal

M. Araujo, C. Beirão Da Cruz E Silva, A. Boletti, M. Bozzo, T. Camporesi${}^{55,59}$

G. Da Molin, M. Gallinaro, R. Guitton, J. Hollar, H. Legoinha, N. Leonardo${}^{60}$, G.B. Marozzo, A. Petrilli, M. Pisano, J. Seixas, J. Varela, J.W. Wulff

Faculty of Physics, University of Belgrade, Belgrade, Serbia

P. Adzic, L. Markovic, P. Milenovic, V. Milosevic

Vinca Institute of Nuclear Science, Belgrade, Serbia

D. Devetak, M. Dordevic, J. Milosevic, L. Nadderd, V. Rekovic, M. Stojanovic

Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain

M. Alcalde Martinez, J. Alcaraz Maestre, J.A. Brochero Cifuentes, M. Cepeda, M. Cerrada, N. Colino, B. De La Cruz, A. Escalante Del Valle, C. Fernandez Bedoya, D. Fernández Del Val, J.P. Fernández Ramos, J. Flix, M.C. Fouz, C. Garcia Sanchez, M. Gonzalez Hernandez, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. Llorente Merino, O. Manzanilla, C. Martin Perez, E. Martin Viscasillas, D. Moran, C.M. Morcillo Perez, Á. Navarro Tobar, J. Puerta Pelayo, A.M. Pérez-Calero Yzquierdo, I. Redondo, D.D. Redondo Ferrero, E. Sanchez Berenguer, J. Vazquez Escobar

Universidad Autónoma de Madrid, Madrid, Spain

J.F. de Troconiz

Universidad de Oviedo, Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), Oviedo, Spain

E. Aller Gutierrez, B. Alvarez Gonzalez, J. Ayllon Torresano, A. Cardini, J. Cuevas, J. Del Riego Badas, D. Estrada Acevedo, J. Fernandez Menendez, S. Folgueras, L. Garcia Diaz, I. Gonzalez Caballero, P. Leguina, M. Obeso Menendez, E. Palencia Cortezon, J. Prado Pico, S. Sanchez Cruz, A. Soto Rodríguez, P. Vischia

Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain

S. Blanco Fernández, I.J. Cabrillo, A. Calderon, M. Caserta, J. Duarte Campderros, M. Fernandez, G. Gomez, A. Gomez Carrera, C. Lasaosa García, R. Lopez Ruiz, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras, P. Matorras Cuevas, E. Navarrete Ramos, J. Piedra Gomez, C. Quintana San Emeterio, V. Rodriguez, L. Scodellaro, I. Vila, R. Vilar Cortabitarte, J.M. Vizan Garcia

University of Colombo, Colombo, Sri Lanka

B. Kailasapathy⁶¹

University of Ruhuna, Department of Physics, Matara, Sri Lanka

W.G. Dharmaratna⁶², N. Perera

CERN, European Organization for Nuclear Research, Geneva, Switzerland

D. Abbaneo, C. Amendola, R. Ardino, E. Auffray, J. Baechler, G. Bardelli, D. Barney, J. Bendavid, I. Bestintzanos, M. Bianco, A. Bocci, G. Boldrini, L. Borgonovi, C. Botta, A. Bragagnolo, C.E. Brown, C. Caillol, G. Cerminara, P. Connor, K. Cormier, D. D’Enterria, A. Dabrowski, P. Das, A. David Tinoco Mendes, M.M. Defranchis, M. Deile, M. Dobson, L. Favilla, P.J. Fernández Manteca, E. Fialova, B.A. Fontana Santos Alves, E. Fontanesi, W. Funk, A. Gaddi, S. Giani, D. Gigi, K. Gill, S. Giorgetti, F. Glege, M. Glowacki, A. Gruber, J. Hegeman, R. Hofsaess, B. Huber, T. James, P. Janot, L. Jeppe, O. Kaluzinska, O. Karacheban²⁶, G. Karathanasis, S. Laurila, P. Lecoq, E. Leutgeb, J. Leon Holgado, C. Lourenco, A.m. Lyon, M. Magherini, L. Malgeri, E. Manca, M. Mannelli, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi, M. Migliorini, F. Monti, F. Moortgat, M.C. Muehlnikel, M. Mulders, M. Musich, I. Neutelings, S. Orfanelli, F. Pantaleo, M. Pari, F. Pereira Carneiro, G. Petrucciani, A. Pfeiffer, M. Pierini, M. Pitt, H. Qu, W. Redjeb, A. Reimers, B. Ribeiro Lopes, F. Riti, P. Rosado, M. Rovere, H. Sakulin, R. Salvatico, S. Scarfi, S.F. Schaefer, M. Selvaggi, P. Silva, P. Sphicas⁶³, A.G. Stahl Leiton, A. Steen, S. Summers, G. Terragni, D. Treille, P. Tropea, E. Vernazza, M. Vojinovic, J. Wanczyk⁶⁴, S. Wuchterl, M. Zarucki, P. Zehetner, P. Zejdl, G. Zevi Della Porta

PSI Center for Neutron and Muon Sciences, Villigen, Switzerland

L. Caminada⁶⁵, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, C. Lange, U. Langenegger, A. Nigamova, L. Noehte⁶⁵, L. Redard-Jacot⁶⁵, T. Rohe, A. Samalan

ETH Zurich - Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland

T.K. Aarrestad, M. Backhaus, A. Belvedere, T. Bevilacqua⁶⁵, G. Bonnemelli, K. Datta, P. De Bryas Dexmiers D’Archiacchiae⁶⁴, A. De Cosa, G. Dissertori, M. Dittmar, M. Donegà, F. Glessgen, C. Grab, T.G. Harte, N. Härringer, B. Kaynak, M. Koppel, W. Lustermann, M. Malucchi, R.A. Manzoni, L. Marchese, F. Nessi-Tedaldi, F. Pauss, A.A. Petre, J. Prendi, B. Ristic, S. Rohletter, P.M. Sander, R. Seidita, A. Tarabini, C.Z. Tee, D. Valsecchi, P.H. Wagner, R. Wallny

Universität Zürich, Zurich, Switzerland

C. Amsler⁶⁶, F. Bilandzija, P. Bärtschi, M.F. Canelli, G. Celotto, Z. Ghafoor, T.A. Goldschmidt, V. Guglielmi, A. Jofrehei, B. Kilminster, T.H. Kwok, S. Leontsinis, V. Lukashenko, A. Macchiolo, F. Meng, J. Motta, P. Robmann, E. Shokr, F. Stäger, R. Tramontano, P. Viscone

Çukurova University, Adana, Türkiye

D. Agyel, F. Dolek, I. Dumanoglu⁶⁷, Y. Guler⁶⁸, E. Gurpinar Guler⁶⁸, A. Kayis Topaksu, G. Onengut, K. Ozdemir⁶⁹, B. Tali⁷⁰, U.G. Tok, E. Uslan

Hacettepe University, Ankara, Türkiye

S. Sen

Bogazici University, Istanbul, Türkiye

B. Akgun, I.O. Atakisi⁷¹, E. Gulmez, M. Kaya⁷², O. Kaya⁷³, M.A. Sarkisla⁷⁴, S. Tekten⁷⁵

Istanbul Technical University, Istanbul, Türkiye

D. Boncukcu, A. Cakir, K. Cankocak^67,76, M. Gumustekin, A.D. Gungordu

Istanbul University, Istanbul, Türkiye

B. Hacisahinoglu, I. Hos⁷⁷, S. Ozkorucuklu, O. Potok, H. Sert, C. Simsek, C. Zorbilmez

Yildiz Technical University, Istanbul, Türkiye

S. Cerci, C. Dozen⁷⁸, E. Iren⁷⁹, B. Isildak, E. Simsek, D. Sunar Cerci, T. Yetkin⁷⁸

National Central University, Chung-Li, Taiwan

D. Bhowmik, Y.h. Chou, C.M. Kuo, P.K. Rout, S. Taj, P.C. Tiwari³⁸

National Taiwan University (NTU), Taipei, Taiwan

L. Ceard, K.F. Chen, Z.g. Chen, A. De Iorio, G.W.S. Hou, H.w. Hsia, T.h. Hsu, S. Karmakar, F. Khuzaimah, G. Kole, Y.y. Li, R.S. Lu, E. Paganis, X.f. Su, J. Thomas-Wilsker, L.s. Tsai, D. Tsionou, H.y. Wu, E. Yazgan

High Energy Physics Research Unit, Department of Physics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand

C. Asawatangtrakuldee, N. Srimanobhas

Tunis El Manar University, Tunis, Tunisia

Y. Maghrbi

Institute for Scintillation Materials of National Academy of Science of Ukraine, Kharkiv, Ukraine

O. Dadazhanova, B. Grynyov

National Science Centre, Kharkiv Institute of Physics and Technology, Kharkiv, Ukraine

K. Klimenko, O. Kurov, L. Levchuk, S. Lukyanenko, A. Pristavka, D. Soroka

University of Bristol, Bristol, United Kingdom

J.J. Brooke, A. Bundock, F.J.J. Bury, E. Clement, D. Cussans, D. Dharmender, H. Flacher, J. Goldstein, H.F. Heath, M.I. Holmberg, A. Karakoulaki, L. Kreczko, S. Paramesvaran, L. Robertshaw, M.S. Sanjrani⁴⁰, J. Segal, V.J. Smith Rutherford Appleton Laboratory, Didcot, United Kingdom

A. Ball, K.W. Bell, A. Belyaev⁸⁰, C. Brew, R.M. Brown, D.J. Cockerill, A. Elliot, K.V. Ellis, J. Gajownik, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, M. Moallemi, D.M. Newbold, E. Olaiya, D. Petyt, T. Reis, A.R. Sahasransu, T. Schuh, C. Shepherd-Themistocleous, I.R. Tomalin, K.C. Whalen, T. Williams

Imperial College, London, United Kingdom

I. Andreou, S. Awan, R. Bainbridge, P. Bloch, O. Buchmuller, C.A. Carrillo Montoya, D. Colling, A. Cox, I. Das, P. Dauncey, G. Davies, A. De Roeck, M. Della Negra, S. Fayer, G. Fedi, G. Hall, H.R. Hoorani, A. Howard, G. Iles, C.R. Knight, P. Krueper, J. Langford, K.H. Law, L. Lyons, A.M. Magnan, B. Maier, S. Mallios, A. Mastronikolis, J. Nash⁸¹, M. Pesaresi, P.B. Pradeep, E.V. Protopapa, B.C. Radburn-Smith, A. Richards, A. Rose, T.B. Runting, L. Russell, K. Savva, R. Schmitz, C. Seez, R. Shukla, A. Tapper, T. Travis, K. Uchida, G.P. Uttley, T. Virdee²⁸, N. Wardle, D. Winterbottom, J. Xiao

Brunel University, Uxbridge, United Kingdom

J. Cole, L. Juckett, A. Khan, P. Kyberd, I. Reid

The University of Alabama, Tuscaloosa, Alabama, USA

B. Bam, A. Buchot Perraguin, S. Campbell, R. Chudasama, S. Cooper, C. Crovella, G. Fidalgo, S.V. Gleyzer, R. Kaur, A. Khukhunaishvili, K. Matchev, E. Pearson, P. Rumerio⁸², E. Usai

University of California, Davis, Davis, California, USA

S. Abbott, S. Baradia, B. Barton, R. Breedon, H. Cai, M. Calderon De La Barca Sanchez, E. Cannaert, M. Chertok, M. Citron, J. Conway, P.T. Cox, F. Eble, R. Erbacher, C. Fairchild, O. Kukral, S. Ostrom, I. Salazar Segovia, J.H. Steenis, J.S. Tafoya Vargas, W. Wei, S. Yoo

University of California, San Diego, La Jolla, California, USA

A. Aportela, A. Arora, J.G. Branson, S. Cittolin, B. D’Anzi, D. Diaz, J. Duarte, L. Giannini, Y. Gu, J. Guiang, V. Krutelyov, R. Lee, J. Letts, H. Li, R. Marroquin Solares, M. Masciovecchio, F. Mokhtar, S. Morovic, S. Mukherjee, M. Pieri, D. Primosch, M. Quinnan, V. Sharma, M. Tadel, E. Vourliotis, F. Würthwein, A. Yagil, Z. Zhao

University of California, Los Angeles, California, USA

K. Adamidis, H. Ancelin, M. Bachtis, D. Campos, R. Cousins, S. Crossley, G. Flores Avila, J. Hauser, M. Ignatenko, M.A. Iqbal, T. Lam, Y.f. Lo, A. Nunez Del Prado, D. Saltzberg, V. Valuev

California Institute of Technology, Pasadena, California, USA

A. Albert, S. Bhattacharya, A. Bornheim, O. Cerri, Z. Hao, R. Kansal, L. Mori, H.B. Newman, G. Reales Gutiérrez, T. Sievert, P. Simmerling, E. Sledge, M. Spiropulu, C. Sun, J.R. Vlimant, R.A. Wynne, S. Xie, R.Y. Zhu

University of California, Riverside, Riverside, California, USA

R. Clare, J.W. Gary, G. Hanson

University of California, Santa Barbara - Department of Physics, Santa Barbara, California, USA

A. Barzdukas, L. Brennan, C. Campagnari, S. Carron Montero⁸³, K. Downham, C. Grieco, J.S. Guo, M.M. Hussain, D. Imani, J. Incandela, A. Krishna, M.W.K. Lai, P. Masterson, J.J.H. Ockenfuss, J. Richman, S.N. Santpur, D. Stuart, T.Á. Vámi, X. Yan, D. Zhang

University of Colorado Boulder, Boulder, Colorado, USA

J.P. Cumalat, W.T. Ford, J. Fraticelli, A. Hart, M. Herrmann, S. Kwan, J. Pearkes, N. Schonbeck, K. Stenson, K. Ulmer, S.R. Wagner, N. Zipper, D. Zuolo

The Catholic University of America, Washington, DC, USA

R. Bartek, A. Dominguez, S. Raj, B. Sahu, A.E. Simsek, B. Singhal, S.S. Yu

University of Florida, Gainesville, Florida, USA

C. Aruta, P. Avery, C. Basile, D. Bourilkov, P. Chang, V. Cherepanov, M. Dittrich, R.D. Field, C. Huh, E. Koenig, M. Kolosova, J. Konigsberg, A. Korytov, G. Mitselmakher, K. Mohrman, A. Muthirakalayil Madhu, N. Rawal, S. Rosenzweig, Y. Takahashi, J. Wang

Florida Institute of Technology, Melbourne, Florida, USA

B. Alsufyani, S. Das, S. Demarest, L. Hasa, M. Hohlmann, M. Lavinsky, E. Yanes

Florida State University, Tallahassee, Florida, USA

T. Adams, A. Al Kadhim, D. Alam, A. Askew, S. Bower, R. Goff, R. Hashmi, A. Hassani, T. Kolberg, G. Martinez, M. Mazza, H. Prosper, P.R. Prova, R. Yohay

Fermi National Accelerator Laboratory, Batavia, Illinois, USA

M. Albrow, M. Alyari, O. Amram, G. Apollinari, A. Apresyan, L.A. Bauerdick, D. Berry, J. Berryhill, P.C. Bhat, K. Burkett, J.N. Butler, A. Canepa, G.B. Cerati, H. Cheung, F. Chlebana, C. Cosby, G. Cummings, I. Dutta, V.D. Elvira, J. Freeman, A. Gandrakota, Z. Gecse, L. Gray, D. Green, A. Grummer, S. Grunendahl, D. Guerrero, O. Gutsche, R.M. Harris, J. Hirschauer, V. Innocente, B. Jayatilaka, S. Jindariani, M. Johnson, R.S. Kim, S. Lammel, D. Lincoln, R. Lipton, T. Liu, K. Maeshima, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, J. Ngadiuba, D. Noonan, S. Norberg, V. Papadimitriou, N. Pastika, K. Pedro, C. Pena⁸⁴, C.E. Perez Lara, V. Perovic, F. Ravera, A. Reinsvold Hall⁸⁵, L. Ristori, M. Safdari, E. Sexton-Kennedy, E. Smith, N. Smith, A. Soha, L. Spiegel, S. Stoynev, J. Strait, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, C. Wang, I. Zoi

University of Illinois Chicago, Chicago, Illinois, USA

M.R. Adams, N. Barnett, A. Baty, C. Bennett, N. Brandman-hughes, R. Cavanaugh, S.J. Das, R. Escobar Franco, O. Evdokimov, C.E. Gerber, H. Gupta, M. Hawksworth, A. Hingrajiya, D.J. Hofman, Z. Huang, J.h. Lee, C. Mills, S. Nanda, G. Nigmatkulov, B. Ozek, V. Pant, T. Phan, D. Pilipovic, R. Pradhan, E. Prifti, T. Roy, D. Shekar, N. Singh, F. Strug, A. Thielen, M. Tonjes, N. Varelas, M.A. Wadud, A. Wang, J. Yoo

Northwestern University, Evanston, Illinois, USA

S. Dittmer, K.A. Hahn, S. King, D. Li, M. Mcginnis, Y. Miao, D.G. Monk, M.H. Schmitt, A. Taliercio, M. Velasco, J. Wang, D. Wilbern

Purdue University Northwest, Hammond, Indiana, USA

N. Parashar, A. Pathak, E. Shumka

University of Notre Dame, Notre Dame, Indiana, USA

G. Agarwal, R. Band, S. Castells, A. Das, A. Datta, A. Ehnis, R. Goldouzian, M. Hildreth, T. Ivanov, C. Jessop, K. Lannon, J. Lawrence, D. Lutton, J. Mariano, N. Marinelli, P. Mastrapasqua, A. Masud, T. McCauley, C. Mcgrady, C. Moore, Y. Musienko²², H. Nelson, M. Osherson, A. Piccinelli, R. Ruchti, A. Townsend, Y. Wan, M. Wayne, H. Yockey

Purdue University, West Lafayette, Indiana, USA

S. Chandra, A. Gu, L. Gutay, L. He, M. Huwiler, M. Jones, A.W. Jung, I.G. Karslioglu, D. Kondratyev, J. Li, M. Liu, M. Macedo, G. Negro, N. Neumeister, G. Paspalaki, S. Piperov, N.R. Saha, J.F. Schulte, R. Sharma, F. Wang, A.L. Wesolek, A. Wildridge, W. Xie, Y. Yao, Y. Zhong

The University of Iowa, Iowa City, Iowa, USA

M. Alhusseini, D. Blend, K. Dilsiz⁸⁶, O.K. Köseyan, A. Mestvirishvili⁵⁹, O. Neogi, H. Ogul⁸⁷, Y. Onel, A. Penzo, C. Snyder

The University of Kansas, Lawrence, Kansas, USA

A. Abreu, L.F. Alcerro Alcerro, J. Anguiano, S. Arteaga Escatel, P. Baringer, A. Bean, R. Bhattacharya, M. Chukwuka, Z. Flowers, D. Grove, J. King, G. Krintiras, M. Lazarovits, C. Le Mahieu, J. Marquez, M. Murray, M. Nickel, E. Reynolds, C. Rogan, C. Royon, S. Rudrabhatla, S. Sanders, J.A. Velazquez Corral, G. Wilson

Kansas State University, Manhattan, Kansas, USA

A. Ahmad, B. Allmond, N. Islam, A. Ivanov, K. Kaadze, Y. Maravin, J. Natoli, G.G. Reddy, D. Roy, G. Sorrentino

Johns Hopkins University, Baltimore, Maryland, USA

B. Blumenfeld, J. Davis, A. Gritsan, Z. Huang, L. Kang, P. Maksimovic, N. Pinto, M. Roguljic, S. Sekhar, M.V. Srivastav, M. Swartz

University of Maryland, College Park, Maryland, USA

Z. Alton, D. Baden, A. Belloni, J. Bistany-riebman, S.C. Eno, N.J. Hadley, S. Jabeen, R.G. Kellogg, T. Koeth, B. Kronheim, J. Lee, P. Major, A. Mignerey, C. Palmer, C. Papageorgakis, M.M. Paranjpe, E. Popova⁸⁸, A. Shevelev, M. Wrotny, L. Zhang

Boston University, Boston, Massachusetts, USA

S. Cholak, Z. Demiragli, C. Erice, C. Fangmeier, C. Fernandez Madrazo, J. Fulcher, J. Garcia De Castro, F. Golf, S. Jeon, G. Linney, J. O’Cain, I. Reed, J. Rohlf, D. Sperka, I. Suarez, A. Tsatsos, E. Wurtz, A.G. Zecchinelli

Northeastern University, Boston, Massachusetts, USA

A. Aarif, G. Alverson, E. Barberis, S. Bein, J. Bonilla, B. Bylsma, M. Campana, R. Clark, Y. Han, I. Israr, M. Lu, N. Manganelli, R. Mccarthy, D.M. Morse, T. Orimoto, L. Skinnari, C.S. Thoreson, E. Tsai, D. Wood

Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

C. Baldenegro Barrera, H. Bossi, S. Bright-Thorne, I.A. Cali, Y.C. Chen, P.C. Chou, M. D’Alfonso, K. Devereaux, J. Eysmans, C. Freer, G. Gomez Ceballos, M. Goncharov, G. Grosso, P. Harris, D. Hoang, A. Holterman, G.M. Innocenti, K. Ivanov, G. Kopp, D. Kovalskyi, J. Lang, L. Lavezzo, Y.J. Lee, P. Lugato, C. Mcginn, E. Moreno, A. Novak, M.I. Park, C. Paus, C. Reissel, C. Roland, G. Roland, S. Rothman, T.a. Sheng, G. Stephans, D. Walter, J. Wang, Z. Wang, B. Wyslouch, T. Yang, K. Yoon Wayne State University, Detroit, Michigan, USA

P.E. Karchin

University of Minnesota, Minneapolis, Minnesota, USA

A. Alpana, B. Crossman, W.J. Jackson, C. Kapsiak, D. Mahon, J. Mans, B. Marzocchi, R. Rusack, O. Sancar, R. Saradhy, N. Strobbe

Bethel University, St. Paul, Minnesota, USA

J.M. Hogan

University of Nebraska-Lincoln, Lincoln, Nebraska, USA

K. Bloom, D.R. Claes, S.V. Dixit, G. Haza, J. Hossain, C. Joo, I. Kravchenko, K.H.M. Kwok, Y. Mehra, J. Morris, A. Rohilla, J.E. Siado, A. Vagnerini, A. Wightman

Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA

B. Chiarito, J.P. Chou, S. Donnelly, D. Gadkari, Y. Gershtein, E. Halkiadakis, C. Houghton, D. Jaroslawski, A. Kaur, A. Kobert, A. Lath, J. Martins, P. Meltzer, M. Perez Prada, K. Ramdin, B. Rand, J. Reichert, P. Saha, S. Salur, S. Somalwar, R. Stone, S.A. Thayil, S. Thomas, J. Vora

Princeton University, Princeton, New Jersey, USA

H. Bouchamaoui, G. Dezoort, P. Elmer, A. Frankenthal, M. Galli, B. Greenberg, K. Kennedy, Y. Lai, D. Lange, A. Loeliger, D. Marlow, I. Ojalvo, J. Olsen, F. Simpson, D. Stickland, C. Tully, S. Yoon

State University of New York at Buffalo, Buffalo, New York, USA

H. Bandyopadhyay, I. Iashvili, A. Kalogeropoulos, A. Kharchilava, A. Mandal, C. McLean, D. Nguyen, O. Poncet, S. Rappoccio, H. Rejeb Sfar, W. Terrill, D. Yu

Cornell University, Ithaca, New York, USA

J. Alexander, X. Chen, G. De Castro, J. Dickinson, A. Duquette, J. Fan, X. Fan, J. Grassi, P. Kotamnives, K. Krzyzanska, J. Monroy, G. Niendorf, M. Oshiro, J.R. Patterson, A. Ryd, J. Thom, H.A. Weber, B. Weiss, P. Wittich, Y. Wu, R. Zou, L. Zygala

University of Rochester, Rochester, New York, USA

A. Bodek, R. Demina, A. Garcia-Bellido, H.S. Hare, O. Hindrichs, Y.w. Kao, N. Parmar, P. Parygin{sup}88`, H. Seo, R. Taus, Y.h. Yu

The Ohio State University, Columbus, Ohio, USA

M. Carrigan, R. De Los Santos, L.S. Durkin, C. Hill, M. Joyce, L. Nestor, D.A. Wenzl, B.L. Winer, B. Yates

Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

J. Alison, S. An, M. Cremonesi, V. Dutta, E.Y. Ertorer, T. Ferguson, T.A. Gómez Espinosa, A. Harilal, A. Kallil Tharayil, M. Kanemura, A. Khanal, C. Liu, M. Marchegiani, P. Meiring, S. Murthy, P. Palit, K. Park, M. Paulini, A. Roberts, Y. Zhou

University of Puerto Rico, Mayaguez, Puerto Rico, USA

S. Malik, R. Sharma

Brown University, Providence, Rhode Island, USA

G. Barone, G. Benelli, D. Cutts, S. Ellis, S. Gottlieb, L. Gouskos, M. Hadley, L. Hay, U. Heintz, K.W. Ho, R. Jain, T. Kwon, L. Lambrecht, G. Landsberg, K.T. Lau, M. LeBlanc, J. Luo, S. Mondal, J. Roloff, T. Russell, S. Sagir⁸⁹, X. Shen, M. Stamenkovic, S. Sunnarborg, J. Tang, N. Venkatasubramanian

University of Tennessee, Knoxville, Tennessee, USA

A. Abdelhamid, D. Ally, A.G. Delannoy, J. Dervan, S. Fiorendi, J. Harris, T. Holmes, A.R. Kanuganti, N. Karunarathna, J. Lawless, L. Lee, E. Nibigira, B. Skipworth, S. Spanier, C. Thompson, A. Vendrasco

Vanderbilt University, Nashville, Tennessee, USA

U. Acharya, E. Appelt, Y. Chen, S. Greene, A. Gurrola, W. Johns, R. Kunnawalkam Elayavalli, A. Melo, D. Rathjens, F. Romeo, I. Shvetsov, S. Tuo, J. Velkovska, J. Zhang

Texas A&M University, College Station, Texas, USA

D. Aebi, M. Ahmad, T. Akhter, K. Androsov, A. Basnet, A. Bolshov, O. Bouhali⁹⁰, A. Cagnotta, S. Cooperstein, V. D’Amante, R. Eusebi, P. Flanagan, J. Gilmore, Y. Guo, T. Kamon, R. Mueller, G. Pizzati, A. Safonov

Rice University, Houston, Texas, USA

D. Acosta, A. Agrawal, C. Arbour, T. Carnahan, K.M. Ecklund, F.J. Geurts, I. Krommydas, N. Lewis, W. Li, J. Lin, X. Liu, C. Loizides, O. Miguel Colin, B.P. Padley, R. Redjimi, J. Rotter, C. Vico Villalba, M. Wulansatiti, E. Yigitbasi, Y. Zhang

Texas Tech University, Lubbock, Texas, USA

N. Akchurin, J. Damgov, Y. Feng, N. Gogate, W. Jin, S.W. Lee, C. Madrid, S. Magedov, A. Mankel, T. Peltola, I. Volobouev

Baylor University, Waco, Texas, USA

S. Abdullin, A. Brinkerhoff, E. Collins, M.R. Darwish, J. Dittmann, K. Hatakeyama, V. Hegde, J. Hiltbrand, B. McMaster, J. Samudio, S. Sawant, C. Sutantawibul, J. Wilson

University of Virginia, Charlottesville, Virginia, USA

B. Cardwell, H. Chung, B. Cox, J. Hakala, G. Hamilton Ilha Machado, R. Hirosky, M. Jose, A. Ledovskoy, C. Mantilla, R. Menon Raghunandanan, C. Neu, C. Ramón Álvarez, Z. Wu

University of Wisconsin - Madison, Madison, Wisconsin, USA

A. Aravind, S. Banerjee, K. Black, T. Bose, E. Chavez, R. Cruz, S. Dasu, P. Everaerts, C. Galloni, H. He, M. Herndon, A. Herve, C.K. Koraka, S. Lomte, R. Loveless, J. Marquez, A. Mohammadi, S. Mondal, T. Nelson, G. Parida, D. Pinna, A. Savin, V. Sharma, R. Simeon, W.H. Smith, D. Teague, M. Thakore, A. Thete, A. Warden

Authors affiliated with an international laboratory covered by a cooperation agreement with CERN

S. Afanasiev, V. Alexakhin, Y. Andreev, D. Budkouski, R. Chistov, M. Danilov, T. Dimova, I. Gorbunov, A. Kamenev, V. Karjavine, O. Kodolova⁹¹, V. Korenkov, I. Korsakov, A. Kozyrev, A. Lanev, A. Malakhov, V. Matveev, A. Nikitenko^92,91, V. Palichik, V. Perelygin, S. Polikarpov, O. Radchenko, M. Savina, V. Shalaev, S. Shmatov, S. Shulha, Y. Skovpen, K. Slizhevskiy, V. Smirnov, O. Teryaev, A. Toropin, N. Voytishin, A. Zarubin, I. Zhizhin Authors affiliated with an institute formerly covered by a cooperation agreement with CERN

L. Dudko, V. Kim{sup}22`, V. Murzin, V. Oreshkin, D. Sosnov ⁴⁶ Also at Kocaeli University, Kocaeli, Türkiye

⁴⁷ Also at Centro Siciliano di Fisica Nucleare e di Struttura della Materia, Catania, Italy

⁴⁸ Also at James Madison University, Harrisonburg, Virginia, USA

⁴⁹ Also at Università degli Studi Guglielmo Marconi, Roma, Italy

⁵⁰ Also at Scuola Superiore Meridionale, Università di Napoli ’Federico II’, Napoli, Italy

⁵¹ Also at Fermi National Accelerator Laboratory, Batavia, Illinois, USA

⁵² Also at Lulea University of Technology, Lulea, Sweden

⁵³ Also at Ain Shams University, Cairo, Egypt

⁵⁴ Also at Consiglio Nazionale delle Ricerche - Istituto Officina dei Materiali, Perugia, Italy

⁵⁵ Also at Boston University, Boston, Massachusetts, USA

⁵⁶ Also at UPES - University of Petroleum and Energy Studies, Dehradun, India

⁵⁷ Also at Institut de Physique des 2 Infinis de Lyon (IP2I), Villeurbanne, France

⁵⁸ Also at Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia

⁵⁹ Also at Georgian Technical University, Tbilisi, Georgia

⁶⁰ Also at Departamento de Física Instituto Superior Técnico Universidade de Lisboa, Lisbon, Portugal

⁶¹ Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri Lanka

⁶² Also at Saegis Campus, Nugegoda, Sri Lanka

⁶³ Also at National and Kapodistrian University of Athens, Athens, Greece

⁶⁴ Also at Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland

⁶⁵ Also at Universität Zürich, Zurich, Switzerland

⁶⁶ Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna, Austria

⁶⁷ Also at Near East University, Research Center of Experimental Health Science, Mersin, Türkiye

⁶⁸ Also at Konya Technical University, Konya, Türkiye

⁶⁹ Also at Izmir Bakircay University Faculty of Engineering and Architecture, Izmir, Türkiye

⁷⁰ Also at Adiyaman University, Adiyaman, Türkiye

⁷¹ Also at Istanbul Sabahattin Zaim University, Istanbul, Türkiye

⁷² Also at Marmara University, Istanbul, Türkiye

⁷³ Also at Milli Savunma University, Naval Academy, Istanbul, Türkiye

⁷⁴ Also at The Science and Technological research Council of Türkiye, Informatics and Information Security Research Center, Gebze/Kocaeli, Türkiye

⁷⁵ Also at Kafkas University, Kars, Türkiye

⁷⁶ Now at Istanbul Okan University, Istanbul, Türkiye

⁷⁷ Also at Istanbul University - Cerrahpasa, Faculty of Engineering, Istanbul, Türkiye

⁷⁸ Also at Istinye University, Istanbul, Türkiye

⁷⁹ Also at Mimar Sinan University, Istanbul, Istanbul, Türkiye

⁸⁰ Also at School of Physics and Astronomy, University of Southampton, Southampton, United Kingdom

⁸¹ Also at Monash University, Faculty of Science, Clayton, Australia

⁸² Also at Università di Torino, Torino, Italy

⁸³ Also at California Lutheran University, Thousand Oaks, California, USA

⁸⁴ Also at California Institute of Technology, Pasadena, California, USA

⁸⁵ Also at United States Naval Academy - Physics Department, Annapolis, Maryland, USA

⁸⁶ Also at Bingol University, Bingol, Türkiye

⁸⁷ Also at Sinop University, Sinop, Türkiye

⁸⁸ Now at another institute formerly covered by a cooperation agreement with CERN

⁸⁹ Also at Karamanoğlu Mehmetbey University, Karaman, Türkiye ^{90}Also at Hamad Bin Khalifa University (HBKU), Doha, Qatar

^{91}Also at Yerevan Physics Institute, Yerevan, Armenia

^{92}Also at Imperial College, London, United Kingdom