Search for long-lived particles decaying to a pair of muons in proton-proton collisions at √s = 13 tev

JHEP05(2023)228 Published for SISSA by Springer Received:May 17, 2022 Accepted:September 19, 2022 Published:May 30, 2023 Search for long-lived particles decaying to a pair of muons in proton-proton collisions at √s= 13 TeV The CMS collaboration E-mail: [email protected] Abstract: An inclusive search for long-lived exotic particles decaying to a pair of muons is presented. The search uses data collected by the CMS experiment at the CERN LHC in proton-proton collisions at √s= 13 TeV in 2016 and 2018 and corresponding to an integrated luminosity of 97.6fb−1. The experimental signature is a pair of oppositely charged muons originating from a common secondary vertex spatially separated from the pp interaction point by distances ranging from several hundred µm to several meters. The results are interpreted in the frameworks of the hidden Abelian Higgs model, in which the Higgs boson decays to a pair of long-lived dark photons ZD, and of a simplified model, in which long-lived particles are produced in decays of an exotic heavy neutral scalar boson. For the hidden Abelian Higgs model with m(ZD)greater than 20GeV and less than half the mass of the Higgs boson, they provide the best limits to date on the branching fraction of the Higgs boson to dark photons for cτ(ZD)(varying with m(ZD)) between 0.03 and ≈0.5mm, and above ≈0.5m. Our results also yield the best constraints on long-lived particles with masses larger than 10GeV produced in decays of an exotic scalar boson heavier than the Higgs boson and decaying to a pair of muons. Keywords: Exotics, Hadron-Hadron Scattering, Lifetime ArXiv ePrint: 2205.08582 Open Access, Copyright CERN, for the benefit of the CMS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP05(2023)228 JHEP05(2023)228 Contents 1 Introduction 1 2 The CMS detector 2 3 Signal models, data and simulated samples 3 4 Analysis strategy and event selection 5 5 Background estimation and associated systematic uncertainties 13 5.1 Estimation of Drell–Yan and other prompt backgrounds 15 5.2 Estimation of nonprompt backgrounds 17 5.3 Validation of background predictions 20 6 Systematic uncertainties affecting the signal 22 7 Results 24 8 Summary 32 The CMS collaboration 37 1 Introduction Long-lived particles (LLPs) are predicted by many extensions of the standard model (SM), in particular by various supersymmetric scenarios [1,2] and “hidden sector” models [3,4]. Such particles could manifest themselves through decays to SM particles at macroscopic distances from the proton-proton (pp) interaction point (IP). This paper describes an inclusive search for an exotic massive LLP decaying to a pair of oppositely charged muons, referred to as a “displaced dimuon”, that originates from a common secondary vertex spatially separated from the IP. The search is based on an analysis of pp collisions corresponding to an integrated luminosity of 97.6fb−1collected with the CMS detector at √s= 13 TeV during Run 2 of the CERN LHC. A minimal set of requirements and loose event selection criteria allow the search to be sensitive to a wide range of models predicting LLPs that decay to final states that include a pair of oppositely charged muons. We interpret the results of the search in the framework of two benchmark models: the hidden Abelian Higgs model (HAHM), in which displaced dimuons arise from decays of hypothetical dark photons [5], and a simplified model, in which a non-SM Higgs boson decays to a pair of long-lived exotic heavy neutral scalar bosons, at least one of which decays into a pair of muons [6]. – 1 – JHEP05(2023)228 The present search explores the LLP mass range above 10GeV accessible with the Run 2 dimuon triggers and is sensitive to secondary vertex displacements ranging from several hundred µm to several meters. It is a continuation and extension of two CMS analyses performed using data taken at √s= 8 TeV during Run 1 of the LHC. One analysis was dedicated to a search for LLPs decaying to two electrons or two muons in the tracker [7]; the other looked for LLP decays to final states containing two muons reconstructed only in the muon system [8]. The analysis of the Run 2 data described here contains numerous improvements over these Run 1 searches, notably in refined event selection and improved background evaluation procedures. It also benefits from an increase in integrated luminosity by almost a factor of five, collected at a higher √s. A search for LLPs decaying to displaced dimuons has also been performed by the ATLAS Collaboration, using 2016 data corresponding to an integrated luminosity of 32.9 fb−1[9]. This paper is organized as follows. Section 2describes the CMS detector. Section 3 presents the signal models as well as the samples analyzed from data and from the Monte Carlo simulation. Section 4describes the analysis strategy, the triggers, and the offline event selection. Estimation of backgrounds and the associated systematic uncertainties are described in section 5. Section 6summarizes the systematic uncertainties affecting signal efficiencies. Section 7describes the results obtained in the individual dimuon categories and their combination. The analysis summary is presented in section 8. Tabulated results and supplementary material for reinterpreting the results in the framework of models not explicitly considered in this paper are provided in HEPData [10]. 2 The CMS detector The central feature of the CMS detector is a superconducting solenoid of 6m internal diameter, providing a magnetic field of 3.8T. Within the solenoid volume are a silicon pixel and strip tracker extending outwards to a radius of 1.1m, a lead tungstate crystal electromagnetic calorimeter, and a brass and scintillator hadron calorimeter, each composed of a barrel and two endcap sections. Forward calorimeters extend the coverage in pseudorapidity ηprovided by the barrel and endcap detectors. Muons are detected in gas-ionization chambers covering the range |η|<2.4and embedded in the steel flux-return yoke outside the solenoid. The muon system is composed of three types of chambers: drift tubes (DTs) in the barrel, cathode strip chambers (CSCs) in the endcaps, and resistive-plate chambers in both the barrel and the endcaps. The chambers are assembled into four “stations” at increasing distance from the IP; each station provides reconstructed hits in several detection planes, which are combined into track segments, forming the basis of muon reconstruction in the muon system [11]. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in ref. [12]. 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 approximately 100kHz within a fixed time interval of less than 4µs [13]. The second level, known as the high-level trigger (HLT), consists of – 2 – JHEP05(2023)228 H κ ¯µ µ ¯ f f HD ZD ZD Φ ¯µ µ ¯ f f X X Figure 1. Feynman diagrams for (left) the HAHM model, showing the production of long-lived dark photons ZDvia the Higgs portal, through H–HDmixing with the parameter κ, with subsequent decays via the vector portal; and (right) the heavy-scalar model with Φboson decaying to a pair of long-lived bosons X. The symbols fand frepresent, respectively, fermions and antifermions lighter than half the LLP mass. a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to about 1kHz before data storage [14]. 3 Signal models, data and simulated samples The search is performed using pp collision data collected at √s= 13 TeV in 2016 and 2018 corresponding to integrated luminosities of 36.3±0.4and 61.3±1.5fb−1, respectively [15, 16]. The data collected in 2017 are not used because the triggers required for the analysis were not included when those data were recorded. As mentioned above, two signal models with different final-state topologies and event kinematics are used in the optimization of event selection criteria and in the interpretation of results. The first belongs to a class of models featuring a “hidden” or “dark” sector of matter that does not interact directly with SM particles, but can manifest itself through mixing effects. This HAHM benchmark contains an extra dark U(1)Dgauge group whose symmetry is broken by a new dark Higgs field [5,17]. The spin-1 mediator of the U(1)D group, known as the dark photon ZD, mixes kinetically with the hypercharge SM gauge boson (“vector portal”), whereas the dark Higgs boson HDmixes with the SM Higgs boson H(“Higgs portal”) and gives mass m(ZD)to the dark photon. If there are no hidden-sector states with masses smaller than m(ZD), the mixing through the vector portal with the SM photon and Zboson causes the dark photon to decay exclusively to SM particles, with a sizable branching fraction to leptons. Pair production of the ZDvia the Higgs portal with subsequent decays of dark photons via the vector portal is shown in figure 1(left). The present search probes the regime of m(ZD)>10 GeV with small values of the Z–ZDkinetic mixing parameter [5]. In this regime, the dark photon is long-lived, since its mean proper lifetime τ(ZD)is proportional to −2. In particular, the dark photon with 10 GeV .m(ZD)< m(H)/2is expected to have macroscopically large mean proper decay lengths cτ(ZD)&O(100 µm)for  < O(10−6). The ZDproduction rate is governed by the – 3 – JHEP05(2023)228 branching fraction B(H →ZDZD), which does not depend on but is proportional to the square of κm2(H)/|m2(H) −m2(HD)|, where κis the H–HDmixing parameter. Since κ and m(HD)affect only the overall dark photon production rate, sampling of m(ZD)and is sufficient to explore different kinematical and topological scenarios of the model. We generated a set of 24 HAHM samples with m(ZD)between 10 and 60GeV and between 10−6and 2×10−9. In this mass range, the model’s prediction for B(ZD→µµ)varies between 15.4% at m(ZD) = 10 GeV and 10.7% at m(ZD) = 60 GeV. The dark Higgs boson is assumed to be heavy enough such that H→HDHDdecays are kinematically forbidden. In the sample generation, we specify m(HD) = 400 GeV and κ= 0.1. The production of dark photons is modeled at leading order by MadGraph5_amc@nlo [18] version 2.4.2. The generation of the samples is done for the dominant gluon-fusion production mechanism. The Higgs boson production cross section is normalized to the most recent theoretical prediction for the sum of all production modes for m(H) = 125 GeV, 55.7pb [19]. The decays of the dark photons are modeled by pythia 8.212 and 8.230 [20] in samples corresponding to the 2016 and 2018 data sets, respectively. At the LHC, another way that LLPs might arise is via production of mediators heavier than the Higgs boson that decay into LLPs. To explore ranges of kinematic variables and event topologies broader than those offered by HAHM, we also consider a simplified benchmark model [6], previously used in Run 1 searches for displaced dimuons [7,8], in which the LLP is an exotic spin-0 boson X. The scalar Xhas a non-zero branching fraction to dimuons and is pair produced in the decay of a new heavier scalar boson Φ, which is produced in gluon-gluon fusion: gg →Φ→XX,X→µ+µ−. The Feynman diagram for this process is shown in figure 1(right). The samples for Φ→XX are generated with pythia. Two sets of samples are produced, depending on whether one or both Xbosons are forced to decay to dimuons. Samples in each set are generated with different combinations of Φboson masses m(Φ)(ranging from 125GeV to 1TeV) and Xboson masses m(X) (ranging from 20 to 350 GeV). The width of the Φboson is assumed to be small for the purpose of simulation, but the analysis has negligible dependence on this assumption. Each sample is furthermore produced with three different mean proper lifetimes τ(X) of the Xbosons, corresponding to mean transverse decay lengths of approximately 3, 30, and 250 cm. Events generated at the selected values of m(Φ),m(X), and τ(X) allow us to study wide ranges of signal displacements, kinematical variables, and event topologies. Since the optimization of the event selection criteria and the evaluation of the residual backgrounds are performed using data, the simulated background samples are used primarily to gain a better understanding of the nature and composition of surviving background events. Simulated background samples used in the analysis include Drell–Yan (DY) dilepton production; tt,tW, and tW events; Wand Zboson pair production (dibosons); W+jets; and events comprised of jets produced through the strong interaction that are enriched in muons from semileptonic decays of hadrons containing bor cquarks. The 2016 simulated signal and background samples are produced with either the NNPDF2.3 (leading order) or NNPDF3.0 (next-to-leading order) parton distribution functions (PDFs) [21], using the CUETP8M1 [22] tune to model the underlying event. All 2018 – 4 – JHEP05(2023)228 simulated samples are produced with the NNPDF3.1 PDFs [23] (next-to-next-to-leading order), using the CP5 [24] tune, which is optimized for the NNPDF3.1 PDFs. Simulation of the passage of particles through detector material is performed by Geant4 [25]. Simulated minimum bias events are superimposed on a hard interaction in simulated events to describe the effect of additional inelastic pp interactions within the same or neighboring bunch crossings, known as pileup; the samples are weighted to match the pileup distribution observed in data. All simulated events are then reconstructed with the same algorithms as used for data. 4 Analysis strategy and event selection An LLP produced in the hard interaction of the colliding protons may travel a significant distance in the detector before decaying into muons. While trajectories of the muons produced well within the silicon tracker can be reconstructed by both the tracker and the muon system, tracks of muons produced in the outer tracker layers or beyond can only be reconstructed by the muon system. Since the dimuon vertex resolution and the background composition differ dramatically depending on whether the muon is reconstructed in the tracker, we classify all reconstructed dimuon events into three mutually exclusive categories: a) both muons are reconstructed using both the tracker and the muon system (TMS-TMS category); b) both muons are reconstructed using only the muon system, as “standalone” muons (STA-STA category); and c) one muon is reconstructed only in the muon system, whereas the other muon is reconstructed using both the tracker and the muon system (STATMS category). These three categories of events are analyzed separately, each benefiting from dedicated event selection criteria and background evaluation. The results in each category are statistically combined to provide the final results. The beamspot is identified with the mean position of the pp interaction vertices. The primary vertex (PV) 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 of ref. [26]. A pair of reconstructed muon tracks is fitted to a common vertex (CV), which is expected to be displaced with respect to the PV. The transverse decay length Lxy is defined as the distance between the PV and the CV in the plane transverse to the beam direction. The transverse impact parameter d0is defined as the distance of closest approach (DCA) of the muon track in the transverse plane with respect to the PV. Events were collected with dedicated triggers aimed at recording dimuons produced both within and outside of the tracker. Therefore, these triggers require two muons reconstructed in the muon system alone, without using any information from the tracker, and do not impose the beamspot constraint in the muon track fit at the HLT [27]. Each muon is required to be within the region |η|<2.0and to have transverse momentum magnitude pT>28(23) GeV in 2016 (2018) data taking. To reduce the trigger rate caused by hadron punch-through and poorly measured muons, each muon track is required to be composed of segments found in two or more muon stations. To reduce the contribution to the trigger rate from cosmic ray muons and low-mass dimuon resonances, the trigger used to collect 2016 data also required that the 3D angle between the muons be less than 2.5rad, and – 5 – JHEP05(2023)228 0 20 40 60 80 100 [cm] 0 d 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Efficiency Cosmic ray muon data thresholds L1 T > 33 GeV, 2016 p STA T pCMS > 4 GeV) L1 T Data (p > 4 GeV) L1 T XX (p→ φ > 11 GeV) L1 T Data (p > 11 GeV) L1 T XX (p→ φ 0 20 40 60 80 100 [cm] 0 d 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Efficiency Cosmic ray muon data thresholds L1 T > 28 GeV, 2018 p STA T pCMS > 7 GeV) L1 T Data (p > 7 GeV) L1 T XX (p→ φ > 15 GeV) L1 T Data (p > 15 GeV) L1 T XX (p→ φ Figure 2. L1 muon trigger efficiency in cosmic ray muon data (blue) and signal simulation (red) as a function of d0, for the L1 trigger pTthresholds used in (left) 2016 and (right) 2018. The denominator in the efficiency calculations is the number of STA muons with |η|<1.2and pT>33 (28)GeV in 2016 (2018). that the invariant mass of the two muons be larger than 10GeV. The optimization of the online selection prior to the 2018 data taking made it possible to remove these two requirements from the 2018 trigger, thus providing additional validation regions for background evaluation and increasing the signal efficiency. The efficiency of triggering on signal events in 2018 was further improved by complementing the above trigger with one very similar to it, but using a modified version of the initial “seeding” stage of the muon trajectory building at the HLT. The seed generator used in the new trigger was specifically designed for muons not pointing to the beamspot and helped to increase the reconstruction efficiency for displaced muons. The high-level triggers used in the analysis were seeded by L1 dimuon triggers that required the pTof the muons to be above certain thresholds. The values of the thresholds were varied during the data taking, depending on the instantaneous luminosity, from 11 and 4GeV (for the leading and subleading L1 muons, respectively) during most of 2016, to 15 and 7GeV at the end of Run 2. At L1, the pTassignment for muons was made under the assumption that the muons originated at the beamspot. As a result, the pTof the displaced muons not pointing to the beamspot were underestimated and could fall below the L1 trigger thresholds. The ensuing signal efficiency loss was larger when higher L1 trigger pT thresholds were used. Since this effect is decoupled from the collision environment (e.g., instantaneous luminosity), it can be studied using cosmic ray muons recorded with very loose triggers during periods with no beam. Figure 2shows that the decrease in the L1 muon trigger efficiency as a function of the impact parameter d0for various L1 trigger pT thresholds used in 2016 (left) and 2018 (right) is well reproduced by the signal simulation in the barrel. As noted in section 1, no single muon reconstructor provides optimal performance over the wide range of displacements of secondary vertices considered in the analysis. Muons produced near the IP can be accurately reconstructed by using commonly used algorithms developed for prompt muons and combining measurements in the tracker and – 6 – JHEP05(2023)228 the muon system. Among them are the global muon and tracker muon reconstruction algorithms [11,28]. The first algorithm builds “global muons” by using hits in the tracker and segments in the muon system in a common track fit. The second constructs “tracker muons” by propagating tracks in the inner tracker to the muon system and requiring loose geometrical matching to DT or CSC segments. The efficiency of these algorithms, however, rapidly decreases as the distance between the IP and muon origin increases, dropping to zero for muons produced in the outer tracker layers and beyond. On the other hand, such muons can still be efficiently reconstructed by algorithms that use only information from the muon system. These STA algorithms [11,28] can reconstruct muons with displacements of up to a few meters, but they have poorer spatial and momentum resolution than muons reconstructed using more precise information from the silicon tracker. To benefit from the advantages offered by both types of algorithms and to follow what was done in the trigger, we begin the muon selection with the most efficient standalone muons and replace them with more accurately reconstructed global and tracker muons whenever such muons are found. We use muons reconstructed by an STA algorithm with the beamspot constraints removed from all stages of the muon reconstruction procedure, which yields the highest efficiency and the best resolution for displaced muons, out of all available STA algorithms. The event selection starts with the requirement that the event is selected by the triggers described above and has at least two STA muons, each containing more than 12 valid CSC or DT hits. The requirement of the minimal number of hits suppresses backgrounds from hadron punch-through and other sources, and ensures that the STA muons have acceptable pTresolution and charge assignment. The STA muons that satisfy this basic quality requirement form the initial list of the muon candidates retained for the analysis. Next, we reject events in which no HLT muon pair that triggered the event matches two STA muons in the list. This requirement suppresses events that triggered on muons not related to the signal and facilitates application of trigger efficiency measurements in the analysis. We then attempt to match each STA muon in the list with a TMS muon, i.e., a global or a tracker muon. The STA and TMS muons are considered to be matched if they share at least two thirds of their segments or if ∆RSTA−TMS <0.1, where ∆RSTA−TMS = p(ηhit −ηpca)2+ (φhit −φpca)2is the separation between ηhit (φhit) of the position of the innermost hit of the STA muon and ηpca (φpca) of the point of closest approach of the TMS muon to this hit. If an associated TMS muon is found, it replaces the corresponding STA muon in the list of the muon candidates used for further analysis; otherwise, the original STA muon is kept. The matching procedure was optimized using events in the simulated signal and background samples as well as data in the signal-free control regions discussed in section 5. It eliminates most of the pp collision background to LLP decays outside of the tracker and greatly increases sensitivity to LLP decays in the tracker, thanks to a far superior resolution of TMS muons compared to that of STA muons. The impact of the STA-to-TMS muon association procedure on the event selection is further illustrated in figure 3, which shows the fraction of simulated Φ→XX → µµ+anything signal events with zero, one, and two STA muons matched to TMS muons – 7 – JHEP05(2023)228 0 50 100 150 200 250 300 350 400 [cm] true xy L 0 0.2 0.4 0.6 0.8 1 Fraction of dimuons by category +anythingµµ→XX→ φ CMS Simulation (13 TeV) TMS-TMS STA-TMS STA-STA Figure 3. Fractions of signal events with zero (green), one (blue), and two (red) STA muons matched to TMS muons by the STA-to-TMS muon association procedure, as a function of true Lxy, in all simulated Φ→XX →µµ+anything signal samples combined. The fractions are computed relative to the number of signal events passing the trigger and containing two STA muons with more than 12 muon detector hits and pT>10 GeV matched to generated muons from X→µµ decays. as a function of Ltrue xy , defined as the transverse distance between the simulated positions of the hard-interaction and LLP decay vertices. While almost all dimuons produced close to the IP have both STA muons matched to TMS muons, the fraction of these events rapidly decreases with Ltrue xy , reflecting the dependence on Ltrue xy of the tracker reconstruction efficiency. Events with one STA muon matched to a TMS muon start to dominate at Ltrue xy = 25 cm and remain the dominant component up to ≈50 cm, where events with no STA-to-TMS matches take over. When LLPs decay in the outer tracker layers or beyond the tracker, all STA-to-TMS associations are purely accidental and occur for fewer than 5% of the simulated signal muons. Therefore, the association procedure gives rise to three categories of dimuons, each dominating in a certain Ltrue xy range: TMS-TMS at small Ltrue xy , STA-TMS at intermediate Ltrue xy , and STA-STA at large Ltrue xy . The number of STA-STA dimuons beyond the solenoid, at Ltrue xy >3.2m, is low because of the low trigger efficiency. The STA and TMS muons are then subjected to additional selection criteria optimized using simulated signal and background samples, and samples of dimuons misreconstructed as displaced in the signal-free regions in data. The STA muons are required to have pT>10 GeV and to satisfy the following criteria: •relative pTuncertainty σpT/pT<1.0, where σpTis the internal uncertainty from the muon track fit; •χ2/dof of the muon track fit less than 2.5; •more than 18 DT hits for muons reconstructed only in the barrel; – 8 – JHEP05(2023)228 are chosen by inverting one or more selection criteria in order to obtain a region populated mostly by a given type of background and containing a negligible contribution from the signal processes. The definitions of the control regions and the details of the background evaluation procedure differ for different dimuon categories and are described in the rest of this section. To avoid potential bias in the event selection, the events passing the full selection (i.e., those in the signal region) were “blinded” until the last steps of the analysis. The contribution from cosmic ray muons is evaluated separately for each dimuon category from the number of dimuons satisfying all selection criteria but failing the cos αrequirements. The evaluation procedure makes use of the efficiency of the cos αrequirements measured from a sample of cosmic ray muons collected during periods with no beam. In all dimuon categories, the residual background arising from cosmic ray muons is estimated to be smaller than 0.1 events in all mass intervals combined. 5.1 Estimation of Drell–Yan and other prompt backgrounds In all three dimuon categories, the contribution from prompt misreconstructed dimuons, collectively referred to as DY-like events, is evaluated from events in the signal-free |∆Φ|- symmetric control region, |∆Φ|>3π/4: Ni DY(OS;|∆Φ|< π/4) = Ni DY(OS;|∆Φ|>3π/4) Ri DY ,(5.1) where Ni DY(OS;|∆Φ|< π/4) and Ni DY(OS;|∆Φ|>3π/4) are, respectively, the numbers of DY background events in the signal and its |∆Φ|-symmetric control region; Ri DY is the transfer factor accounting for the residual asymmetry in the population of events in the two |∆Φ|regions and obtained from auxiliary measurements; and the index idenotes the dimuon category (STA-STA, STA-TMS, or TMS-TMS). The number of DY dimuons in the |∆Φ|>3π/4region is taken to be the total number of events in that region minus the expected contribution from other types of background events estimated as discussed in section 5.2. The symmetry of the |∆Φ|distributions in this class of background events is studied using data and simulated events. In the STA-STA and STA-TMS categories, we use events in the control regions obtained by reversing the STA-to-TMS association. Specifically, we select events that consist of STA-STA or, alternatively, STA-TMS dimuons passing all selection criteria, but in which each of the constituent STA muons is associated with a TMS muon. To ensure that such STA-STA and STA-TMS dimuons are promptly produced (and thus are not signal), we require that the associated TMS-TMS dimuons, which have a far superior spatial resolution, are prompt. This is achieved by requiring Lxy/σLxy <1.0 for the associated TMS-TMS dimuon in the STA-STA category, and d0/σd0<1.5for the TMS muon associated with the STA muon in the STA-TMS category. To minimize contamination from muons from jets, which we discuss separately in what follows, each TMS muon in the associated TMS-TMS dimuon is required to satisfy the isolation requirement Irel trk <0.05. Since the TMS and STA muons are predominantly reconstructed from information in different detectors (the tracker and the muon system, respectively), a genuine prompt muon – 15 – JHEP05(2023)228 0 0.5 1 1.5 2 2.5 3 |Φ∆| 1− 10 1 10 2 10 3 10 4 10 5 10 Events / 0.063 units STA-STA CMS (13 TeV) -1 61.3 fb Data Drell-Yan tt WttW+ WW+WZ+ZZ Stat. unc. 0 0.5 1 1.5 2 2.5 3 |Φ∆| 1− 10 1 10 2 10 3 10 4 10 5 10 Events / 0.063 units STA-TMS CMS (13 TeV) -1 61.3 fb Data Drell-Yan tt WttW+ WW+WZ+ZZ Stat. unc. Figure 4. Distributions of |∆Φ|for (left) STA-STA and (right) STA-TMS dimuons in 2018 data (black dots) and simulated background processes (stacked histograms), for events in the control regions with the STA-to-TMS association of the STA muons reversed, as described in the text. All nominal selection requirements, including dimuon Lxy/σLxy and TMS muon d0/σd0, are applied to the STA-STA and STA-TMS dimuons. The simulated processes are scaled to correspond to the integrated luminosity of the data. The shaded area shows the statistical uncertainty in the simulated background yield. giving rise to a displaced STA muon is usually accurately reconstructed as prompt by the TMS muon reconstruction. As a result, the aforementioned control regions contain genuine prompt dimuons that are reconstructed as displaced STA-STA or STA-TMS dimuons because of reconstruction failures or vertex fit anomalies in these categories, i.e., exactly the type of background events that we wish to study. The |∆Φ|distributions of STA-STA and STA-TMS dimuons in these control regions, in 2018 data and simulated background samples, are shown in figure 4. (The distributions in 2016 data are very similar.) The observed distributions, sculpted by the interplay between the geometric effects and the trigger and offline selection requirements, are well reproduced by the simulation. The distributions are still fairly symmetric around π/2, but there is a small asymmetry caused by the event selection criteria. Corrections accounting for this asymmetry are obtained from the ratio of events with |∆Φ|< π/4and |∆Φ|>3π/4in the aforementioned control regions, Ri DY =Nrev,i DY (OS;|∆Φ|< π/4) Nrev,i DY (OS;|∆Φ|>3π/4) .(5.2) In both STA-STA and STA-TMS categories, no dependence of the transfer factor Ri DY on mass is observed, and a single value is used for all signal mass intervals. The resulting Ri DY values only weakly depend on the dimuon category and the data-taking year, and are in the range of 0.8–0.9. The statistical uncertainties of the measurements do not exceed a few per cent. The systematic uncertainties in Ri DY are assessed by comparing Ri DY measured in individual mass intervals with the result of the inclusive measurement and by varying the boundaries and definitions of the auxiliary control regions. The latter includes repeating the measurements of RSTA-STA DY in the region with only one STA-to-TMS muon association and of RSTA-TMS DY in the region obtained by requiring Lxy/σLxy <1.5for the – 16 – JHEP05(2023)228 associated TMS-TMS dimuon. Based on these studies, we assign systematic uncertainties of 15% in RSTA-STA DY and 40% in RSTA-TMS DY . In the TMS-TMS dimuon category, the symmetry of the |∆Φ|distribution in DYlike backgrounds is assessed from events in the control region obtained by reversal of the requirement on χ2 vtx. We observe a strong correlation between vertex χ2and DCA in both data and simulated DY events, which suggests that χ2 vtx is effectively a measure of the distance between the TMS muons forming the dimuon. Further studies of TMS-TMS dimuons in DY events passing and failing the χ2 vtx requirement confirm that they differ only in how far away the two muons are reconstructed from each other, and have very similar properties otherwise. We use events in the inverted vertex χ2control region to evaluate the transfer factor RTMS-TMS DY following eq. (5.2). The measured value of RTMS-TMS DY agrees with unity within the statistical uncertainties in most mµµ intervals and d0/σd0bins. Since no systematic trends are observed, we use the value of RTMS-TMS DY = 1 at all masses and in all min(d0/σd0) bins, and assign a 15% systematic uncertainty to account for the largest deviations of RTMS-TMS DY from unity. 5.2 Estimation of nonprompt backgrounds The background evaluation method described above is based on the symmetry of the |∆Φ| distribution and does not account for the contributions from background sources that yield dimuons exclusively or predominantly at small |∆Φ|. Such background sources include: dimuon decays of nonprompt low-mass resonances such as a J/ψmeson from bhadron decay; cascade decays of bhadrons; and dimuons formed from a pair of unrelated nonprompt muons in the same jet. If well reconstructed, most such background events have mµµ not exceeding a few GeV and are rejected by the mµµ >10 GeV requirement. However, a small fraction of them with mismeasured mµµ can satisfy this requirement and pass the event selection. Such dimuons mostly have small |∆Φ|values (because the pµµ Tand Lxy vectors are collinear) and may have large, signal-like Lxy/σLxy and d0/σd0values. They are also likely to have invariant masses close to the 10GeV threshold. The other source of nonprompt background consists of dimuons formed from muons embedded in different jets. Since all these nonprompt background events arise from jets produced through the strong interaction, we collectively refer to them as quantum chromodynamics (QCD) events. To gain insight into a contribution from this class of background events to the STASTA and STA-TMS categories, we study events in control regions similar to those described above, but tailored to select events with muons embedded in jets. Once again, we invert the STA-to-TMS association and select STA-STA or, alternatively, STA-TMS dimuons passing all selection criteria, except that at least one of the constituent STA muons is associated with a TMS muon. To suppress |∆Φ|-symmetric background events such as DY as well as potential contributions from signal processes, we require each TMS muon to be nonisolated, defined as Irel trk >0.1in the STA-STA and Irel trk >0.125 in the STATMS category. According to the simulation, this requirement selects a subset of events almost entirely composed of QCD events. Figure 5(left) shows the |∆Φ|distribution of OS STA-STA dimuons in 2018 data in the samples thus obtained. Unlike the DY events in – 17 – JHEP05(2023)228 0 0.2 0.4 0.6 0.8 1 1.2 1.4 *|Φ∆| 0 50 100 150 200 250 300 350 400 Events / 0.20 units STA-STA CMS (13 TeV) -1 61.3 fb /2)π| < Φ∆| (|Φ∆ |≡*| Φ∆| /2)π| > Φ∆| (|Φ∆ |− π ≡*| Φ∆| 1 10 2 10 [GeV] TMS-TMS µµ m 10 2 10 3 10 4 10 5 10 Events / GeV STA-STA > 10 GeV STA-STA µµ m CMS (13 TeV) -1 61.3 fb Figure 5. Distributions of (left) |∆Φ∗|(defined in the legend) of STA-STA dimuons and (right) mµµ of TMS-TMS dimuons associated with STA-STA dimuons. Both distributions show STA-STA dimuons in 2018 data in the control region enriched in QCD events, as described in the text. (The distributions in 2016 data are very similar.) In the left plot, exactly one STA muon is associated with a TMS muon, while in the right plot, both are. All nominal selection requirements, including mµµ >10 GeV and Lxy/σLxy >6, are applied to the STA-STA dimuons. figure 4, which are approximately symmetric around π/2, the QCD events have a signallike peak at |∆Φ|= 0. Most of these events are genuine low-mass dimuons that are reconstructed at higher mµµ because of poor pTresolution of STA muons, and hence pass the mµµ >10 GeV requirement. This is demonstrated by figure 5(right), which shows the distribution of well-measured mµµ of TMS-TMS dimuons associated with STA-STA dimuons with mµµ >10 GeV. Many of the background processes yielding small-|∆Φ|OS dimuons also give rise to small-|∆Φ|SS dimuons, either because these processes are charge symmetric or via the muon charge misassignment. Thus, we evaluate the contribution from the QCD background to the signal region, Ni QCD(OS;|∆Φ|< π/4), from the number of small-|∆Φ|SS dimuons, Ni(SS;|∆Φ|< π/4): Ni QCD(OS;|∆Φ|< π/4) = Ni(SS;|∆Φ|< π/4) Ri QCD .(5.3) The transfer factor Ri QCD between the numbers of QCD events in these two regions is obtained from the ratio of OS to SS dimuons in the aforementioned control region with the STA-to-TMS association reversed and TMS muons not isolated: Ri QCD =Nrev,i QCD(OS;|∆Φ|< π/4) Nrev,i QCD(SS;|∆Φ|< π/4) .(5.4) Because the charge misassignment and the probability of finding the STA-to-TMS association were found to be anticorrelated, the measurement of Ri QCD in the STA-STA category is performed using dimuons with exactly one of the constituent STA muons associated with a TMS muon, providing events that are more representative of those in the signal region. Since the composition of the QCD background varies as a function of mµµ , the evaluation – 18 – JHEP05(2023)228 of Ri QCD is performed separately in the individual mass intervals, with the exception of the STA-STA category, where a common value is used for mµµ >35 GeV to avoid large statistical fluctuations of RSTA-STA QCD . The measured values of Ri QCD in these two categories vary between 1.1 and 2.3 depending on the mass interval and year, with statistical uncertainties in the range of 10–30% in the STA-STA and 2–20% in the STA-TMS category. The systematic uncertainties in RSTA-STA QCD are assessed by evaluating the potential impact on RSTA-STA QCD of the correlation between the success rate of the STA-to-TMS association and STA muon charge misassignment. The systematic uncertainties in RSTA-TMS QCD are evaluated by varying the definitions of the auxiliary control regions, e.g., performing the measurement of RSTA-TMS QCD in the region obtained by inverting the isolation requirement applied to the TMS muon. Based on these studies, we assign a systematic uncertainty in the range of 10–30% in RSTA-STA QCD , depending on the mass interval and the year, and a fixed 30% systematic uncertainty in RSTA-TMS QCD . A priori, we do not expect a large contribution from |∆Φ|-asymmetric low-mass dimuons in the TMS-TMS category, because of a far superior dimuon invariant mass resolution. Indeed, the study of simulated QCD events shows that a vast majority of both OS and SS TMS-TMS dimuons passing all selection criteria arise from a pair of unrelated nonprompt muons in two different jets. Such events do not contain genuine displaced dimuons and are expected to have a symmetric |∆Φ|distribution. Nevertheless, since some contribution from |∆Φ|-asymmetric dimuons may still be present in the background events in data, we prefer not to rely on the |∆Φ|symmetry in the evaluation of nonprompt backgrounds. Instead, similarly to the STA-STA and STA-TMS categories, we use the fact that dijet and multijet events give rise to both OS and SS dimuons, and base our estimate of the QCD background on the number of SS dimuons following eq. (5.3). The transfer factor RTMS-TMS QCD is obtained from the ratio of OS to SS dimuons in the control region with the muon isolation requirement reversed, which comprises dimuons passing the nominal event selection but with at least one muon with Irel trk >0.075 and both with Irel trk <0.5. We have verified that these events, as well as SS dimuons passing isolation requirements, contain negligible contributions from signal and DY events. As the signal region is divided into several min(d0/σd0) bins, the evaluation of RTMS-TMS QCD is performed separately in each min(d0/σd0) bin. Since no dependence of the value of RTMS-TMS QCD on mµµ is observed, RTMS-TMS QCD in each min(d0/σd0) bin is calculated by integrating events in the entire invariant mass spectrum. The measured values of RTMS-TMS QCD decrease from ≈2to ≈1as min(d0/σd0) increases, with statistical uncertainties in the range of 5–20%. A systematic uncertainty of 15% is assigned to account for variations of RTMS-TMS QCD as a function of the invariant mass and as the result of changing the definition and boundaries of the auxiliary control region. To avoid overestimating the DY background, the same QCD background evaluation method is applied to dimuons in the |∆Φ|>3π/4control region. The obtained estimate of the QCD background is then subtracted from the total observed number of OS dimuons with |∆Φ|>3π/4to obtain the number of DY dimuons in this |∆Φ|region, Ni DY(OS;|∆Φ|>3π/4), used for the evaluation of the DY backgrounds in the signal – 19 – JHEP05(2023)228 0 1 2 3 4 5 6 xy L σ/ xy L 0 1 2 3 4 Obs. / pred. σ/L 0 5 10 15 20 25 30 Events / 1 unit CMS (13 TeV) -1 36.3 fb Observed Drell-Yan (predicted) QCD (predicted) Stat. uncertainty STA-STA 0 1 2 3 4 5 6 xy L σ/ xy L 0 1 2 3 4 Obs. / pred. σ/L 0 5 10 15 20 25 30 Events / 1 unit CMS (13 TeV) -1 61.3 fb Observed Drell-Yan (predicted) QCD (predicted) Stat. uncertainty STA-STA Figure 6. Distributions of Lxy/σLxy of STA-STA dimuons in the Lxy/σLxy <6VR, in (left) 2016 and (right) 2018 data, compared to the background predictions. The observed distributions (black points with error bars) are overlayed on stacked histograms containing the expected numbers of DY (green) and QCD (yellow) background events. The lower panels show the ratio of the observed to predicted numbers of events. The shaded area shows the statistical uncertainty in the total background prediction; the admixture of the QCD background in this validation region is estimated separately and has a larger statistical uncertainty than the total background. |∆Φ|< π/4region according to eq. (5.1). This procedure is not applied in the STA-STA category, where the |∆Φ|-symmetric QCD background is negligible. The sum of the QCD and DY background estimates constitute the total predicted background in the signal region. According to the background evaluation method, the DY backgrounds are expected to dominate at small d0/σd0and Lxy/σLxy values, whereas the relative QCD contribution becomes larger as d0/σd0and Lxy/σLxy increase. The uncertainty in the background predictions is dominated by the statistical uncertainty in the numbers of events in the |∆Φ|>3π/4and SS control regions. 5.3 Validation of background predictions The background evaluation method described in sections 5.1 and 5.2 is tested in several validation regions (VRs) that are expected to contain negligible contribution from signal. The evaluation of DY backgrounds is examined in the VRs obtained by inverting the Lxy/σLxy and d0/σd0requirements and thereby enriched in this class of events. One example of such studies is shown in figure 6, which compares the background predictions to the observed distributions in the Lxy/σLxy <6VR in the STA-STA category. The yields in data are consistent with predictions of the method, which also correctly predicts a larger STA-STA background in 2016 compared to 2018 due to a lower tracking efficiency in a part of 2016 data [34]. In another check, we apply the background evaluation procedure to the TMS-TMS dimuons in the 2<min(d0/σd0)<6sideband. The comparison of the predicted background and data in bins of Lxy/σLxy is shown in figure 7. The expected and observed numbers of events are in agreement in the entire probed Lxy/σLxy range. There are more background events in 2018 data than in 2016 data because of looser trigger requirements and larger integrated luminosity. – 20 – JHEP05(2023)228 0 5 10 15 20 25 30 xy L σ/ xy L 0 0.5 1 1.5 2 Obs. / pred. 1− 10 1 10 2 10 3 10 4 10 5 10 Events / 2 units CMS (13 TeV) -1 36.3 fb Observed Drell-Yan (predicted) QCD (predicted) Stat. uncertainty TMS-TMS ) < 6 0 d σ/ 0 2 < min(d 0 5 10 15 20 25 30 xy L σ/ xy L 0 0.5 1 1.5 2 Obs. / pred. 1− 10 1 10 2 10 3 10 4 10 5 10 Events / 2 units CMS (13 TeV) -1 61.3 fb Observed Drell-Yan (predicted) QCD (predicted) Stat. uncertainty TMS-TMS ) < 6 0 d σ/ 0 2 < min(d Figure 7. Distributions of Lxy/σLxy of TMS-TMS dimuons in the 2<min(d0/σd0)<6VR, in (left) 2016 and (right) 2018 data, compared to the background predictions. The observed distributions (black points with error bars) are overlayed on stacked histograms containing the expected numbers of DY (green) and QCD (yellow) background events. The last bin includes events in the overflow. The lower panels show the ratio of the observed to predicted numbers of events. The shaded area shows the statistical uncertainty in the background prediction. The evaluation of the |∆Φ|-asymmetric component of QCD backgrounds, which is particularly important in the STA-STA category, is tested in the low-mass (6< mµµ < 10 GeV) VR, as well as in the region obtained by inverting the requirement on the minimum number of DT hits and muon segments applied to dimuons with |∆ηµµ |<0.1, referred to as the small-|∆ηµµ |VR. Using dimuons with STA muons associated with TMS muons and taking well-measured mµµ and |∆Φ|values of corresponding TMS-TMS dimuons as proxies for true values of these quantities, we have verified that the samples of events in these VRs predominantly consist of small-|∆Φ|dimuons with mismeasured mµµ . Figure 8 shows the comparison of the predicted background yields and data in these two VRs. The low-mass VR is only available in 2018 data because the trigger used to collect 2016 data for this analysis included the mµµ >10 GeV requirement. The small-|∆ηµµ |VR is available in both data sets, but since the number of events in this VR in 2016 data is small, an additional test is performed on a subset of 2018 data collected using the 2016 trigger, and therefore enriched in events similar to those recorded in 2016. The mµµ intervals of 10–32, 15–60, and 20–80 GeV shown for the small-|∆ηµµ|VRs are the intervals chosen to probe LLP masses of 20, 30, and 50 GeV, respectively. The yields in data are found to be consistent with background predictions in all tests and mµµ intervals. Finally, to ensure the validity of the method at different values of the main discriminating variable in the TMS-TMS and STA-TMS categories, the validation checks are performed in bins of d0/σd0of the TMS muon. Such checks include comparisons in the d0/σd0 sideband (d0/σd0<6) in the signal |∆Φ|region, as well as those in the entire d0/σd0range in the |∆Φ|sideband, π/4<|∆Φ|< π/2. In the latter, the region with π/4<|∆Φ|< π/2 is used as a signal-free proxy for the |∆Φ|< π/4signal region. The background evaluation procedure is applied to the OS and SS dimuons in the |∆Φ|-symmetric region, π/2<|∆Φ|<3π/4, as well as SS dimuons with π/4<|∆Φ|< π/2. The comparisons – 21 – JHEP05(2023)228 0 10 20 30 40 50 60 Events / bin STA-STA [GeV] µµ m [6, 10] [10, 32] [15, 60] [20, 80] [10, 32] [15, 60] [20, 80] [10, 32] [15, 60] [20, 80] 2018 2018 2018 (2016 HLT) 2016 µµ low-m | µµ η∆small-| | µµ η∆small-| | µµ η∆small-| CMS (13 TeV) -1 97.6 fb Observed QCD (predicted) Stat. uncertainty Figure 8. Comparison of observed (black points with error bars) and predicted (histograms) yields of STA-STA dimuons in the validation regions enriched in QCD background events. The first bin shows the yields in the low-mass VR in 2018 data. The other three groups of bins, separated by solid lines, show the yields in the small-|∆ηµµ |VR, in (from left to right) the entire 2018 data set, a subset of 2018 data enriched in events collected in 2016, and the 2016 data set. Each of these three VRs is further subdivided into three mµµ intervals, 10–32, 15–60, and 20–80 GeV. The expected number of background events is computed according to eqs. (5.3) and (5.4), separately in each mµµ bin. The shaded area shows the statistical uncertainty in the background prediction. of the predicted background and 2018 data in the TMS-TMS and STA-TMS categories in this VR are shown in figure 9. The observed and expected numbers of events are consistent within statistical uncertainties. 6 Systematic uncertainties affecting the signal Most of the systematic uncertainties affecting the signal efficiencies are evaluated separately in each dimuon category and for each data-taking year. Unless stated otherwise, we consider sources of uncertainties to be uncorrelated among different dimuon categories and years. In the STA-STA and STA-TMS categories, the dominant systematic uncertainties come from the STA muon identification and trigger efficiencies. At small displacements, both efficiencies are accurately measured as a function of muon pTand ηby applying the “tagand-probe method” [28] to muons from J/ψmeson and Zboson decays. The differences in the identification and trigger efficiencies between data and simulation are used to correct the signal simulation yields. In the STA-STA category, these corrections range from 0.78 to 1.13, depending on the signal sample. The evolution of efficiencies with displacement is studied using a sample of cosmic ray muons collected during periods with no beam, and additional d0-dependent corrections and systematic uncertainties are derived. At d0= 10 – 22 – JHEP05(2023)228 0 5 10 15 20 25 30 ) 0 d σ/ 0 min(d 0 0.5 1 1.5 2 Obs. / pred. 1− 10 1 10 2 10 3 10 4 10 5 10 Events / 2 units CMS (13 TeV) -1 61.3 fb Observed Drell-Yan (predicted) QCD (predicted) Stat. uncertainty TMS-TMS /2π| < Φ∆/4 < |π 0 5 10 15 20 25 30 0 d σ/ 0 d 0 0.5 1 1.5 2 2.5 Obs. / pred. 1− 10 1 10 2 10 3 10 4 10 Events / 2 units CMS (13 TeV) -1 61.3 fb Observed Drell-Yan (predicted) QCD (predicted) Stat. uncertainty STA-TMS /2π| < Φ∆/4 < |π Figure 9. Distributions in the π/4<|∆Φ|< π/2VR in 2018 data: (left) the smaller of the two d0/σd0values for the TMS-TMS dimuon; (right) d0/σd0of the TMS muon in the STA-TMS dimuon. The observed distributions (black points with error bars) are compared to the results of the background prediction method applied to events with π/2<|∆Φ|<3π/4. The stacked histograms show the expected numbers of DY (green) and QCD (yellow) background events. The last bin includes events in the overflow. The lower panels show the ratios of the observed to predicted numbers of events. The shaded area shows the statistical uncertainty in the background prediction. (100)cm, the correction amounts to 0.99 (0.96) per muon, whereas the uncertainty is on the order of 10 (35)%. Since the d0-dependent uncertainty is dominated by the accuracy of the L1 trigger efficiency measurements, it is taken to be correlated among all dimuon categories. The dominant systematic uncertainties in the TMS-TMS category come from the d0 dependence of the L1 trigger efficiency discussed above and the efficiency to reconstruct displaced muons in the tracker. The evolution of the tracking efficiency with d0is measured using a sample of cosmic ray muons and compared to the tracking efficiency predicted by simulation. Based on the results of the comparison, we assign a 5% systematic uncertainty per muon for muons with d0>1cm. The overall efficiency corrections applied to the simulated signal yields range from 0.74 to 1.08, depending on the signal sample, and arise mostly from imperfect modeling of the HLT efficiencies at small displacements. The remaining systematic uncertainties related to the signal efficiency are much smaller. The impact of mismodeling of the muon pTresolution on the signal yield is evaluated by smearing the muon pTin simulated signal events according to the measurements performed using cosmic ray muons and muons from Zboson decays. This leads to variations that are less than 2% at all signal masses except for m(ZD) = 10 GeV. Corrections of up to 2% are applied to the TMS muon efficiency to account for the difference in efficiency of isolation requirements measured using muons from Zboson decays, and an additional systematic uncertainty of 2% is assigned. A systematic uncertainty ranging from 1 to 8%, depending on the signal sample, is assigned to account for mismodeling of the DCA requirement in the STA-STA category. The efficiency of the vertex χ2requirement as a function of displacement is studied using cosmic ray muons and muons from Z – 23 – JHEP05(2023)228 boson decays in the STA-STA category, and muons from decays of nonprompt J/ψmesons in the TMS-TMS category. The differences between data and simulation contribute an uncertainty of 2% in each category. The efficiencies of several other selection criteria, such as requirements on the number of tracker hits upstream of the vertex position and the difference between the number of pixel hits on two TMS muons, are found to be well modeled by simulation, and no additional uncertainty is assigned. The uncertainty in the integrated luminosity, partially correlated between the years, is 1.2% in 2016 [15] and 2.5% in 2018 [16]. The uncertainty in the signal efficiency due to pileup is 2%. Both uncertainties are correlated among dimuon categories. 7 Results The predicted background yields in the representative mµµ intervals and the corresponding numbers of observed events are shown in figure 10 for the STA-STA category and figure 11 for the STA-TMS category. For illustration, signals at the level of the median expected exclusion limits at 95% confidence level (CL) in the absence of signal are also shown. As expected, events observed in the STA-STA and STA-TMS categories are predominantly at low masses—14 out of 18 STA-STA and 9 out of 13 STA-TMS events have mµµ <20 GeV— and have characteristics typical of those for QCD background events. The numbers of observed events and the predicted background and signal yields in the TMS-TMS category are shown in figure 12 as functions of mµµ in each of the three min(d0/σd0) bins and in figure 13 as a function of min(d0/σd0). The observed TMS-TMS events have a steeply falling min(d0/σd0)distribution and cluster at mµµ values of a few tens of GeV, which are both consistent with the characteristics of the expected background. The numbers of observed events are consistent with the predicted background yields in all dimuon categories and mµµ intervals, in both data sets. No significant excess of events above the SM background is observed. For each of the two benchmark models, we compute upper limits on the product of the signal production cross section σand the branching fraction Bto two muons as a function of mass and mean proper decay length. The limit extraction is based on a modified frequentist approach [35,36] and uses the CMS Combine package developed for statistically combining the results of Higgs boson searches [37]. The method yielding background predictions in the signal region is implemented using a multibin likelihood, which is a product of Poisson distributions corresponding to the signal region and the control regions. The systematic uncertainties affecting the signal yield are incorporated as nuisance parameters using log-normal distributions. The expected and observed upper limits are evaluated through the use of simulated pseudo-experiments. For each signal model, the limits are first computed separately in each dimuon category and for each datataking year. The individual likelihoods are then combined to obtain the combined limits. The signal efficiencies are obtained from simulation and further corrected by the datato-simulation scale factors described in section 6; they are computed separately for each year, signal model, dimuon category, and mass interval. A reweighting procedure is employed to calculate an estimated number of signal events for lifetimes other than the life- – 24 – JHEP05(2023)228 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 [cm]τc 5− 10 4− 10 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 ) [pb]µµ→ D (Z Β ) D Z D Z→(Hσ 95% CL upper limits: = 125 GeV H m = 10 GeV D Z m ) = 1% D Z D Z→(H Β ) = 0.1% D Z D Z→(H Β ) = 0.01% D Z D Z→(H Β ) = 0.001% D Z D Z→(H Β ) = 0.144µµ→ D (Z Β CMS (13 TeV) -1 97.6 fb Combined observed Combined expected (median) Combined expected (68% quantile) Combined expected (95% quantile) STA-STA expected (median) TMS-TMS expected (median) STA-TMS expected (median) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 [cm]τc 5− 10 4− 10 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 ) [pb]µµ→ D (Z Β ) D Z D Z→(Hσ 95% CL upper limits: = 125 GeV H m = 20 GeV D Z m ) = 1% D Z D Z→(H Β ) = 0.1% D Z D Z→(H Β ) = 0.01% D Z D Z→(H Β ) = 0.001% D Z D Z→(H Β ) = 0.143µµ→ D (Z Β CMS (13 TeV) -1 97.6 fb Combined observed Combined expected (median) Combined expected (68% quantile) Combined expected (95% quantile) STA-STA expected (median) TMS-TMS expected (median) STA-TMS expected (median) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 [cm]τc 5− 10 4− 10 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 ) [pb]µµ→ D (Z Β ) D Z D Z→(Hσ 95% CL upper limits: = 125 GeV H m = 30 GeV D Z m ) = 1% D Z D Z→(H Β ) = 0.1% D Z D Z→(H Β ) = 0.01% D Z D Z→(H Β ) = 0.001% D Z D Z→(H Β ) = 0.140µµ→ D (Z Β CMS (13 TeV) -1 97.6 fb Combined observed Combined expected (median) Combined expected (68% quantile) Combined expected (95% quantile) STA-STA expected (median) TMS-TMS expected (median) STA-TMS expected (median) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 [cm]τc 5− 10 4− 10 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 ) [pb]µµ→ D (Z Β ) D Z D Z→(Hσ 95% CL upper limits: = 125 GeV H m = 40 GeV D Z m ) = 1% D Z D Z→(H Β ) = 0.1% D Z D Z→(H Β ) = 0.01% D Z D Z→(H Β ) = 0.001% D Z D Z→(H Β ) = 0.134µµ→ D (Z Β CMS (13 TeV) -1 97.6 fb Combined observed Combined expected (median) Combined expected (68% quantile) Combined expected (95% quantile) STA-STA expected (median) TMS-TMS expected (median) STA-TMS expected (median) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 [cm]τc 5− 10 4− 10 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 ) [pb]µµ→ D (Z Β ) D Z D Z→(Hσ 95% CL upper limits: = 125 GeV H m = 50 GeV D Z m ) = 1% D Z D Z→(H Β ) = 0.1% D Z D Z→(H Β ) = 0.01% D Z D Z→(H Β ) = 0.001% D Z D Z→(H Β ) = 0.122µµ→ D (Z Β CMS (13 TeV) -1 97.6 fb Combined observed Combined expected (median) Combined expected (68% quantile) Combined expected (95% quantile) STA-STA expected (median) TMS-TMS expected (median) STA-TMS expected (median) 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 [cm]τc 5− 10 4− 10 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 ) [pb]µµ→ D (Z Β ) D Z D Z→(Hσ 95% CL upper limits: = 125 GeV H m = 60 GeV D Z m ) = 1% D Z D Z→(H Β ) = 0.1% D Z D Z→(H Β ) = 0.01% D Z D Z→(H Β ) = 0.001% D Z D Z→(H Β ) = 0.103µµ→ D (Z Β CMS (13 TeV) -1 97.6 fb Combined observed Combined expected (median) Combined expected (68% quantile) Combined expected (95% quantile) STA-STA expected (median) TMS-TMS expected (median) STA-TMS expected (median) Figure 18. The 95% CL upper limits on σ(H →ZDZD)B(ZD→µµ)as a function of cτ(ZD) in the HAHM model, for m(ZD)ranging from 10 GeV (upper left) to 60GeV (lower right). The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of B(H →ZDZD)indicated next to the lines. – 31 – JHEP05(2023)228 10 20 30 40 50 60 ) [GeV] D m(Z 3− 10 2− 10 1− 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8 10 [cm]τc 95% CL exclusion contours CMS (13 TeV) -1 97.6 fb ) = 1% D Z D Z→(H Β ) = 0.1% D Z D Z→(H Β ) = 0.05% D Z D Z→(H Β ) = 0.01% D Z D Z→(H Β ) = 0.005% D Z D Z→(H Β 10 20 30 40 50 60 ) [GeV] D m(Z 10− 10 9− 10 8− 10 7− 10 6− 10 5− 10 4− 10 3− 10 ε 95% CL exclusion contours CMS (13 TeV) -1 97.6 fb ) = 1% D Z D Z→(H Β ) = 0.1% D Z D Z→(H Β ) = 0.05% D Z D Z→(H Β ) = 0.01% D Z D Z→(H Β ) = 0.005% D Z D Z→(H Β Figure 19. Observed 95% CL exclusion contours in the HAHM model, in the (left) (m(ZD), cτ(ZD)) and (right) (m(ZD),) planes. The contours correspond to several representative values of B(H →ZDZD)ranging from 0.005 to 1%. 8 Summary Data collected by the CMS experiment in proton-proton collisions at √s= 13 TeV in 2016 and 2018 and corresponding to an integrated luminosity of 97.6 fb−1have been used to conduct an inclusive search for long-lived exotic neutral particles (LLPs) decaying to a pair of oppositely charged muons. The search is largely model-independent and is sensitive to a broad range of LLP lifetimes and masses. No significant excess of events above the standard model background is observed. The results are interpreted as limits on the parameters of the hidden Abelian Higgs model, in which the Higgs boson decays to a pair of long-lived dark photons ZD, and of a simplified model, in which LLPs are produced in decays of an exotic heavy neutral scalar boson. In the mass range 20 < m(ZD)<60 GeV, a branching fraction of the Higgs boson to dark photons of 1% is excluded at 95% confidence level in the range of proper decay length cτ(ZD)from a few tens of µm to ≈100 m. The results of this search significantly extend the previously excluded range of model parameters. For the hidden Abelian Higgs model with m(ZD)greater than 20GeV and less than half the mass of the Higgs boson, they provide the best limits to date on the branching fraction of the Higgs boson to dark photons for cτ(ZD)(varying with m(ZD)) between 0.03 and ≈0.5mm, and above ≈0.5m. At exotic scalar boson masses larger than the Higgs boson mass, our results represent the best current constraints for all considered LLP masses and lifetimes. 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 – 32 – JHEP05(2023)228 construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: BMBWF 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); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); J MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); 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, 884104, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science — EOS" — be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Deutsche Forschungsgemeinschaft (DFG), under Germany’s Excellence Strategy — EXC 2121 “Quantum Universe" — 390833306, and under project number 400140256 — GRK2497; the Hungarian Academy of Sciences, the New National Excellence Program — ÚNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundação para a Ciência e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe", and the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B05F650021 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA). – 33 – JHEP05(2023)228 Open Access. 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Tavernier , W. Van Doninck, D. Vannerom Université Libre de Bruxelles, Bruxelles, Belgium B. Clerbaux , G. De Lentdecker , L. Favart , J. Jaramillo , K. Lee , M. Mahdavikhorrami , I. Makarenko , A. Malara , S. Paredes , L. Pétré , N. Postiau, E. Starling , L. Thomas , M. Vanden Bemden, C. Vander Velde , P. Vanlaer Ghent University, Ghent, Belgium D. Dobur , J. Knolle , L. Lambrecht , G. Mestdach, M. Niedziela , C. Rendón, C. Roskas , A. Samalan, K. Skovpen , M. Tytgat , N. Van Den Bossche , B. Vermassen, L. Wezenbeek Université Catholique de Louvain, Louvain-la-Neuve, Belgium A. Benecke , A. Bethani , G. Bruno , F. Bury , C. Caputo , P. David , C. Delaere , I.S. Donertas , A. Giammanco , K. Jaffel , Sa. Jain , V. Lemaitre, K. Mondal , J. Prisciandaro, A. Taliercio , T.T. Tran , P. Vischia , S. Wertz Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil G.A. Alves , E. Coelho , C. Hensel , A. Moraes , P. Rebello Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil W.L. Aldá Júnior , M. Alves Gallo Pereira , M. Barroso Ferreira Filho , H. Brandao Malbouisson , W. Carvalho , J. Chinellato6, E.M. Da Costa , G.G. Da Silveira7, D. De Jesus Damiao , V. Dos Santos Sousa , S. Fonseca De Souza , J. Martins8, C. Mora Herrera , K. Mota Amarilo , L. Mundim , H. Nogima , A. Santoro , S.M. Silva Do Amaral , A. Sznajder , M. Thiel , F. Torres Da Silva De Araujo9, A. Vilela Pereira – 37 – JHEP05(2023)228 Universidade Estadual Paulista, Universidade Federal do ABC, São Paulo, Brazil C.A. Bernardes7, L. Calligaris , T.R. Fernandez Perez Tomei , E.M. Gregores , P.G. Mercadante , S.F. Novaes , Sandra S. Padula Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia, Bulgaria A. Aleksandrov , G. Antchev , R. Hadjiiska , P. Iaydjiev , M. Misheva , M. Rodozov, M. Shopova , G. Sultanov University of Sofia, Sofia, Bulgaria A. Dimitrov , T. Ivanov , L. Litov , B. Pavlov , P. Petkov , A. Petrov, E. Shumka Beihang University, Beijing, China T. Cheng , T. Javaid10, M. Mittal , L. Yuan Department of Physics, Tsinghua University, Beijing, China M. Ahmad , G. Bauer11, Z. Hu , S. Lezki , K. Yi11,12 Institute of High Energy Physics, Beijing, China G.M. Chen10 , H.S. Chen10 , M. Chen10 , F. Iemmi , C.H. Jiang, A. Kapoor , H. Liao , Z.-A. Liu13 , V. Milosevic , F. Monti , R. Sharma , J. Tao , J. ThomasWilsker , J. Wang , H. Zhang , J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China A. Agapitos , Y. An , Y. Ban , C. Chen, A. Levin , Q. Li , X. Lyu, Y. Mao, S.J. Qian , X. Sun , D. Wang , J. Xiao , H. Yang Sun Yat-Sen University, Guangzhou, China M. Lu , Z. You Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ionbeam Application (MOE) - Fudan University, Shanghai, China X. Gao5, D. Leggat, H. Okawa , Y. Zhang Zhejiang University, Hangzhou, Zhejiang, China Z. Lin , C. Lu , M. Xiao Universidad de Los Andes, Bogota, Colombia C. Avila , D.A. Barbosa Trujillo, A. Cabrera , C. Florez , J. Fraga Universidad de Antioquia, Medellin, Colombia J. Mejia Guisao , F. Ramirez , M. Rodriguez , J.D. Ruiz Alvarez University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia D. Giljanovic , N. Godinovic , D. Lelas , I. Puljak – 38 – JHEP05(2023)228 University of Split, Faculty of Science, Split, Croatia Z. Antunovic, M. Kovac , T. Sculac Institute Rudjer Boskovic, Zagreb, Croatia V. Brigljevic , B.K. Chitroda , D. Ferencek , D. Majumder , M. Roguljic , A. Starodumov14 , T. Susa University of Cyprus, Nicosia, Cyprus A. Attikis , K. Christoforou , G. Kole , M. Kolosova , S. Konstantinou , J. Mousa , C. Nicolaou, F. Ptochos , P.A. Razis , H. Rykaczewski, H. Saka Charles University, Prague, Czech Republic M. Finger14 , M. Finger Jr.14 , A. Kveton Escuela Politecnica Nacional, Quito, Ecuador E. Ayala Universidad San Francisco de Quito, Quito, Ecuador E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt S. Elgammal15, A. Ellithi Kamel16 Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt M. Abdullah Al-Mashad , M.A. Mahmoud National Institute of Chemical Physics and Biophysics, Tallinn, Estonia S. Bhowmik , R.K. Dewanjee , K. Ehataht , M. Kadastik, S. Nandan , C. Nielsen , J. Pata , M. Raidal , L. Tani , C. Veelken Department of Physics, University of Helsinki, Helsinki, Finland P. Eerola , H. Kirschenmann , K. Osterberg , M. Voutilainen Helsinki Institute of Physics, Helsinki, Finland S. Bharthuar , E. Brücken , F. Garcia , J. Havukainen , M.S. Kim , R. Kinnunen, T. Lampén , K. Lassila-Perini , S. Lehti , T. Lindén , M. Lotti, L. Martikainen , M. Myllymäki , J. Ott , M.m. Rantanen , H. Siikonen , E. Tuominen , J. Tuominiemi Lappeenranta-Lahti University of Technology, Lappeenranta, Finland P. Luukka , H. Petrow , T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France C. Amendola , M. Besancon , F. Couderc , M. Dejardin , D. Denegri, J.L. Faure, F. Ferri , S. Ganjour , P. Gras , G. Hamel de Monchenault , P. Jarry , V. Lohezic , J. Malcles , J. Rander, A. Rosowsky , M.Ö. Sahin , A. Savoy-Navarro17 , P. Simkina , M. Titov – 39 – JHEP05(2023)228 Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France C. Baldenegro Barrera , F. Beaudette , A. Buchot Perraguin , P. Busson , A. Cappati , C. Charlot , O. Davignon , B. Diab , G. Falmagne , B.A. Fontana Santos Alves , S. Ghosh , R. Granier de Cassagnac , A. Hakimi , B. Harikrishnan , J. Motta , M. Nguyen , C. Ochando , L. Portales , J. Rembser , R. Salerno , U. Sarkar , J.B. Sauvan , Y. Sirois , A. Tarabini , E. Vernazza , A. Zabi , A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France J.-L. Agram18 , J. Andrea, D. Apparu , D. Bloch , G. Bourgatte, J.-M. Brom , E.C. Chabert , C. Collard , D. Darej, U. Goerlach , C. Grimault, A.-C. Le Bihan , E. Nibigira , P. Van Hove Institut de Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France S. Beauceron , C. Bernet , G. Boudoul , C. Camen, A. Carle, N. Chanon , J. Choi , D. Contardo , P. Depasse , C. Dozen19 , H. El Mamouni, J. Fay , S. Gascon , M. Gouzevitch , G. Grenier , B. Ille , I.B. Laktineh, M. Lethuillier , L. Mirabito, S. Perries, K. Shchablo, V. Sordini , L. Torterotot , M. Vander Donckt , P. Verdier , S. Viret Georgian Technical University, Tbilisi, Georgia D. Chokheli , I. Lomidze , Z. Tsamalaidze14 RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany V. Botta , L. Feld , K. Klein , M. Lipinski , D. Meuser , A. Pauls , N. Röwert , M. Teroerde RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany S. Diekmann , A. Dodonova , N. Eich , D. Eliseev , M. Erdmann , P. Fackeldey , B. Fischer , T. Hebbeker , K. Hoepfner , F. Ivone , M.y. Lee , L. Mastrolorenzo, M. Merschmeyer , A. Meyer , S. Mondal , S. Mukherjee , D. Noll , A. Novak , F. Nowotny, A. Pozdnyakov , Y. Rath, H. Reithler , A. Schmidt , S.C. Schuler, A. Sharma , L. Vigilante, S. Wiedenbeck , S. Zaleski RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany C. Dziwok , G. Flügge , W. Haj Ahmad20 , O. Hlushchenko, T. Kress , A. Nowack , O. Pooth , A. Stahl21 , T. Ziemons , A. Zotz Deutsches Elektronen-Synchrotron, Hamburg, Germany H. Aarup Petersen, M. Aldaya Martin , P. Asmuss, S. Baxter , M. Bayatmakou , O. Behnke, A. Bermúdez Martínez , S. Bhattacharya , A.A. Bin Anuar , F. Blekman22 , K. Borras23 , D. Brunner , A. Campbell , A. Cardini , C. Cheng, F. Colombina, S. Consuegra Rodríguez , G. Correia Silva , M. De Silva , L. Didukh , G. Eckerlin, D. Eckstein, L.I. Estevez Banos , O. Filatov , E. Gallo22 , A. Geiser , A. Giraldi , G. Greau, A. Grohsjean , V. Guglielmi , M. Guthoff , A. Jafari24 , N.Z. Jomhari , B. Kaech , A. Kasem23 , M. Kasemann , H. Kaveh , – 40 – JHEP05(2023)228 Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa, Portugal M. Araujo , P. Bargassa , D. Bastos , A. Boletti , P. Faccioli , M. Gallinaro , J. Hollar , N. Leonardo , T. Niknejad , M. Pisano , J. Seixas , O. Toldaiev , J. Varela VINCA Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia P. Adzic55 , M. Dordevic , P. Milenovic , J. Milosevic Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain M. Aguilar-Benitez, J. Alcaraz Maestre , A. Álvarez Fernández , M. Barrio Luna, Cristina F. Bedoya , C.A. Carrillo Montoya , M. Cepeda , M. Cerrada , N. Colino , B. De La Cruz , A. Delgado Peris , J.P. Fernández Ramos , J. Flix , M.C. Fouz , O. Gonzalez Lopez , S. Goy Lopez , J.M. Hernandez , M.I. Josa , J. León Holgado , D. Moran , C. Perez Dengra , A. Pérez-Calero Yzquierdo , J. Puerta Pelayo , I. Redondo , D.D. Redondo Ferrero , L. Romero, S. Sánchez Navas , J. Sastre , L. Urda Gómez , J. Vazquez Escobar , C. Willmott Universidad Autónoma de Madrid, Madrid, Spain J.F. de Trocóniz Universidad de Oviedo, Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), Oviedo, Spain B. Alvarez Gonzalez , J. Cuevas , J. Fernandez Menendez , S. Folgueras , I. Gonzalez Caballero , J.R. González Fernández , E. Palencia Cortezon , C. Ramón Álvarez , V. Rodríguez Bouza , A. Soto Rodríguez , A. Trapote , N. Trevisani , C. Vico Villalba Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain J.A. Brochero Cifuentes , I.J. Cabrillo , A. Calderon , J. Duarte Campderros , M. Fernandez , C. Fernandez Madrazo , P.J. Fernández Manteca , A. García Alonso, G. Gomez , C. Lasaosa García , C. Martinez Rivero , P. Martinez Ruiz del Arbol , F. Matorras , P. Matorras Cuevas , J. Piedra Gomez , C. Prieels, A. Ruiz-Jimeno , L. Scodellaro , I. Vila , J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka M.K. Jayananda , B. Kailasapathy56 , D.U.J. Sonnadara , D.D.C. Wickramarathna University of Ruhuna, Department of Physics, Matara, Sri Lanka W.G.D. Dharmaratna , K. Liyanage , N. Perera , N. Wickramage CERN, European Organization for Nuclear Research, Geneva, Switzerland D. Abbaneo , J. Alimena , E. Auffray , G. Auzinger , J. Baechler, P. Baillon†, D. Barney , J. Bendavid , M. Bianco , B. Bilin , A. Bocci , E. Brondolin , C. Caillol , T. Camporesi , G. Cerminara , N. Chernyavskaya , S.S. Chhibra , S. Choudhury, – 47 – JHEP05(2023)228 M. Cipriani , L. Cristella , D. d’Enterria , A. Dabrowski , A. David , A. De Roeck , M.M. Defranchis , M. Deile , M. Dobson , M. Dünser , N. Dupont, A. ElliottPeisert, F. Fallavollita57, A. Florent , L. Forthomme , G. Franzoni , W. Funk , S. Ghosh , S. Giani, D. Gigi, K. Gill, F. Glege , L. Gouskos , E. Govorkova , M. Haranko , J. Hegeman , V. Innocente , T. James , P. Janot , J. Kaspar , J. Kieseler , M. Komm , N. Kratochwil , S. Laurila , P. Lecoq , A. Lintuluoto , C. Lourenço , B. Maier , L. Malgeri , M. Mannelli , A.C. Marini , F. Meijers , S. Mersi , E. Meschi , F. Moortgat , M. Mulders , S. Orfanelli, L. Orsini, F. Pantaleo , E. Perez, M. Peruzzi , A. Petrilli , G. Petrucciani , A. Pfeiffer , M. Pierini , D. Piparo , M. Pitt , H. Qu , T. Quast, D. Rabady , A. Racz, G. Reales Gutiérrez, M. Rovere , H. Sakulin , J. Salfeld-Nebgen , S. Scarfi, M. Selvaggi , A. Sharma , P. Silva , W. Snoeys , P. Sphicas58 , A.G. Stahl Leiton , S. Summers , K. Tatar , V.R. Tavolaro , D. Treille , P. Tropea , A. Tsirou, J. Wanczyk59 , K.A. Wozniak , W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland L. Caminada60 , A. Ebrahimi , W. Erdmann , R. Horisberger , Q. Ingram , H.C. Kaestli , D. Kotlinski , C. Lange , M. Missiroli60 , L. Noehte60 , T. Rohe ETH Zurich - Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland T.K. Aarrestad , K. Androsov59 , M. Backhaus , P. Berger, A. Calandri , A. De Cosa , G. Dissertori , M. Dittmar, M. Donegà , F. Eble , M. Galli , K. Gedia , F. Glessgen , T.A. Gómez Espinosa , C. Grab , D. Hits , W. Lustermann , A.-M. Lyon , R.A. Manzoni , L. Marchese , C. Martin Perez , A. Mascellani59 , M.T. Meinhard , F. Nessi-Tedaldi , J. Niedziela , F. Pauss , V. Perovic , S. Pigazzini , M.G. Ratti , M. Reichmann , C. Reissel , T. Reitenspiess , B. Ristic , D. Ruini, D.A. Sanz Becerra , J. Steggemann59 , D. Valsecchi21 , R. Wallny Universität Zürich, Zurich, Switzerland C. Amsler61 , P. Bärtschi , C. Botta , D. Brzhechko, M.F. Canelli , K. Cormier , A. De Wit , R. Del Burgo, J.K. Heikkilä , M. Huwiler , W. Jin , A. Jofrehei , B. Kilminster , S. Leontsinis , S.P. Liechti , A. Macchiolo , P. Meiring , V.M. Mikuni , U. Molinatti , I. Neutelings , A. Reimers , P. Robmann, S. Sanchez Cruz , K. Schweiger , M. Senger , Y. Takahashi National Central University, Chung-Li, Taiwan C. Adloff62, C.M. Kuo, W. Lin, S.S. Yu National Taiwan University (NTU), Taipei, Taiwan L. Ceard, Y. Chao , K.F. Chen , P.s. Chen, H. Cheng , W.-S. Hou , Y.y. Li , R.- S. Lu , E. Paganis , A. Psallidas, A. Steen , H.y. Wu, E. Yazgan , P.r. Yu Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok, Thailand C. Asawatangtrakuldee , N. Srimanobhas – 48 – JHEP05(2023)228 Çukurova University, Physics Department, Science and Art Faculty, Adana, Turkey D. Agyel , F. Boran , Z.S. Demiroglu , F. Dolek , I. Dumanoglu63 , E. Eskut, Y. Guler64 , E. Gurpinar Guler64 , C. Isik , O. Kara, A. Kayis Topaksu , U. Kiminsu , G. Onengut , K. Ozdemir65 , A. Polatoz , A.E. Simsek , B. Tali66 , U.G. Tok , S. Turkcapar , E. Uslan , I.S. Zorbakir Middle East Technical University, Physics Department, Ankara, Turkey G. Karapinar, K. Ocalan67 , M. Yalvac68 Bogazici University, Istanbul, Turkey B. Akgun , I.O. Atakisi , E. Gülmez , M. Kaya69 , O. Kaya70 , Ö. Özçelik , S. Tekten71 Istanbul Technical University, Istanbul, Turkey A. Cakir , K. Cankocak63 , Y. Komurcu , S. Sen72 Istanbul University, Istanbul, Turkey O. Aydilek , S. Cerci66 , B. Hacisahinoglu , I. Hos73 , B. Isildak74 , B. Kaynak , S. Ozkorucuklu , C. Simsek , D. Sunar Cerci66 Institute for Scintillation Materials of National Academy of Science of Ukraine, Kharkiv, Ukraine B. Grynyov National Science Centre, Kharkiv Institute of Physics and Technology, Kharkiv, Ukraine L. Levchuk University of Bristol, Bristol, United Kingdom D. Anthony , E. Bhal , J.J. Brooke , A. Bundock , E. Clement , D. Cussans , H. Flacher , M. Glowacki, J. Goldstein , G.P. Heath, H.F. Heath , L. Kreczko , B. Krikler , S. Paramesvaran , S. Seif El Nasr-Storey, V.J. Smith , N. Stylianou75 , K. Walkingshaw Pass, R. White Rutherford Appleton Laboratory, Didcot, United Kingdom A.H. Ball, K.W. Bell , A. Belyaev76 , C. Brew , R.M. Brown , D.J.A. Cockerill , C. Cooke , K.V. Ellis, K. Harder , S. Harper , M.-L. Holmberg77 , J. Linacre , K. Manolopoulos, D.M. Newbold , E. Olaiya, D. Petyt , T. Reis , T. Schuh, C.H. Shepherd-Themistocleous , I.R. Tomalin, T. Williams Imperial College, London, United Kingdom R. Bainbridge , P. Bloch , S. Bonomally, J. Borg , S. Breeze, C.E. Brown , O. Buchmuller, V. Cacchio, V. Cepaitis , G.S. Chahal78 , D. Colling , J.S. Dancu, P. Dauncey , G. Davies , J. Davies, M. Della Negra , S. Fayer, G. Fedi , G. Hall , M.H. Hassanshahi , A. Howard, G. Iles , J. Langford , L. Lyons , A.-M. Magnan , S. Malik, A. Martelli , M. Mieskolainen , D.G. Monk , J. Nash79 , M. Pesaresi, B.C. Radburn-Smith , D.M. Raymond, A. Richards, A. Rose , E. Scott , C. Seez , – 49 – JHEP05(2023)228 A. Shtipliyski, R. Shukla , A. Tapper , K. Uchida , G.P. Uttley , L.H. Vage, T. Virdee21 , M. Vojinovic , N. Wardle , S.N. Webb , D. Winterbottom Brunel University, Uxbridge, United Kingdom K. Coldham, J.E. Cole , A. Khan, P. Kyberd , I.D. Reid , L. Teodorescu Baylor University, Waco, Texas, USA S. Abdullin , A. Brinkerhoff , B. Caraway , J. Dittmann , K. Hatakeyama , A.R. Kanuganti , B. McMaster , M. Saunders , S. Sawant , C. Sutantawibul , J. Wilson Catholic University of America, Washington, DC, USA R. Bartek , A. Dominguez , R. Uniyal , A.M. Vargas Hernandez The University of Alabama, Tuscaloosa, Alabama, USA A. Buccilli , S.I. Cooper , D. Di Croce , S.V. Gleyzer , C. Henderson , C.U. Perez , P. Rumerio80 , C. West Boston University, Boston, Massachusetts, USA A. Akpinar , A. Albert , D. Arcaro , C. Cosby , Z. Demiragli , C. Erice , E. Fontanesi , D. Gastler , S. May , J. Rohlf , K. Salyer , D. Sperka , D. Spitzbart , I. Suarez , A. Tsatsos , S. Yuan Brown University, Providence, Rhode Island, USA G. Benelli , B. Burkle , X. Coubez23, D. Cutts , M. Hadley , U. Heintz , J.M. Hogan81 , T. Kwon , G. Landsberg , K.T. Lau , D. Li, J. Luo , M. Narain, N. Pervan , S. Sagir82 , F. Simpson , E. Usai , W.Y. Wong, X. Yan , D. Yu , W. Zhang University of California, Davis, Davis, California, USA J. Bonilla , C. Brainerd , R. Breedon , M. Calderon De La Barca Sanchez , M. Chertok , J. Conway , P.T. Cox , R. Erbacher , G. Haza , F. Jensen , O. Kukral , G. Mocellin , M. Mulhearn , D. Pellett , B. Regnery , D. Taylor , Y. Yao , F. Zhang University of California, Los Angeles, California, USA M. Bachtis , R. Cousins , A. Dasgupta, A. Datta , D. Hamilton , J. Hauser , M. Ignatenko , M.A. Iqbal , T. Lam , W.A. Nash , S. Regnard , D. Saltzberg , B. Stone , V. Valuev University of California, Riverside, Riverside, California, USA Y. Chen, R. Clare , J.W. Gary , M. Gordon, G. Hanson , G. Karapostoli , O.R. Long , N. Manganelli , W. Si , S. Wimpenny University of California, San Diego, La Jolla, California, USA J.G. Branson, P. Chang , S. Cittolin, S. Cooperstein , D. Diaz , J. Duarte , R. Gerosa , L. Giannini , J. Guiang , R. Kansal , V. Krutelyov , R. Lee , J. Letts , M. Masciovecchio , F. Mokhtar , M. Pieri , B.V. Sathia Narayanan , V. Sharma , M. Tadel , F. Würthwein , Y. Xiang , A. Yagil – 50 – JHEP05(2023)228 University of California, Santa Barbara - Department of Physics, Santa Barbara, California, USA N. Amin, C. Campagnari , M. Citron , G. Collura , A. Dorsett , V. Dutta , J. Incandela , M. Kilpatrick , J. Kim , A.J. Li , B. Marsh, P. Masterson , H. Mei , M. Oshiro , M. Quinnan , J. Richman , U. Sarica , R. Schmitz , F. Setti , J. Sheplock , P. Siddireddy, D. Stuart , S. Wang California Institute of Technology, Pasadena, California, USA A. Bornheim , O. Cerri, I. Dutta , J.M. Lawhorn , N. Lu , J. Mao , H.B. Newman , T. Q. Nguyen , M. Spiropulu , J.R. Vlimant , C. Wang , S. Xie , Z. Zhang , R.Y. Zhu Carnegie Mellon University, Pittsburgh, Pennsylvania, USA J. Alison , S. An , M.B. Andrews , P. Bryant , T. Ferguson , A. Harilal , C. Liu , T. Mudholkar , S. Murthy , M. Paulini , A. Roberts , A. Sanchez , W. Terrill University of Colorado Boulder, Boulder, Colorado, USA J.P. Cumalat , W.T. Ford , A. Hassani , G. Karathanasis , E. MacDonald, R. Patel, A. Perloff , C. Savard , N. Schonbeck , K. Stenson , K.A. Ulmer , S.R. Wagner , N. Zipper Cornell University, Ithaca, New York, USA J. Alexander , S. Bright-Thonney , X. Chen , D.J. Cranshaw , J. Fan , X. Fan , D. Gadkari , S. Hogan , J. Monroy , J.R. Patterson , D. Quach , J. Reichert , M. Reid , A. Ryd , J. Thom , P. Wittich , R. Zou Fermi National Accelerator Laboratory, Batavia, Illinois, USA M. Albrow , M. Alyari , G. Apollinari , A. Apresyan , L.A.T. Bauerdick , D. Berry , J. Berryhill , P.C. Bhat , K. Burkett , J.N. Butler , A. Canepa , G.B. Cerati , H.W.K. Cheung , F. Chlebana , K.F. Di Petrillo , J. Dickinson , V.D. Elvira , Y. Feng , J. Freeman , A. Gandrakota , Z. Gecse , L. Gray , D. Green, S. Grünendahl , O. Gutsche , R.M. Harris , R. Heller , T.C. Herwig , J. Hirschauer , L. Horyn , B. Jayatilaka , S. Jindariani , M. Johnson , U. Joshi , T. Klijnsma , B. Klima , K.H.M. Kwok , S. Lammel , D. Lincoln , R. Lipton , T. Liu , C. Madrid , K. Maeshima , C. Mantilla , D. Mason , P. McBride , P. Merkel , S. Mrenna , S. Nahn , J. Ngadiuba , V. Papadimitriou , N. Pastika , K. Pedro , C. Pena83 , F. Ravera , A. Reinsvold Hall84 , L. Ristori , E. SextonKennedy , N. Smith , A. Soha , L. Spiegel , J. Strait , L. Taylor , S. Tkaczyk , N.V. Tran , L. Uplegger , E.W. Vaandering , H.A. Weber , I. Zoi University of Florida, Gainesville, Florida, USA P. Avery , D. Bourilkov , L. Cadamuro , V. Cherepanov , R.D. Field, D. Guerrero , M. Kim, E. Koenig , J. Konigsberg , A. Korytov , K.H. Lo, K. Matchev , N. Menendez , G. Mitselmakher , A. Muthirakalayil Madhu , N. Rawal , D. Rosenzweig , S. Rosenzweig , K. Shi , J. Wang , Z. Wu – 51 – JHEP05(2023)228 Florida State University, Tallahassee, Florida, USA T. Adams , A. Askew , R. Habibullah , V. Hagopian , R. Khurana, T. Kolberg , G. Martinez, H. Prosper , C. Schiber, O. Viazlo , R. Yohay , J. Zhang Florida Institute of Technology, Melbourne, Florida, USA M.M. Baarmand , S. Butalla , T. Elkafrawy85 , M. Hohlmann , R. Kumar Verma , D. Noonan , M. Rahmani, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, Illinois, USA M.R. Adams , H. Becerril Gonzalez , R. Cavanaugh , S. Dittmer , O. Evdokimov , C.E. Gerber , D.J. Hofman , D. S. Lemos , A.H. Merrit , C. Mills , G. Oh , T. Roy , S. Rudrabhatla , M.B. Tonjes , N. Varelas , X. Wang , Z. Ye , J. Yoo The University of Iowa, Iowa City, Iowa, USA M. Alhusseini , K. Dilsiz86 , L. Emediato , R.P. Gandrajula , O.K. Köseyan , J.- P. Merlo, A. Mestvirishvili87 , J. Nachtman , H. Ogul88 , Y. Onel , A. Penzo , C. Snyder, E. Tiras89 Johns Hopkins University, Baltimore, Maryland, USA O. Amram , B. Blumenfeld , L. Corcodilos , J. Davis , A.V. Gritsan , S. Kyriacou , P. Maksimovic , J. Roskes , M. Swartz , T.Á. Vámi The University of Kansas, Lawrence, Kansas, USA A. Abreu , L.F. Alcerro Alcerro , J. Anguiano , P. Baringer , A. Bean , Z. Flowers , T. Isidori , S. Khalil , J. King , G. Krintiras , M. Lazarovits , C. Le Mahieu , C. Lindsey, J. Marquez , N. Minafra , M. Murray , M. Nickel , C. Rogan , C. Royon , R. Salvatico , S. Sanders , E. Schmitz , C. Smith , Q. Wang , Z. Warner, J. Williams , G. Wilson Kansas State University, Manhattan, Kansas, USA B. Allmond , S. Duric, R. Gujju Gurunadha , A. Ivanov , K. Kaadze , D. Kim, Y. Maravin , T. Mitchell, A. Modak, K. Nam, J. Natoli , D. Roy Lawrence Livermore National Laboratory, Livermore, California, USA F. Rebassoo , D. Wright University of Maryland, College Park, Maryland, USA E. Adams , A. Baden , O. Baron, A. Belloni , S.C. Eno , N.J. Hadley , S. Jabeen , R.G. Kellogg , T. Koeth , Y. Lai , S. Lascio , A.C. Mignerey , S. Nabili , C. Palmer , C. Papageorgakis , M. Seidel , L. Wang , K. Wong Massachusetts Institute of Technology, Cambridge, Massachusetts, USA D. Abercrombie, R. Bi, W. Busza , I.A. Cali , Y. Chen , M. D’Alfonso , J. Eysermans , C. Freer , G. Gomez-Ceballos , M. Goncharov, P. Harris, M. Hu , D. 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Williams Northeastern University, Boston, Massachusetts, USA G. Alverson , E. Barberis , Y. Haddad , Y. Han , A. Krishna , J. Li , J. Lidrych , G. Madigan , B. Marzocchi , D.M. Morse , V. Nguyen , T. Orimoto , A. Parker , L. Skinnari , A. Tishelman-Charny , T. Wamorkar , B. Wang , A. Wisecarver , D. Wood Northwestern University, Evanston, Illinois, USA S. Bhattacharya , J. Bueghly, Z. Chen , A. Gilbert , T. Gunter , K.A. Hahn , Y. Liu , N. Odell , M.H. Schmitt , M. Velasco University of Notre Dame, Notre Dame, Indiana, USA R. Band , R. Bucci, M. Cremonesi, A. Das , R. Goldouzian , M. Hildreth , K. Hurtado Anampa , C. Jessop , K. Lannon , J. Lawrence , N. Loukas , L. Lutton , J. Mariano, N. Marinelli, I. Mcalister, T. McCauley , C. Mcgrady , K. Mohrman , C. Moore , Y. Musienko14 , R. Ruchti , A. Townsend , M. Wayne , H. Yockey, M. Zarucki , L. Zygala The Ohio State University, Columbus, Ohio, USA B. Bylsma, L.S. Durkin , B. Francis , C. Hill , A. Lesauvage , M. Nunez Ornelas , K. Wei, B.L. Winer , B. R. Yates Princeton University, Princeton, New Jersey, USA F.M. Addesa , B. Bonham , P. Das , G. Dezoort , P. Elmer , A. Frankenthal , B. Greenberg , N. Haubrich , S. Higginbotham , A. Kalogeropoulos , G. Kopp , S. Kwan , D. Lange , D. Marlow , K. Mei , I. Ojalvo , J. Olsen , D. Stickland , C. Tully University of Puerto Rico, Mayaguez, Puerto Rico, USA S. Malik , S. Norberg – 53 – JHEP05(2023)228 Purdue University, West Lafayette, Indiana, USA A.S. Bakshi , V.E. Barnes , R. Chawla , S. Das , L. Gutay, M. Jones , A.W. Jung , D. Kondratyev , A.M. Koshy, M. Liu , G. Negro , N. Neumeister , G. Paspalaki , S. Piperov , A. Purohit , J.F. Schulte , M. Stojanovic , J. Thieman , F. Wang , R. Xiao , W. Xie Purdue University Northwest, Hammond, Indiana, USA J. Dolen , N. Parashar Rice University, Houston, Texas, USA D. Acosta , A. Baty , T. Carnahan , M. Decaro, S. Dildick , K.M. Ecklund , S. Freed, P. Gardner, F.J.M. Geurts , A. Kumar , W. Li , B.P. Padley , R. Redjimi, J. Rotter , W. Shi , S. Yang , E. Yigitbasi , L. Zhang90, Y. Zhang , X. Zuo University of Rochester, Rochester, New York, USA A. Bodek , P. de Barbaro , R. Demina , J.L. Dulemba , C. Fallon, T. Ferbel , M. Galanti, A. Garcia-Bellido , O. Hindrichs , A. Khukhunaishvili , E. Ranken , R. Taus , G.P. Van Onsem The Rockefeller University, New York, New York, USA K. Goulianos Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA B. Chiarito, J.P. Chou , Y. Gershtein , E. Halkiadakis , A. Hart , M. Heindl , O. Karacheban25 , I. Laflotte , A. Lath , R. Montalvo, K. Nash, M. Osherson , S. Salur , S. Schnetzer, S. Somalwar , R. Stone , S.A. Thayil , S. Thomas, H. Wang University of Tennessee, Knoxville, Tennessee, USA H. Acharya, A.G. Delannoy , S. Fiorendi , T. Holmes , S. Spanier Texas A&M University, College Station, Texas, USA O. Bouhali91 , M. Dalchenko , A. Delgado , R. Eusebi , J. Gilmore , T. Huang , T. Kamon92 , H. Kim , S. Luo , S. Malhotra, R. Mueller , D. Overton , D. Rathjens , A. Safonov Texas Tech University, Lubbock, Texas, USA N. Akchurin , J. Damgov , V. Hegde , K. Lamichhane , S.W. Lee , T. Mengke, S. Muthumuni , T. Peltola , I. Volobouev , Z. Wang, A. Whitbeck Vanderbilt University, Nashville, Tennessee, USA E. Appelt , S. Greene, A. Gurrola , W. Johns , A. Melo , F. Romeo , P. Sheldon , S. Tuo , J. Velkovska , J. Viinikainen University of Virginia, Charlottesville, Virginia, USA B. Cardwell , B. Cox , G. Cummings , J. Hakala , R. Hirosky , M. Joyce , A. Ledovskoy , A. Li , C. Neu , C.E. Perez Lara , B. Tannenwald Wayne State University, Detroit, Michigan, USA P.E. Karchin , N. Poudyal – 54 – JHEP05(2023)228 University of Wisconsin - Madison, Madison, Wisconsin, USA S. Banerjee , K. Black , T. Bose , S. Dasu , I. De Bruyn , P. Everaerts , C. Galloni, H. He , M. Herndon , A. Herve , C.K. Koraka , A. Lanaro, A. Loeliger , R. Loveless , J. Madhusudanan Sreekala , A. Mallampalli , A. Mohammadi , D. Pinna, A. Savin, V. Shang , V. Sharma , W.H. Smith , D. Teague, W. Vetens Authors affiliated with an institute or an international laboratory covered by a cooperation agreement with CERN S. Afanasiev, V. Andreev , Yu. Andreev , T. Aushev , M. Azarkin , A. Babaev , A. Belyaev , V. Blinov93, E. Boos , V. Borshch , D. Budkouski , V. Bunichev , O. Bychkova, M. Chadeeva93 , V. Chekhovsky, A. Dermenev , T. Dimova93 , I. Dremin , M. Dubinin83 , L. Dudko , V. Epshteyn , G. Gavrilov , V. Gavrilov , S. Gninenko , V. Golovtcov , N. Golubev , I. Golutvin, I. Gorbunov , A. Gribushin , V. Ivanchenko , Y. Ivanov , V. Kachanov , L. Kardapoltsev93 , V. Karjavine , A. Karneyeu , V. Kim93 , M. Kirakosyan, D. Kirpichnikov , M. Kirsanov , V. Klyukhin , O. Kodolova94 , D. Konstantinov , V. Korenkov , A. Kozyrev93 , N. Krasnikov , E. Kuznetsova95, A. Lanev , P. Levchenko , A. Litomin, N. Lychkovskaya , V. Makarenko , A. Malakhov , V. Matveev93 , V. Murzin , A. Nikitenko96 , S. Obraztsov , V. Okhotnikov , I. Ovtin93 , V. Palichik , P. Parygin , V. Perelygin , M. Perfilov, G. Pivovarov , V. Popov, E. Popova , O. Radchenko93 , V. Rusinov, M. Savina , V. Savrin , D. Selivanova , V. Shalaev , S. Shmatov , S. Shulha , Y. Skovpen93 , S. Slabospitskii , V. Smirnov , A. Snigirev , D. Sosnov , A. Stepennov , V. Sulimov , E. Tcherniaev , A. Terkulov , O. Teryaev , I. Tlisova , M. Toms , A. Toropin , L. Uvarov , A. Uzunian , E. Vlasov , A. Vorobyev, N. Voytishin , B.S. Yuldashev97, A. Zarubin , I. Zhizhin , A. Zhokin †Deceased 1Also at Yerevan State University, Yerevan, Armenia 2Also at TU Wien, Vienna, Austria 3Now at Institute of Physics, University of Graz, Graz, Austria 4Also at Institute of Basic and Applied Sciences, Faculty of Engineering, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt 5Also at Université Libre de Bruxelles, Bruxelles, Belgium 6Also at Universidade Estadual de Campinas, Campinas, Brazil 7Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil 8Also at UFMS, Nova Andradina, Brazil 9Also at The University of the State of Amazonas, Manaus, Brazil 10 Also at University of Chinese Academy of Sciences, Beijing, China 11 Also at Nanjing Normal University Department of Physics, Nanjing, China 12 Now at The University of Iowa, Iowa City, Iowa, USA 13 Also at University of Chinese Academy of Sciences, Beijing, China 14 Also at an institute or an international laboratory covered by a cooperation agreement with CERN 15 Now at British University in Egypt, Cairo, Egypt 16 Now at Cairo University, Cairo, Egypt – 55 – JHEP05(2023)228 17 Also at Purdue University, West Lafayette, Indiana, USA 18 Also at Université de Haute Alsace, Mulhouse, France 19 Also at Department of Physics, Tsinghua University, Beijing, China 20 Also at Erzincan Binali Yildirim University, Erzincan, Turkey 21 Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland 22 Also at University of Hamburg, Hamburg, Germany 23 Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany 24 Also at Isfahan University of Technology, Isfahan, Iran 25 Also at Brandenburg University of Technology, Cottbus, Germany 26 Also at Forschungszentrum Jülich, Juelich, Germany 27 Also at Physics Department, Faculty of Science, Assiut University, Assiut, Egypt 28 Also at Karoly Robert Campus, MATE Institute of Technology, Gyongyos, Hungary 29 Also at Wigner Research Centre for Physics, Budapest, Hungary 30 Also at Institute of Physics, University of Debrecen, Debrecen, Hungary 31 Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary 32 Now at Universitatea Babes-Bolyai — Facultatea de Fizica, Cluj-Napoca, Romania 33 Also at Faculty of Informatics, University of Debrecen, Debrecen, Hungary 34 Also at Punjab Agricultural University, Ludhiana, India 35 Also at UPES — University of Petroleum and Energy Studies, Dehradun, India 36 Also at University of Visva-Bharati, Santiniketan, India 37 Also at University of Hyderabad, Hyderabad, India 38 Also at Indian Institute of Science (IISc), Bangalore, India 39 Also at Indian Institute of Technology (IIT), Mumbai, India 40 Also at IIT Bhubaneswar, Bhubaneswar, India 41 Also at Institute of Physics, Bhubaneswar, India 42 Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany 43 Also at Sharif University of Technology, Tehran, Iran 44 Also at Department of Physics, University of Science and Technology of Mazandaran, Behshahr, Iran 45 Also at Helwan University, Cairo, Egypt 46 Also at Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Bologna, Italy 47 Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia, Catania, Italy 48 Also at Scuola Superiore Meridionale, Università di Napoli ’Federico II’, Napoli, Italy 49 Also at Fermi National Accelerator Laboratory, Batavia, Illinois, USA 50 Also at Università di Napoli ’Federico II’, Napoli, Italy 51 Also at Consiglio Nazionale delle Ricerche — Istituto Officina dei Materiali, Perugia, Italy 52 Also at Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia 53 Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico 54 Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France 55 Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia 56 Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri Lanka 57 Also at INFN Sezione di Pavia, Università di Pavia, Pavia, Italy 58 Also at National and Kapodistrian University of Athens, Athens, Greece 59 Also at Ecole Polytechnique Fédérale Lausanne, Lausanne, Switzerland 60 Also at Universität Zürich, Zurich, Switzerland 61 Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria – 56 –