Science, Faculty of
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The European Physical Journal C. 2025 Feb 06;85(2):153
The Author(s)
ATLAS Collaboration
A search for a light charged Higgs boson produced in decays of the top quark, t → H±b with H± → cs, is presented. This search targets the production of top-quark pairs tt ¯ → WbH±b, with W → ν ( = e, μ), resulting in a lepton-plus-jets final state characterised by an isolated electron or muon and at least four jets. The search exploits b-quark and c-quark identification techniques as well as multivariate methods to suppress the dominant tt ¯ background. The data analysed correspond to 140 fb−1 of pp collisions at √s = 13 TeV recorded with the ATLAS detector at the LHC between 2015 and 2018. Observed (expected) 95% confidence-level upper limits on the branching fraction B(t → H±b), assuming B(t → W b) + B(t → H±(→ cs)b) = 1.0, are set between 0.066% (0.077%) and 3.6% (2.3%) for a charged Higgs boson with a mass between 60 and 168 GeV.
Eur. Phys. J. C (2025) 85:153 https://doi.org/10.1140/epjc/s10052-024-13715-4Regular Article - Experimental PhysicsSearch for a light charged Higgs boson in t → H±b decays, withH± → cs, in pp collisions at √s = 13 TeV with the ATLASdetectorATLAS CollaborationCERN, 1211 Geneva 23, SwitzerlandReceived: 16 July 2024 / Accepted: 17 December 2024© CERN for the benefit of the ATLAS Collaboration 2025Abstract A search for a light charged Higgs boson pro-duced in decays of the top quark, t → H±b with H± → cs,is presented. This search targets the production of top-quarkpairs t t¯ → W bH±b, with W → ν ( = e, μ), result-ing in a lepton-plus-jets final state characterised by an iso-lated electron or muon and at least four jets. The searchexploits b-quark and c-quark identification techniques as wellas multivariate methods to suppress the dominant t t¯ back-ground. The data analysed correspond to 140 fb−1 of ppcollisions at√s = 13 TeV recorded with the ATLAS detec-tor at the LHC between 2015 and 2018. Observed (expected)95% confidence-level upper limits on the branching fractionB(t → H±b), assuming B(t → W b) + B(t → H±(→cs)b) = 1.0, are set between 0.066% (0.077%) and 3.6%(2.3%) for a charged Higgs boson with a mass between 60and 168 GeV.Contents1 Introduction . . . . . . . . . . . . . . . . . . . . . .2 ATLAS detector . . . . . . . . . . . . . . . . . . . .3 Data and simulated event samples . . . . . . . . . . .4 Object definition and event selection . . . . . . . . .5 Background modelling . . . . . . . . . . . . . . . . .6 Analysis strategy . . . . . . . . . . . . . . . . . . . .6.1 t t¯-system reconstruction . . . . . . . . . . . . .6.2 Multivariate signal extraction . . . . . . . . . . .7 Systematic uncertainties . . . . . . . . . . . . . . . .8 Statistical interpretation . . . . . . . . . . . . . . . .9 Results . . . . . . . . . . . . . . . . . . . . . . . . .10 Conclusions . . . . . . . . . . . . . . . . . . . . . .References . . . . . . . . . . . . . . . . . . . . . . . . . e-mail: atlas.publications@cern.ch1 IntroductionThe discovery of the Higgs boson at the Large Hadron Col-lider (LHC) in 2012 was a great achievement of the ATLASand CMS Collaborations [1,2], and has led to numerous mea-surements to determine its properties [3,4]. One of the maingoals of these studies is to establish if the discovered Higgsboson is the single fundamental scalar particle of the Stan-dard Model (SM) or rather the first observed particle of anextended scalar sector.Extensions to the scalar sector are motivated by the solu-tions they provide to several open questions in particlephysics. An extended scalar sector can modify the elec-troweak phase transition and facilitate baryogenesis [5],enhance vacuum stability, provide a dark-matter candi-date [6,7] or yield a solution to the strong CP problem [8].Many physics models beyond the SM (BSM) require anextended scalar sector. For example, in the minimal super-symmetric extension of the SM the existence of two Higgsdoublets is required [9]. In models with a Type-II seesawmechanism, Higgs triplets [10–14] are required.Two-Higgs-doublet models (2HDMs) [15,16] are popu-lar and simple extensions of the scalar sector and predictthe existence of two charged Higgs bosons, H+ and H−,and two neutral Higgs bosons in addition to the discoveredneutral one. The various 2HDMs are categorised into typesdefined by the Yukawa couplings of the fermions to the Higgsdoublets. The production mechanisms and decay modes ofcharged Higgs bosons depend on the Yukawa couplings andother model parameters, especially the ratio of the two Higgs-doublet vacuum expectation values (tan β) and the chargedHiggs boson’s mass (m H±). Many phenomenology studiesadvocate searching for a light charged Higgs bosons (belowthe top-quark mass) in the decays to a charm quark and astrange quark, H± → cs,1 to a charm quark and a bot-1 Unless explicitly stated otherwise, charge conjugation is implied inthis paper; the notation cs is used in place of cs¯/c¯s.0123456789().: V,-vol 123 153 Page 2 of 34 Eur. Phys. J. C (2025) 85:153 tom quark, H± → cb, and to a τ -lepton and a τ -neutrino,H± → τντ [6,15]. The branching fraction for H± → cbis typically smaller than for H± → cs, due to the differ-ent values of the CKM matrix elements, Vcs  Vcb. In theType-I 2HDM, only the τντ and cs decay modes are relevant.In Type-II and Type-X (or “lepton-specific”) models the cschannel is dominant for values of tan β < 1. In Type-Y (or“flipped”) models the cs and cb channels are important fortan β > 5 [6]. For a 2HDM model where one doublet cou-ples mainly to the third generation, while the other doubletcouples mainly to the first and second generations, the csand cb channels are dominant [17]. This is also the case forleptophobic multiple-Higgs-doublet models [6].Searches for H± → cs in top-quark decays have beenperformed by the ATLAS and CMS Collaborations, basedon 4.7 fb−1 and 35.9 fb−1 of proton–proton (pp) collisiondata collected at centre-of-mass energies of√s = 7 TeVand√s = 13 TeV, respectively. The search performed by theATLAS Collaboration obtained 95% confidence level (CL)observed (expected) upper limits on the branching fractionB(t → H±b) ranging from 1.2% (1.5%) to 5.1% (8%),assuming B(H± → cs) = 1.0, for m H± between 90 and150 GeV [18]. The search performed by the CMS Collab-oration obtained 95% CL observed (expected) upper limitson B(t → H±b) ranging from 0.25% (0.29%) to 1.68%(2.39%), assuming B(H± → cs) = 1.0, for m H± between80 and 160 GeV [19]. Related searches for H± → cb intop-quark decays were performed by the ATLAS and CMSCollaborations using 139 fb−1 and 19.7 fb−1 of pp collisiondata collected at√s = 13 TeV [20] and 8 TeV [21], respec-tively. The most stringent observed limits onB(t → H±b)×B(H± → cb) are set by ATLAS and range from 0.15 to0.42% for m H± between 60 GeV and 160 GeV. In that analy-sis a moderate excess of signal events is observed in the vicin-ity of 130 GeV, with a global significance of 2.5σ . Searchesfor H± → τν in 36.1 fb−1 of pp collision data recorded withthe ATLAS and CMS detectors at√s = 13 TeV were carriedout over a wide mass range, from 90 to 2000 GeV [22] or from80 to 3000 GeV [23] respectively, covering the masses of lightand heavy charged Higgs bosons. The ATLAS (CMS) Col-laboration set observed upper limits on the branching frac-tion B(t → H±b) × B(H± → τν) ranging from 0.25 to0.031% (0.36–0.079%) in the mass range between 90 GeV(80 GeV) and 160 GeV. In the absence of other new physics,measurements of the process b → sγ exclude in Type-IIand Type-Y models a charged Higgs boson with mass below580 GeV independent of tan β [24].This analysis searches for a charged Higgs boson witha mass below the top-quark mass. In this regime the mainproduction mode for charged Higgs bosons is via t t¯ events,with the charged Higgs boson emerging from rare top-quark decays: t → H±b. This analysis focuses on thet t¯ → W bH±b process, with W → ν ( = e, μ) andFig. 1 Illustrative Feynman diagram of the signal processH± → cs, resulting in a lepton-plus-jets final state (seeFig. 1). Only events with an electron or muon, includingthose produced via leptonically decaying τ -leptons, denotedby τlep, are considered.This paper is based on 140 fb−1 of pp collision data at√s = 13 TeV. In contrast to previous H± → cs searches,multivariate analysis techniques are deployed here to searchfor a potential signal in the data. This approach exploits boththe kinematic properties of signal events, including the H±candidate mass, and their flavour composition, which differsfrom that of the mostly SM t t¯ background events. The kine-matics of the top-quark decays are derived by reconstructingthe t t¯ event topology. Flavour-tagging algorithms are utilisedto tag jets as b-jets, c-jets or light-flavour jets. A dedicatedflavour-tagging scheme which facilitates simultaneous tag-ging of b-jets and c-jets is adopted.2 ATLAS detectorThe ATLAS detector [25] at the LHC covers nearly the entiresolid angle around the collision point.2 It consists of aninner tracking detector surrounded by a thin superconductingsolenoid, electromagnetic (ECAL) and hadronic calorime-2 ATLAS uses a right-handed coordinate system with its origin at thenominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the cen-tre of the LHC ring, and the y-axis points upwards. Polar coordi-nates (r, φ) are used in the transverse plane, φ being the azimuthalangle around the z-axis. The pseudorapidity is defined in terms ofthe polar angle θ as η = − ln tan(θ/2) and is equal to the rapidityy = (1/2) ln [(E + pz)/(E − pz)]in the relativistic limit. Angulardistance is measured in units of R ≡ √( y)2 + ( φ)2.123Eur. Phys. J. C (2025) 85:153 Page 3 of 34 153 ters, and a muon spectrometer (MS) incorporating three largesuperconducting air-core toroidal magnets.The inner-detector system (ID) is immersed in a 2 T axialmagnetic field and provides charged-particle tracking in therange |η| < 2.5. The high-granularity silicon pixel detectorcovers the vertex region and typically provides four measure-ments per track, the first hit generally being in the insertableB-layer (IBL) installed before Run 2 [26,27]. It is followedby the SemiConductor Tracker (SCT), which usually pro-vides eight measurements per track. These silicon detectorsare complemented by the transition radiation tracker (TRT),which enables radially extended track reconstruction up to|η| = 2.0. The TRT also provides electron identificationinformation based on the fraction of hits (typically 30 intotal) above a higher energy-deposit threshold correspond-ing to transition radiation.The calorimeter system covers the pseudorapidity range|η| < 4.9. Within the region |η| < 3.2, electromag-netic calorimetry is provided by barrel and endcap high-granularity lead/liquid-argon (LAr) calorimeters, with anadditional thin LAr presampler covering |η| < 1.8 to cor-rect for energy loss in material upstream of the calorimeters.Hadronic calorimetry is provided by the steel/scintillator-tile calorimeter, segmented into three barrel structures within|η| < 1.7, and two copper/LAr hadronic endcap calorime-ters. The solid angle coverage is completed with forward cop-per/LAr and tungsten/LAr calorimeter modules optimised forelectromagnetic and hadronic energy measurements respec-tively.The MS comprises separate trigger and high-precisiontracking chambers measuring the deflection of muons in amagnetic field generated by the superconducting air-coretoroidal magnets. The field integral of the toroids rangesbetween 2.0 and 6.0 T m across most of the spectrometer.Three layers of precision chambers, each consisting of layersof monitored drift tubes, cover the region |η| < 2.7, com-plemented by cathode-strip chambers in the forward region,where the background is highest. The muon trigger systemcovers the range |η| < 2.4 with resistive-plate chambers inthe barrel, and thin-gap chambers in the endcap regions.The luminosity is measured mainly by the LUCID-2 [28]detector that records Cherenkov light produced in the quartzwindows of photomultipliers located close to the beam pipe.Events are selected by the first-level trigger system imple-mented in custom hardware, followed by selections made byalgorithms implemented in software in the high-level trig-ger [29]. The first-level trigger accepts events from the 40MHz bunch crossings at a rate below 100 kHz, which thehigh-level trigger further reduces in order to record completeevents to disk at about 1 kHz.A software suite [30] is used in data simulation, in thereconstruction and analysis of real and simulated data, indetector operations, and in the trigger and data acquisitionsystems of the experiment.3 Data and simulated event samplesThis search is based on data collected from pp collisionsat the ATLAS experiment during LHC Run 2 at a centre-of-mass energy of√s = 13 TeV. After applying qualityrequirements, the dataset corresponds to an integrated lumi-nosity of 140.1 ± 1.2 fb−1 [28,31]. Signal and backgroundprocesses, except multijet processes, were simulated usingMonte Carlo (MC) event generators.The main background in this search is SM t t¯ pro-duction. The production of t t¯ and single-top-quark eventsin the tW -, s- and t-channels was modelled with thePowhegBoxv2 [32–35] generator at next-to-leading order(NLO), using the five-flavour scheme (four-flavour schemefor single-top-quark t-channel events) with the NNPDF[3.0nlo] [36] parton distribution function (PDF) set and thehdamp parameter3 set to 1.5 mtop [37]. The events were inter-faced to Pythia8.230 [38] to model the parton shower(PS), hadronisation, and underlying event. For all sam-ples in this search, Pythia8 used the NNPDF2.3lo setof PDFs [39], and its parameter values were set to thoseof the A14 tune [40]. The decays of bottom and charmhadrons were performed by EvtGen1.6.0 [41]. To assessthe uncertainty in the matching of NLO matrix elements(ME) to the PS, the nominal samples were compared withsamples of t t¯ and single-top-quark events generated withMadGraph5_aMC@NLO2.6.0 and 2.6.2 [42] respectively,using the NNPDF3.0nlo set of PDFs, and interfaced withPythia8.230. The impact of using a different PS andhadronisation model was evaluated by comparing the nom-inal samples with alternative samples produced with thePowhegBoxv2 generator using the NNPDF3.0nlo PDFset and interfaced with Herwig [43,44]. Herwig7.13 andHerwig7.16 were used for t t¯ and single-top-quark events,respectively, and both used the Herwig7.1 default setof tuned parameters [44,45] and the MMHT2014lo PDFset [46]. The t t¯–tW interference was handled using the dia-gram removal scheme [47]. The uncertainty associated withthis choice is estimated by comparing the nominal samplewith an alternative sample generated using the diagram sub-traction scheme [37,47]. The t t¯ production cross-sectionis calculated at next-to-next-to-leading-order and next-to-next-to-leading-logarithm (NNLO+NNLL) accuracy [48].The cross-sections for the three single-top-quark production3 The hdamp parameter is a resummation damping factor and one of theparameters that controls the matching of Powheg matrix elements tothe parton shower and thus effectively regulates the high-pT radiationagainst which the t t¯ system recoils.123 153 Page 4 of 34 Eur. Phys. J. C (2025) 85:153 channels are calculated at NLO [49–51]. Simulated t t¯ eventsare categorised according to the flavour of additional jets inthe event, using the procedure described in Ref. [52]. Eventswith at least one additional b-flavour or c-flavour jet arelabelled as t t¯ +HF (where HF stands for “heavy-flavour”).The remaining events are labelled as t t¯ +LF (where LFstands for “light-flavour”). This category is split into t t¯(ud)and t t¯(cs) subcategories according to the decay of the Wbosons. If at least one W boson decays as W → cs the eventfalls into the t t¯(cs) category, otherwise it falls into the t t¯(ud)category. This categorisation is motivated by the fact that thefinal state is identical for t t¯(cs) and signal events.The rare top-quark processes considered in this analy-sis are t t¯ H , t t¯W , t t¯ Z , t t¯ t t¯ , t t¯ t , t H jb, tW H , tW Z andt Zq. The t t¯ H events were modelled with the same gen-erators and versions as the exclusive t t¯ events. The t t¯W ,t t¯ Z , tW Z and t t¯ t t¯ processes were modelled using Mad-Graph5_aMC@NLO2.3.3, the t t¯ t process using Mad-Graph5_aMC@NLO2.2.2, and the t H jb and tW H pro-cesses using MadGraph5_aMC@NLO2.6.2, in all casesat NLO with the NNPDF3.0nlo PDF (NNPDF3.1nlo forthe t t¯ t t¯ process). The t t¯ t and t Zq processes were modelledwith MadGraph2.2.2 at LO with the NNPDF2.3nlo PDF.The events were then interfaced with Pythia8.186–8.235and the decays of bottom and charm hadrons were simu-lated using the EvtGen1.2.0–1.6.0 program. The t t¯ H , t t¯Wand t t¯ Z samples were normalised using cross-sections cal-culated at NLO QCD and NLO EW accuracy using Mad-Graph5_aMC@NLO as reported in Ref. [53].The production of a W or Z/γ ∗ boson in associationwith jets (V + jets) and of dibosons (V V ) was modelledwith the Sherpa2.2.11 [54] and Sherpa2.2.1 generators,respectively, for both the ME and PS. The only exceptionis the V V → ννν process, which was modelled withSherpa2.2.2. The NLO ME for up to two partons (oneparton) and leading-order ME for up to five (three) partonswere calculated with the Comix [55] and OpenLoops [56–58] libraries for V +jets (V V ) events. They were matchedwith the Sherpa PS [59] using the MEPS@NLO prescrip-tion [60–63] and the set of tuned parameters developed bythe Sherpa authors. The NNPDF3.0nnlo set of PDFs wasused.Signal events were modelled by first generating top-quark pairs, similar to the SM t t¯ background, using thePowhegBoxv2 generator at NLO with the NNPDF3.0nloPDF set and the hdamp parameter set to 1.5 mtop. The decayst → H±b and t → W±b were modelled by MadSpin [64]using the Type-II 2HDM [15,65] for BSM decays. Sub-sequent decays of the H± and W± bosons, as well asthe showering of the final-state hadrons, were modelled byPythia8.307. The W± were forced to decay leptonically,with all three lepton flavours allowed. The H± were forced todecay into a cs-quark pair. The decays of bottom and charmhadrons were performed by EvtGen1.7.0. Signal sampleswere generated with zero decay width for twelve charged-Higgs-boson mass points: eleven in steps of 10 GeV from 60GeV to 160 GeV, and one at 168 GeV. The signal samplesare denoted by H±x , where x is the mass of the charged Higgsboson in GeV. If the mass difference between the H± andW bosons is smaller than one of their total widths, the inter-ference term might be of the order of a few percent of theH± contribution. The size and the sign of the interferenceterm depend on the model [66]. For larger mass differences,the interference term can be omitted with high accuracy. Theinterference term is neglected in this analysis for all masspoints. Production of charged Higgs bosons via single-top-quark processes is neglected in this analysis because suchevents usually do not contain a prompt lepton and are there-fore suppressed by the event selection (cf. Sect. 4), and alsobecause the production cross-section is much smaller thanfor t t¯ processes.The effect of multiple interactions in the same andneighbouring bunch crossings (pile-up) was modelled byoverlaying the simulated hard-scattering event with inelas-tic pp events generated with Pythia8.186 [67] using theNNPDF2.3lo PDF set and the A3 set of tuned parame-ters [68]. Events in the nominal background samples werepassed through the full ATLAS detector simulation [69]based on Geant4 [70]. Signal, t t¯ t t¯ , t H and alternative sam-ples were passed through a fast simulation in which theresponse of the calorimeter is parameterised [71]. Both sim-ulation methods were found to provide a similar level ofmodelling accuracy for the physics objects used in the anal-ysis. A full list of samples used in this search is summarisedin Table 1.4 Object definition and event selectionTracks are required to have transverse momentum (pT)greater than 500 MeV, |η| < 2.5, and at least seven hitsin the pixel and SCT detectors. A maximum of one (two)of the expected hits may be missing from the pixel (SCT)detector, and no more than one hit may be shared with othertracks [72]. Events are required to have at least one primaryvertex reconstructed from two or more associated tracks [73].If multiple vertices are found, the one with the highest scalarsum of the p2T of associated tracks is selected as the primaryvertex.Electrons are reconstructed from topological energy clus-ters in the ECAL that are matched to tracks in the ID [74].Electrons are required to have pT > 10 GeV and |η| <2.47, excluding the barrel–endcap transition region 1.37 <|η| < 1.52. They must pass track-quality requirements fol-lowed by a loose likelihood-based selection that requiresthe shower profile to be compatible with that of the electro-123Eur. Phys. J. C (2025) 85:153 Page 5 of 34 153 Table 1 Generators used to simulate the signal and background pro-cesses. The symbol q is used for u, d, c, s quarks. For the signal pro-cesses the subscript “x” is a placeholder for the mass of the chargedHiggs boson in GeV. For the “Other top” and V V processes, only therange of used generator versions is quoted. The exact generator versionused for each process is described in Sect. 3Name Process ME generator PS and hadronisationSignalH±x t t¯ → H±(→ cs)W∓(→ ν)bb¯ PowhegBoxv2 MadSpin + Pythia8.307 +EvtGen1.7.0Top-quarkt t¯(ud) t t¯ → W±(→ ν)W∓(→ ud)bb¯ PowhegBoxv2 Pythia8.230 + EvtGen1.6.0t t¯(cs) t t¯ → W±(→ ν)W∓(→ cs)bb¯t t¯ +HF t t¯ → W±(→ ν)W∓(→qq¯)bb¯ +≥1c/bt t¯(allHad) t t¯ → W±(→ qq¯)W∓(→ qq¯)tW tWSingle top single t-quark s- & t-channelt t¯ H t t¯ HOther top t t¯W , t t¯ Z , t t¯ t t¯ , t H jb, tW H , tW Z , MadGraph5_aMC@NLO2.3.3–2.6.2Pythia8.186–8.230 +EvtGen1.2.0–1.6.0t t¯ t , t Zq MadGraph2.2.2–2.3.3Weak-bosonW + jets W + jets Sherpa2.2.11 Sherpa2.2.11Z + jets Z + jetsV V W W , W Z , Z Z Sherpa2.2.1–2.2.2 Sherpa2.2.1– 2.2.2magnetic shower. Electrons are required to have transverse(d0) and longitudinal (z0) impact parameters, measured rel-ative to the beam-line and primary vertex respectively, sat-isfying |d0|/σ(d0) < 5 and |z0 sin θ | < 0.5 mm. Isolationrequirements are applied via a boosted decision tree (BDT)which was trained on track-isolation, cluster-isolation, andsecondary-vertex information, referred to as “non-prompt-lepton BDT” [75,76]. The electron energy scale and reso-lution calibrations are obtained from Z → ee events andapplied to data and simulations, respectively [74].Muon candidates are reconstructed by matching MS tracksto ID tracks. In the absence of full tracks in the MS, muons canbe reconstructed from ID tracks extrapolated to the MS whichmatch at least three loosely aligned MS hits. The informationfrom the ID and the MS, and the energy loss in the calorime-ters, are then used in a combined track fit [77]. Muons haveto satisfy pT > 10 GeV and |η| < 2.5, and pass qualityrequirements based on the number of hits used to reconstructthe tracks. Muons are also required to satisfy |d0|/σ(d0) < 3and |z0 sin θ | < 0.5 mm. Lastly, isolation requirements arealso made based on the non-prompt-lepton BDT.Jets are reconstructed with the anti-kt jet clustering algo-rithm [78,79] with a radius parameter R = 0.4. The clus-tering is applied to noise-suppressed positive-energy topo-logical energy clusters [80,81] and charged-particle tracks,processed using a particle-flow algorithm [82]. Jet ener-gies are corrected for contributions from pile-up, calibratedusing energy- and η-dependent correction factors determinedfrom comparisons between particle-level objects and recon-structed physics objects in simulated events, and then correc-tions are applied to account for effects due to the initiating-parton type and hadron composition [83]. In data, a residualin situ correction is applied in order to correct for differencesrelative to simulation. Jets in the analysis are required to havepT > 25 GeV and |η| < 2.5. Jets with pT < 60 GeV and|η| < 2.4 also have to pass a jet-vertex-tagger [84] require-ment to reduce the number of selected jets which originatefrom pile-up.Jets containing b- or c-hadrons are identified with theDL1r tagger [85], which is a multivariate classification algo-rithm based on a deep neural network using informationabout the impact parameters of tracks, the jet kinematics,and displaced vertices. The b- and c-tagging scores are basedon log-likelihood ratios of the neural-network output scores.To assign jets to top quarks or to H± boson candidates,one needs to identify b- and c-quark-initiated jets simultane-ously and distinguish them from the light-flavour jets. Cor-rection factors are applied to the simulated events to com-pensate for differences between data and simulation in theb- and c-tagging efficiencies or misidentification rates for b-jets, c-jets and light-flavour jets [86–88]. This search uses apseudo-continuous flavour-tagging (PCFT) calibration withfive exclusive calibrated bins. Jets passing a fixed b-taggingworking point (WP) defined by b-jet efficiencies, measured123 153 Page 6 of 34 Eur. Phys. J. C (2025) 85:153 in t t¯ events, of 70% and 60% have PCFT scores of 3 and 4,respectively. These two b-tagging WPs have background tag-ging efficiencies for c-jets (light-jets) of 7.9% (0.18%) and2.7% (0.05%), respectively [89]. Other jets (b-veto) receivea PCFT score of 1 or 2 if they pass a fixed c-tagging scoredefined by a c-jet efficiency of 45% or 24%, respectively.These two c-tagging WPs have background tagging efficien-cies for b-jets (light-jets) of 16.3% (7.4%) and 4.8% (0.9%),respectively [89]. Jets passing none of the b- and c-taggingWPs (untagged) are assigned a PCFT score of 0. Any jetpassing the loosest calibrated b(c)-tagging WP is referred toas a b(c)-tagged jet.The missing transverse momentum (pmissT ) is defined asthe negative vector sum of the transverse momenta of allreconstructed and calibrated leptons and jets, and all tracksmatched to the primary vertex but not to other reconstructedobjects in the event [90]. The absolute value of pmissT isdenoted by EmissT .An overlap-removal procedure is applied to resolve ambi-guities where multiple physical objects are reconstructedfrom the same detector signature. The angular distance Ris used to measure the overlap of two reconstructed objects.The following procedure is applied in order:1. any calorimeter-tagged muon [77] sharing a track with anelectron is removed;2. any electron sharing a track with a muon is removed;3. any jet within R = 0.2 of an electron is removed;4. any electron within R = 0.4 of a jet is removed;5. any jet with less than 3 tracks that is within R = 0.2 ofa muon is removed;6. any jet with less than 3 tracks that has a muon ID trackghost-associated [78,91] with it is removed;7. any muon within R = 0.4 of a jet is removed.Events were recorded with a single-electron or single-muon trigger with a threshold requirement imposed on thelepton pT. For the data-taking periods 2015 and 2016–2018,the lowest electron pT threshold was 24 GeV or 26 GeVrespectively, and similarly the lowest muon-pT thresholdwas 20 GeV or 26 GeV. The trigger includes lepton iden-tification and isolation requirements based on ID or ECALmeasurements [92–94]. Furthermore, events are required tohave exactly one offline reconstructed lepton with pT >27 GeV that meets the “medium” identification and isola-tion criteria [74,77]. Events with an additional lepton withpT > 10 GeV that satisfies the medium identification criteriaare vetoed to reduce dileptonic t t¯ and Z + jets backgrounds.The offline reconstructed lepton is required to be geometri-cally matched ( R < 0.1) to the online reconstructed leptonwhich fired the trigger. At least four jets with pT > 25 GeVhave to be present, and at least one is required to be b-tagged. Finally, events in the signal region are required tohave exactly one “tight” identified and isolated lepton and atleast two b-tagged jets [74,77]. The t t¯ background contribu-tion in the signal region is about 92%.5 Background modellingThe main background in this search is t t¯ production in asso-ciation with jets. As in many other analyses targeting a kine-matic phase space similar to the signal region of this search,differences between MC-based background predictions anddata are observed in multiple kinematic quantities [20,95].This disagreement can be attributed to missing higher-orderQCD and electroweak corrections in t t¯ MC simulation lead-ing to harder top-quark pT spectra in simulation than indata [96]. A data-driven correction is derived to improve themodelling of the t t¯ background and signal, particularly ofpT-dependent variables.The correction is derived as a function of ST. The ST vari-able is defined as the sum of the scalar transverse momentaof all calibrated objects in the event, i.e. jets, leptons andEmissT , and is therefore related to the transverse momenta ofindividual top quarks. The corrections are derived in bins ofthe number of jets in the event (Njets = 4, 5, 6, 7, 8,≥9), asthis quantity also shows discrepancies between MC eventsand data and is correlated with ST. The correction is derivedin the signal region, since the t t¯ contribution is around 92%in that region, and is applied to signal events as well as t t¯events because the mismodelling is expected to affect thesignal MC prediction in the same way. It was checked thatthe ST distribution in bins of Njets is similar for t t¯ and signalevents. A possible signal contribution in data will thereforenot change the correction weights and the correction will notbias the signal extraction.The t t¯ correction weights are defined as the ratio of datatemplates (with non-t t¯ backgrounds subtracted) to MC t t¯templates. In order to mitigate the effects of statistical fluctu-ations in the data and simulation samples, a linear + exponen-tial function is fitted to the derived t t¯ correction weights. Thefit is performed separately for even and odd event numbers toavoid overfitting. The weights from the fit to even-numberedevents are applied to odd-numbered events and vice versa.Figure 2 shows the ST and lepton-pT distributions afterapplying the t t¯ correction. The red dashed line representsthe total background prediction before applying the correc-tion. Agreement between the data and MC prediction clearlyimproves for ST and other related distributions, especially athigh values. The ST and lepton-pT distributions of the alter-native t t¯ MC samples differ significantly from those of thenominal t t¯ sample. Hence, alternative t t¯ correction weightsare derived and applied to these events.Multijet (MJ) processes can contribute to the backgroundwhen jets are misidentified as leptons or when real non-123Eur. Phys. J. C (2025) 85:153 Page 7 of 34 153 Fig. 2 Distribution of a ST and b lepton pT after applying the t t¯ correc-tion. The processes t t¯(allHad), tW , Single top, t t¯ H , Other top, W + jets,Z + jets, and V V listed in Table 1 are combined with the multijet back-ground in the “Other” category. The uncertainty band represents thecombined statistical and systematic uncertainty of the prediction. Thered dashed line represents the total background prediction before apply-ing the correctionprompt leptons are produced in the decays of heavy-flavourhadrons. A data-driven method commonly called the ABCDmethod (see e.g. Ref. [97]) is used to estimate the MJ back-ground in the signal region. The lepton isolation requirementand the number of b-tagged jets are used to define the fourABCD regions. The shape of the MJ background is esti-mated from a region with a looser lepton-isolation require-ment. The normalisation is derived from events with exactlyone b-tagged jet. The ABCD method is applied separatelyto electron and muon events. The size of the total MJ back-ground in the signal region is about 0.3% of the total esti-mated background.46 Analysis strategyThe presence of a potential signal in data is quantified firstby reconstructing the t t¯ event topology, and then extractingthe signal using a BDT. The t t¯-system is reconstructed byusing calibrated physics objects, i.e. leptons, pmissT and jets,4 The MJ background is considered when deriving the t t¯ correction.The t t¯ correction weights are also applied in the MJ-enriched regions.The reciprocal dependence of the MJ background estimate and thet t¯ correction is considered when deriving the correction weights. Theimpact on the correction weights is minor due to the overall small MJcontribution.as proxies for the lepton, neutrino and quarks from the top-quark decays. Kinematic properties of the reconstructed t t¯-system are then used to train a BDT to classify events assignal or background.6.1 t t¯-system reconstructionThe t t¯-system consists of a semileptonically decaying topquark (tlep) and a hadronically decaying top quark (thad). Thetlep decays into a b-quark (blep) and a W boson, which decaysinto a lepton and neutrino. The thad decays into a b-quark(bhad) and a H± or W boson, which decays into c- and s-quarks or other quarks ( j1, j2), respectively.The lepton from the W -boson decay is unambiguouslyidentified as the single reconstructed lepton. The momentumof the neutrino from the W -boson decay is reconstructedusing pmissT and a W -boson mass constraint. The neutrinopseudorapidity, ην , is calculated by setting the invariant massof the lepton and neutrino equal to the W -boson mass, mW =80.379 GeV [98]:ην = η ± arccosh(m2W2pνT pT+ cos (φν − φ)).This equation generally has two solutions and the one that ischosen depends on the jet labelling, described below. If the123 153 Page 8 of 34 Eur. Phys. J. C (2025) 85:153 argument of arccosh is exactly one, there is only one solution(ην = η). Due to reconstruction inefficiencies or additionalneutrinos in the event, e.g. from the decay of τlep, it may alsohappen that the argument of arccosh is smaller than one, forwhich arccosh is not defined. In this case the argument ofarccosh is set to one. This means ην = η, in which casethe invariant mass of the lepton and the neutrino exceedsmW . The latter case occurs for about 35% of the simulatedt t¯ events.The labelling of the jets as blep, bhad, j1 and j2 suffersfrom a combinatorics problem, which the analysis tries toresolve by comparing the top-quark candidate’s mass with thepredicted top-quark mass. Because the mass resolution dif-fers between semileptonically and hadronically decaying topquarks, the approach adopted uses probability density func-tions of the reconstructed top-quark masses (PDFt ). Theseare built from t t¯ MC events, using reconstructed jets matchedto the generator-level (“truth”) quarks. Since there is only onelepton candidate, no matching is applied for it. For the neu-trino, if there are two solutions for ην , the one closest to thetrue value of ην is selected. The “truth” quarks are geometri-cally matched to the closest reconstructed jet within R =0.4. The b- and c-quarks are only matched if the reconstructedjet has a respective “truth” hadron (pT > 5 GeV) associatedwith it. Other quark types are only matched to the recon-structed jet if no heavy-flavour hadron is associated with it.If multiple “truth” quarks are matched to the same recon-structed jet, ambiguities are resolved by minimising the sumof the (four) R values between the “truth” quarks and anyreconstructed jet within R = 0.4 of the “truth” quarks. Inroughly 53% of the events, at least one “truth” quark cannotbe matched to a reconstructed jet. Such cases are typicallyassociated with “truth” quarks produced outside the detectoracceptance. Such events are not considered in the PDFt . Inorder to get a smooth prediction over the full top-quark massrange, Crystal Ball + Cauchy and Crystal Ball + Gaussianfunctions [99–101] are fitted to the tlep-mass and thad-massPDFt , respectively. The PDFt are shown with the correspond-ing fits in Fig. 3.The derived top-quark mass PDFt are then used to labeljets in an event. All possible permutations of blep, bhad, j1,j2 labellings and ην solutions are built simultaneously. Thepermutation with the largest product of the tlep-mass andthad-mass PDFt , i.e. PDFtlep(mcandtlep )×PDFthad(mcandthad ), is cho-sen, and the jets are labelled accordingly. The highest PDFtproduct value is denoted by Ptt¯ , and Ptt¯ divided by the sumof PDFt product values for all considered permutations isdenoted by Ptt¯ .However, a few physics-motivated requirements areapplied to limit the number of jet permutations. A maximumof six jets are considered when building the permutations.The b-jets are always considered, whereas the highest-pTnon-b-jets are considered first. The jets labelled as blep-jetsor bhad-jets have to be b-tagged and their PCFT scores arerequired to be greater than or equal to the PCFT scores ofj1 and j2. It is also required that the pT of j1 is greater thanthe pT of j2. This requirement removes redundant permuta-tions, since interchanging j1 and j2 yields identical values forthe top-quark and charged-Higgs-boson candidate masses.Finally, if multiple jets among bhad, j1, j2 are b-tagged andthe b-tagged jets have the same PCFT score, multiple per-mutations will yield the same top-quark candidate mass. Inthese rare cases the b-tagged jet with the larger pT is labelledas the bhad-jet. In 64% of the events entering the PDFt , allfour jets are labelled correctly. The performance for signalevents is comparable.6.2 Multivariate signal extractionThe multivariate signal extraction exploits differencesbetween the characteristics of the charged Higgs boson andthe W boson. These are the boson mass, spin and decay prop-erties. These differences are seen in many variables relatedto flavour-tagging and the kinematics of the top-quark decayproducts. The discriminating variables are combined into asingle discriminant through the use of BDTs.The BDT classifies events as signal-like or background-like. Background-like events receive BDT scores close to0, whereas signal-like events receive BDT scores close to 1.Separate BDTs are trained for each signal mass-point hypoth-esis with 5-fold cross-training and using the gradient boostingtechnique [102]. All simulated background samples listedin Sect. 3 are used in the BDT training. Events includedin the training have to pass the event selection described inSect. 4. Training and application are carried out within theXGBoost [103] framework.Any variable describing the properties of top quarks andtheir decay products is considered as an input to the BDTtraining. The final set of BDT input variables is obtained byrecursively removing variables with relatively small power toseparate the signal from the background, and low importancein the BDT training, until a statistically significant loss in per-formance is observed. The performance is quantified by thearea under the receiver-operating-characteristic curve. Thisoptimisation is performed with the 130 GeV signal sample.The signal and t t¯ background kinematics are very similar forthe signal mass points close to the W -boson mass but differgreatly for signal mass points closer to the top-quark mass;the intermediate 130 GeV mass point covers both cases.The final BDT uses the 26 input variables which arelisted in Table 2. The variables can be sorted into three cat-egories: top-quark kinematic variables, event variables, andflavour-tagging variables. The first category contains vari-ables related to the kinematics of the top quarks and theirdecay products. These variables are mainly sensitive to themass difference between the H± and W bosons. If the mass123Eur. Phys. J. C (2025) 85:153 Page 9 of 34 153 Fig. 3 Probability density functions of the reconstructed mass for a semileptonically and b hadronically decaying top quarksTable 2 Final list of BDT inputvariables used in the training Variable type Variable name DefinitionTop-quark kinematic variablesthad j1 pT pT of j1-labelled jetj2 pT pT of j2-labelled jetbhad pT pT of bhad-jetbthad−resthad p Momentum of bhad-jet in thad rest framedijet mass Invariant mass of j1+ j2 jets( j1+bhad) mass Invariant mass of j1+bhad jets( j2+bhad) mass Invariant mass of j2+bhad jetscos θ Boson spin sensitive variabletlep blep pT pT of blep-jetLepton pT pT of reconstructed leptonW mass Invariant mass of reconstructed W bosontlep mass Invariant mass of reconstructed tleptlep pT pT of reconstructed tlept t¯-system R(blep,bhad) R between the blep-jet and bhad-jett t¯ mass Invariant mass of thad+tlepEvent variablesEvent level Njets Number of jets in the eventST Scalar pT sum of all calibrated objectsPtt¯ Normalised probability of correct jet labellingFlavour-tagging variablesFlavour-tagging score j1 PCFT PCFT score of j1j2 PCFT PCFT score of j2bhad PCFT PCFT score of bhad-jetblep PCFT PCFT score of blep-jetNumber of tags Nc-tagLo Number of jets passing loose c-tag WP (b-veto)Nc-tagTi Number of jets passing tight c-tag WP (b-veto)Nb-tag70 Number of jets passing 70% b-tag WPNb-tag60 Number of jets passing 60% b-tag WP123 153 Page 10 of 34 Eur. Phys. J. C (2025) 85:153 difference is comparable to or larger than the dijet mass reso-lution [83], the kinematic variables exhibit larger separationpower than variables in the other categories. The most impor-tant variables (in order of decreasing area under the receiver-operating-characteristic curve) are the invariant mass of j1and j2 and the transverse momentum of the bhad-jet, fol-lowed by the invariant mass of j1 and bhad-jet, the invariantmass of j2 and bhad-jet, and the transverse momenta of j1 andj2. Variables related to the t t¯-system, the tlep-quark and thedecay products provide separation power between signal andnon-t t¯ backgrounds. In addition, they can carry informationabout possible wrong jet labelling and add information viacorrelation with other input variables.The cos θ variable is the only variable that is sensitiveto the spin of the boson from the thad decay and there-fore shows separation power for any H± boson mass. Theangle θ is defined as the angle between the bhad-jet and theup-type-quark-initiated jet from the hadronically decayingboson (H± or W ) in the boson’s rest frame. The up-type-quark-initiated jet is identified with the help of the PCFTscores of j1 and j2. The c-tagged jets are prioritised over b-tagged jets, which in turn are prioritised over untagged jets.If the PCFT scores of j1 and j2 are identical, j1 is assignedto be the jet from the up-type quark. The cos θ distribution isflat for spin-0 particles, like the H± boson. For spin-1 par-ticles, like the W boson, the distribution is more complexbecause polarisation effects play a role, and there are fewerevents close to −1 and 1. However, it is difficult to identifythe quark flavours in the decay of the boson. In addition, thecos θ distribution is heavily affected by jet resolution effectsbecause three jets are used in the calculation.The second category of variables involves event variables.The Ptt¯ variable facilitates the identification of wronglylabelled jets and can also reject non-t t¯ background. The Njetsand ST variables are correlated with most top-quark kine-matic variables. For example, an event with small bhad-jetpT and large ST suggests a signal event with a high-masscharged Higgs boson.The third category contains flavour-tagging variables,which are the PCFT scores of the four labelled jets and thenumber of jets passing a given PCFT working point. Of spe-cial interest are the PCFT scores of j1 and j2, as they aresensitive to the different decay characteristics of the chargedHiggs boson. While all signal events involve a c-quark in theboson decay, only about 50% of the t t¯ background eventsshare the same characteristic. Other flavour-tagging variablesare useful in rejecting non-t t¯ background and add informa-tion via correlations with other variables. The distributionsof selected BDT input variables of different types and withlarge separation power are shown in Fig. 4.The optimisation of the BDT input variables was per-formed with a baseline set of hyperparameters – includingtree depth and learning rate – determined by a lightweightBDT hyperparameter scan. For the final training, the optimalconfiguration of the BDT hyperparameters is determined foreach signal mass point separately with the help of the hyper-opt [104] tool. The hyperopt tool performs a Bayesian opti-misation using Tree-structured Parzen Estimators to obtainthe optimal parameter set from a given parameter range. It ismore efficient than grid or random searches because it usesprevious training steps to learn where the optimum is going tobe. To avoid any bias between the hyperparameter optimisa-tion dataset and the final test dataset, the scan was carried outusing nested cross-training. For each of the 5-folds the scanis performed using 4-fold cross-training on the other fourfolds. For the final training the BDTs are retrained using allfour folds as training data. The BDT-score distributions aftertraining with the H±80, H±110, H±130 and H±150 signal samplesare shown in Fig. 5.7 Systematic uncertaintiesThis section discusses systematic uncertainties the analysis issensitive to, including those affecting the detector response,theoretical uncertainties, and modelling of signal and back-ground processes that affect the normalisation and shapes ofthe simulated signal and background distributions. The indi-vidual systematic uncertainties are considered to be uncor-related, while correlations for a given systematic uncertaintyare maintained across signal and background processes.The uncertainty in the combined 2015–2018 integratedluminosity is 0.83% [31], obtained using the LUCID-2 detec-tor [28] for the primary luminosity measurements, com-plemented by measurements using the inner detector andcalorimeters. This uncertainty is assigned to all physics pro-cesses whose normalisations are taken from simulation. Anuncertainty in the correction of the pile-up distribution [105]in simulation to that in data is taken into account as well.Uncertainties in the calibration of physics objects affect allsimulated samples. Uncertainties associated with the trigger,reconstruction, identification and isolation efficiency calibra-tion [74,77], as well as the impact of the energy (momentum)scale and resolution uncertainties [74,106] on the selectionefficiency are considered for electrons (muons). Jet energyscale (JES) [107] and resolution (JER) [108] uncertaintiesand the uncertainty in the efficiency of matching jets to theprimary vertex [84] are taken into account. The energy scaleand resolution uncertainties for leptons and jets are propa-gated to the EmissT . In addition, the uncertainty in the EmissTfrom tracks matched to the primary vertex but not to otherreconstructed objects [90] is considered. Corrections to simu-lations to match the flavour-tagging and mistagging efficien-cies in data are taken into account in bins of the jet pT [86–88].123Eur. Phys. J. C (2025) 85:153 Page 11 of 34 153 Fig. 4 Distributions of selected BDT input variables. The variablesare defined in Table 2. The processes t t¯(allHad), tW , Single top, t t¯ H ,Other top, W + jets, Z + jets, V V listed in Table 1 are combined withthe multijet background in the “Other” category. The uncertainty bandrepresents the combined statistical and systematic uncertainty of theprediction. Overlaid are the shapes for the H±80 and H±150 signal samplesnormalised to the total background predictionUncertainties in the modelling of different processes areassumed to be uncorrelated. Modelling uncertainties for pro-cesses making a small contribution to the total backgroundyield (t t¯(allHad), t t¯ H and Other top samples) are neglected.Some modelling uncertainties are estimated in the same wayfor all processes. These are the uncertainties due to miss-123 153 Page 12 of 34 Eur. Phys. J. C (2025) 85:153 Fig. 5 BDT-score distributions for the training with the a 80 GeV, b110 GeV, c 130 GeV and d 150 GeV signal mass hypotheses afterperforming background-only binned-likelihood fits to the distributionsas described in Sect. 8. The processes t t¯(allHad), tW , Single top, t t¯ H ,Other top, W + jets, Z + jets, V V listed in Table 1 are combined with themultijet background in the “Other” category. The background yields arenormalised to their best-fit values. The uncertainty band represents thecombined statistical and systematic uncertainty of the prediction. Over-laid is the signal shape normalised to B(t → H±(→ cs)b) = 1%. Thebinning procedure for the BDT score is defined in Sect. 8ing higher orders in the perturbative expansion of the par-tonic cross-section, which are estimated by varying the renor-malisation (μr) and factorisation (μf ) scales by a factor oftwo. The uncertainties associated with the choice of PDFare evaluated by using dedicated PDF error eigensets. ThePDF uncertainties are combined by calculating their standard123Eur. Phys. J. C (2025) 85:153 Page 13 of 34 153 deviation with respect to the nominal set or by summing thedifferences in quadrature for Hessian sets. The uncertaintyrelated to the choice of strong coupling constant value αs isevaluated by comparing predictions using PDF sets obtainedwith two alternative αs values. The uncertainties in the pro-duction cross-sections are included as normalisation uncer-tainties [109–112] for all processes whose normalisations aretaken from simulation.For top-quark processes the uncertainties associated withthe choice of generator for ME and PS simulation are assessedby comparing the nominal sample with alternative samplesgenerated with aMC@NLO instead of PowhegBox andwith Herwig 7 instead of Pythia, respectively. The uncer-tainty due to the choice of hdamp parameter value is deter-mined by comparison with an alternative sample generatedwith hdamp = 3 mtop instead of 1.5 mtop. The alternativesamples are described in detail in Sect. 3. The interferencebetween tW (b) and t t¯ processes [47] is handled using the dia-gram removal scheme, which removes diagrams with inter-mediate top quarks. The uncertainty associated with this pro-cedure is evaluated with the help of an alternative tW samplewhere a subtraction term is added to the matrix element tocancel out the resonant top-quark pole contribution.For top-quark and signal processes the uncertainties in theamounts of initial- and final-state QCD radiation (ISR andFSR) are estimated by varying the corresponding parame-ter (Var3c) of the A14 PS tune and by varying the FSRscale (μFSRr ) by a factor of two, respectively. The system-atic uncertainty introduced by the t t¯ correction, described inSect. 5, is estimated by performing an eigenvalue decompo-sition of the fitted parameters and varying the eigenvaluesseparately by one standard deviation. Uncertainties relatedto the t t¯ ME, PS, hdamp parameter and FSR show, among theconsidered systematic uncertainties, the largest shape differ-ences in the high BDT-score regions and are therefore thedominant uncertainties in this search.For weak-boson processes, electroweak corrections atnext-to-leading order are estimated using the electroweak vir-tual approximation. The electroweak and QCD componentsare combined using an exponentiated prescription [113,114].CKKW and QSF are two parameters of Sherpa that definethe scale for merging/matching jets from the ME with the PS,and the scale used for resummation of soft gluon emissions,respectively. Their impact on the BDT input observables ismeasured at generator-level with the help of alternative sam-ples in which the nominal values are varied. The observeddifferences are covered by a 17% normalisation uncertainty.The effect of NNLO correction factors on the cross-sectionof single-boson processes is 5%. This is added as a normali-sation uncertainty to all weak-boson processes.To evaluate the systematic uncertainty of the multijetbackground estimate, an alternative estimate is made inwhich, instead of the lepton isolation criterion, a looser leptonidentification is used to define the ABCD regions. In addi-tion, a conservative 50% normalisation uncertainty is addedto account for statistical uncertainties of the transfer factorand MC uncertainties in regions B, C and D.The uncertainty in the expected event count in each bin,due to the finite MC sample sizes, is accounted for by oneGaussian-constrained parameter per bin, which representsthe total uncertainty of the MC event content in that bin [115].8 Statistical interpretationThe presence of a charged Higgs boson signal in data is quan-tified with the help of binned maximum-likelihood fits tothe BDT-score distributions. The statistical model is imple-mented using the Histfactory format [116]. Minimisationof the likelihood function is performed in the pyhf frame-work [117].The parameter of interest is the branching fraction of theprocess t → H±b (BH±), which is constrained to BH± ∈[0, 0.1],5 while assuming B(t → W b) + B(t → H±(→cs)b) = 1.0. The t t¯ cross-section scaling factor (μt t¯ ) and thefraction of t t¯ +HF events among t t¯ background events ( fHF)are unconstrained parameters in the fit, with nominal valuesof 1.0 and 0.1364, respectively. These parameters relate thesignal and background yields before the fit (pre-fit) and afterthe fit (post-fit) as follows:6NH±(post-fit) = μt t¯ × 2(1 − BH±) BH±× NH±(pre-fit),Ntt¯ +LF (post-fit) = μt t¯ × (1 − fHF) × (1 − BH±)2× Ntt¯ +LF (pre-fit),Ntt¯ +HF (post-fit) = μt t¯ × fHF × (1 − BH±)2× Ntt¯ +HF (pre-fit).The systematic uncertainties described in Sect. 7 areimplemented as Gaussian-constrained nuisance parameters(NPs) in the fit. Systematic uncertainties with just one com-ponent are symmetrised by mirroring the nominal template.All two-point systematic uncertainties and some reweight-ing systematic uncertainties with large statistical fluctuationsare smoothed. Uncertainties with a negligible impact on theuncertainty of BH± are removed from the likelihood fit toimprove numerical performance.The discovery significance of a signal in data is calculatedin a likelihood ratio test where the background-only hypoth-5 The upper bound on BH± is justified by measurements setting lowerbounds on B(t → W b) [118].6 The event yields before the fit are set to the total expected t t¯ yield, sothat NH± (pre-fit) = Ntt¯ +LF (pre-fit) = Ntt¯ +HF (pre-fit). For the nominalvalues of μt t¯ and fHF, and BH± = 0, one recovers the SM expectation.123 153 Page 14 of 34 Eur. Phys. J. C (2025) 85:153 esis is compared with the signal-plus-background hypoth-esis [119]. The asymptotic approximation is used to esti-mate the probability distribution of the test statistic [120].Upper limits on BH± are set by determining the BH± valuewhich can be rejected in 95% of the cases, i.e. at 95% con-fidence level (CL), with respect to the best-fit signal-plus-background hypothesis. The modified frequentist technique(CLs) [121] is used to avoid excluding signal models wherethe analysis has little sensitivity. The median upper limit,referred to as the expected upper limit, and its 1σ and 2σexpected variations are derived from a background-only Asi-mov dataset [120].The BDT-score templates are binned in such a way that thenumber of bins is minimised, while maximising the expectedsensitivity and ensuring the stability of the fit and the validityof the asymptotic approximation. This is achieved by startingfrom templates with 10,000 equal-width bins and iterativelymerging bins from right to left – starting from the most dis-criminating bins – until a bin in question fulfils all conditions.Each bin is required to contain at least 20 expected back-ground events, and the signal and background MC statisticaluncertainties have to be less than 20% and 10%, respectively.Each bin has to pass a given S/√B threshold defined by theS/√B ratio7 in the first merged bin. In order to retain shapeinformation in the low discriminating BDT-score region, amaximum of 1000 bins may be merged into a single bin.Additionally, the S/√B threshold may be adjusted for BDTscores with very high or very low separation power such thateach template has at least 19 and no more than 49 bins.9 ResultsThe binned maximum-likelihood fit described in Sect. 8 isperformed per signal mass-point hypothesis on the respec-tive BDT-score distribution. Table 3 shows the data and back-ground yields after the background-only fit of the BDT-scoredistribution trained with the 130 GeV signal mass point.The fitted μt t¯ values agree within their uncertainties withthe SM prediction of 1.0 for all mass points. The fHF param-eter is measured to be 0.19 ± 0.02 for the H±80 fit, which isthe most precise fit. The measured values of fHF in otherfits agree with this value within their uncertainties. This is alarger heavy-flavour fraction than predicted by simulations,but agrees with many other ATLAS analyses [20,122–126].The fitted BH± values are equal to, or compatible with, zerofor most signal mass hypotheses. The largest signal signifi-cance observed in data is for the 110 GeV mass point, witha local p-value of 5% (1.5σ ).7 S is the signal and B the total background prediction in the respectivebin.Table 3 Data and background yields after the background-only fit ofthe BDT-score distribution for the 130 GeV signal mass BDT training.For comparison, the expected signal yield for BH± = 1% is added.The sample names are defined in Table 1Name Post-fit yieldst t¯(ud) 1,400,000 ± 76,000t t¯(cs) 1,200,000 ± 92,000t t¯ +HF 710,000 ± 150,000tW 100,000 ± 23,000Single top 68,000 ± 28,000W + jets 70,000 ± 29,000Z + jets & V V 21,000 ± 9500Other top & t t¯(allHad) & t t¯ H 17,000 ± 450Multijet (MJ) 12,000 ± 6800Total background 3,600,000 ± 11,000Data 3,600,000H±130 (BH± = 1%) 38,000The impact of systematic uncertainties on the BH± mea-surement’s accuracy is estimated by fixing the NP under con-sideration to its post-fit value, performing the fit and compar-ing the uncertainty of the fitted BH± with the one from thenominal fit. The results of these fits when fixing a group ofsystematic uncertainties are summarised in Table 4. For the80 GeV signal hypothesis the kinematics are very similar forthe signal and t t¯ processes. Therefore, the flavour-tagginguncertainties have the largest impact on BH± . For other sig-nal hypotheses the t t¯ modelling NPs, especially those for theME, PS, FSR and hdamp uncertainties, have the largest impacton the BH± uncertainty. MC statistical uncertainties natu-rally become more important with finer BDT-score binning.Less impactful but still important are jet, single-top-quarkand weak-boson modelling uncertainties. Least impactful areluminosity, pile-up, lepton and EmissT -related uncertainties.Figure 6 shows the expected limits on BH± with their 1σand 2σ uncertainty bands. The expected limits are least strin-gent for the 80 GeV mass point, at about 2.3%, as this signalmass point is closest to the W-boson mass. For this masspoint, the flavour-tagging information is the most powerfuldiscriminant between signal and background. Moving awayfrom the W-boson mass the top-quark kinematics start to dif-fer more and the limits improve. The most stringent limits areexpected for the 150 GeV mass point, at about 0.077%. Closeto the top-quark mass threshold the limits weaken again as theacceptance decreases. The small acceptance is caused by thelow average momentum of the bhad-quark so that the result-ing jets often fail the kinematic requirements. The solid linein Fig. 6 represents the observed limits. The observed limitsvary between 0.066 to 3.6%. Expected and observed limitsagree within uncertainties.123Eur. Phys. J. C (2025) 85:153 Page 15 of 34 153 Table 4 Breakdown of the relative contributions to the uncertainty inthe extracted BH± in the likelihood fit to data. The contributions areobtained by fixing the relevant NPs to their post-fit values in the likeli-hood fit. The square root of the difference of the squares of the nominaluncertainty and obtained uncertainty is divided by the nominal uncer-tainty to obtain the relative impact. The sum in quadrature of the indi-vidual components differs from the total uncertainty due to correlationsbetween uncertainties in the different groups. The uncertainty from datastatistical uncertainties is determined from fits with all NPs fixed to theirpost-fit values. The total uncertainty in BH± for the fits with H±80 andH±150 is 1.2% and 0.04%, respectivelyH±80 H±150Category Relative contribution Category Relative contributionData statistical 6% Data statistical 38%Systematic 99.8% Systematic 93%Flavour-tagging 64% t t¯ modelling 72%MC statistical 64% MC statistical 35%t t¯ modelling 50% Weak-boson and MJ modelling 27%μt t¯ and fLF 21% Single-top-quark modelling 25%Jet 19% μt t¯ and fLF 24%Single-top-quark modelling 16% Jet 23%Luminosity and pile-up 15% Flavour-tagging 20%Weak-boson and MJ modelling 12% Lepton and EmissT 8%Signal modelling 8% Luminosity and pile-up 7%Lepton and EmissT 7% Signal modelling 5%Fig. 6 Observed (solid line) and expected (dotted line) upper limits onBH± for charged Higgs boson with masses between 60 and 168 GeV,assuming B(t → W b) + B(t → H±(→ cs)b) = 1.0. The ±1σ and±2σ variations around the expected upper limit are indicated by thegreen and yellow bands, respectively10 ConclusionsA search for a light charged Higgs boson produced in decaysof the top quark, t → H±b, with H± → cs is performed inthe H± mass range from 60 to 168 GeV. The data analysedcorresponds to 140 fb−1 of pp collisions at√s = 13 TeVrecorded with the ATLAS detector at the LHC between 2015and 2018. This analysis focuses on the lepton-plus-jets finalstate, characterised by an isolated electron or muon andat least four jets. The search exploits b-quark and c-quarkidentification techniques as well as multivariate methods tosuppress the dominant t t¯ background. No significant sig-nal excess is found in data. Observed and expected 95% CLupper limits on the branching fraction B(t → H±b), assum-ing B(t → W b) + B(t → H±(→ cs)b) = 1.0, are foundto range from 0.066 to 3.6% and 0.077 to 2.3%, respectively,depending on the mass of the charged Higgs boson. Theseare the first direct limits on B(t → H±b) in the H± → cschannel for charged Higgs bosons with masses of 60 GeV,70 GeV and 168 GeV, and currently the most stringent limitsfor masses between 120 GeV and 160 GeV.Acknowledgements We thank CERN for the very successful oper-ation of the LHC and its injectors, as well as the support staff atCERN and at our institutions worldwide without whom ATLAS couldnot be operated efficiently. The crucial computing support from allWLCG partners is acknowledged gratefully, in particular from CERN,the ATLAS Tier-1 facilities at TRIUMF/SFU (Canada), NDGF (Den-mark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany),INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), RAL (UK)and BNL (USA), the Tier-2 facilities worldwide and large non-WLCGresource providers. Major contributors of computing resources are listedin Ref. [127]. We gratefully acknowledge the support of ANPCyT,Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Aus-tria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRCand CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC,China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRFand DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France;SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece;RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel;INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Nether-lands; RCN, Norway; MNiSW, Poland; FCT, Portugal; MNE/IFA,Romania; MSTDI, Serbia; MSSR, Slovakia; ARIS and MVZI, Slove-123 153 Page 16 of 34 Eur. Phys. J. C (2025) 85:153 nia; DSI/NRF, South Africa; MICIU/AEI, Spain; SRC and WallenbergFoundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva,Switzerland; NSTC, Taipei; TENMAK, Türkiye; STFC/UKRI, UnitedKingdom; DOE and NSF, United States of America. Individual groupsand members have received support from BCKDF, CANARIE, CRCand DRAC, Canada; CERN-CZ, FORTE and PRIMUS, Czech Repub-lic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU andMarie Skłodowska-Curie Actions, European Union; Investissementsd’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFGand AvH Foundation, Germany; Herakleitos, Thales and Aristeia pro-grammes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; NCN and NAWA, Poland; La Caixa Bank-ing Foundation, CERCA Programme Generalitat de Catalunya andPROMETEO and GenT Programmes Generalitat Valenciana, Spain;Göran Gustafssons Stiftelse, Sweden; The Royal Society and Lev-erhulme Trust, United Kingdom. In addition, individual memberswish to acknowledge support from Armenia: Yerevan Physics Insti-tute (FAPERJ); CERN: European Organization for Nuclear Research(CERN PJAS); Chile: Agencia Nacional de Investigación y Desar-rollo (FONDECYT 1230812, FONDECYT 1230987, FONDECYT1240864); China: Chinese Ministry of Science and Technology (MOST-2023YFA1605700), National Natural Science Foundation of China(NSFC-12175119, NSFC 12275265, NSFC-12075060); Czech Repub-lic: Czech Science Foundation (GACR-24-11373S), Ministry of Edu-cation Youth and Sports (FORTE CZ.02.01.01/00/22_008/0004632),PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020European Research Council (ERC - 101002463); European Union:European Research Council (ERC-948254, ERC 101089007), Hori-zon 2020 Framework Programme (MUCCA - CHIST-ERA-19-XAI-00), European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU PE00000013), Italian Center for High PerformanceComputing, Big Data and Quantum Computing (ICSC, NextGenera-tionEU); France: Agence Nationale de la Recherche (ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR-0002), Investissements d’Avenir Labex (ANR-11-LABX-0012); Ger-many: Baden-Württemberg Stiftung (BW Stiftung-Postdoc Elitepro-gramme), Deutsche Forschungsgemeinschaft (DFG-469666862, DFG-CR 312/5-2); Italy: Istituto Nazionale di Fisica Nucleare (ICSC,NextGenerationEU), Ministero dell’Università e della Ricerca (PRIN-20223N7F8K-PNRR M4.C2.1.1); Japan: Japan Society for the Pro-motion of Science (JSPS KAKENHI JP22H01227, JSPS KAK-ENHI JP22H04944, JSPS KAKENHI JP22KK0227, JSPS KAK-ENHI JP23KK0245); Netherlands: Netherlands Organisation for Scien-tific Research (NWO Veni 2020-VI.Veni.202.179); Norway: ResearchCouncil of Norway (RCN-314472); Poland: Ministry of Science andHigher Education (IDUB AGH, POB8, D4 no 9722), Polish NationalAgency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001),Polish National Science Centre (NCN 2021/42/E/ST2/00350, NCNOPUS nr 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393,NCN and H2020 MSCA 945339, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148, UMO-2023/49/B/ST2/04085, UMO-2023/51/B/ST2/00920); Slovenia: SlovenianResearch Agency (ARIS grant J1-3010); Spain: Generalitat Valenciana(Artemisa, FEDER, IDIFEDER/2018/048), Ministry of Science andInnovation (MCIN and NextGenEU PCI2022-135018-2, MICIN andFEDER PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I), PROMETEO and GenTProgrammes Generalitat Valenciana (CIDEGENT/2019/027); Swe-den: Swedish Research Council (Swedish Research Council 2023-04654, VR 2018-00482, VR 2022-03845, VR 2022-04683, VR 2023-03403, VR grant 2021-03651), Knut and Alice Wallenberg Foun-dation (KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW2022.0358); Switzerland: Swiss National Science Foundation (SNSF-PCEFP2_194658); United Kingdom: Leverhulme Trust (LeverhulmeTrust RPG-2020-004), Royal Society (NIF-R1-231091); United Statesof America: U.S. Department of Energy (ECA DE-AC02-76SF00515),Neubauer Family Foundation.Data Availability Statement My manuscript has associated data ina data repository. [Author’s comment: HepData Link: https://www.hepdata.net/record/154176].Code Availability Statement My manuscript has associated code/software in a data repository. [Author’s comment: ATLAS collaborationsoftware is open source, and all code necessary to recreate an analysis ispublicly available. The Athena (http://gitlab.cern.ch/atlas/athena) soft-ware repository provides all code needed for calibration and uncertaintyapplication, with configuration files that are also publicly available viaDocker containers and cvmfs. The specific code and configurationswritten in support of this analysis are not public; however, these areinternally preserved.]Open Access This article is licensed under a Creative Commons Attri-bution 4.0 International License, which permits use, sharing, adaptation,distribution and reproduction in any medium or format, as long as yougive appropriate credit to the original author(s) and the source, pro-vide a link to the Creative Commons licence, and indicate if changeswere made. The images or other third party material in this articleare included in the article’s Creative Commons licence, unless indi-cated otherwise in a credit line to the material. If material is notincluded in the article’s Creative Commons licence and your intendeduse is not permitted by statutory regulation or exceeds the permit-ted use, you will need to obtain permission directly from the copy-right holder. 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Camarero Munoz27 ,P. Camarri77a,77b , M.T. Camerlingo73a,73b , D. Cameron37 , C. Camincher168 , M. Campanelli98 ,A. Camplani43 , V. Canale73a,73b , A.C. Canbay3a , E. Canonero97 , J. Cantero166 , Y. Cao165 , F. Capocasa27 ,M. Capua44b,44a , A. Carbone72a,72b , R. Cardarelli77a , J.C.J. Cardenas8 , G. Carducci44a,44b , T. Carli37 ,G. Carlino73a , J.I. Carlotto13 , B.T. Carlson132,p , E.M. Carlson168,159a , J. Carmignani94 , L. Carminati72a,72b ,A. Carnelli138 , M. Carnesale37 , S. Caron116 , E. Carquin140f , I.B. Carr107 , S. Carrá72a , G. Carratta24a,24b ,A.M. Carroll126 , M.P. Casado13,h , M. Caspar49 , F.L. Castillo4 , L. Castillo Garcia13 , V. Castillo Gimenez166 ,N.F. Castro133a,133e , A. Catinaccio37 , J.R. Catmore128 , T. Cavaliere4 , V. Cavaliere30 , N. Cavalli24a,24b ,L.J. Caviedes Betancourt23b , Y.C. Cekmecelioglu49 , E. Celebi83 , S. Cella37 , M.S. Centonze71a,71b ,V. Cepaitis57 , K. Cerny125 , A.S. Cerqueira84a , A. Cerri150 , L. Cerrito77a,77b , F. Cerutti18a , B. 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Ernani Martins Neto88 , M. Errenst174 ,M. Escalier67 , C. Escobar166 , E. Etzion155 , G. Evans133a , H. Evans69 , L.S. Evans97 , A. Ezhilov38 ,S. Ezzarqtouni36a , F. Fabbri24a,24b , L. Fabbri24a,24b , G. Facini98 , V. Fadeyev139 , R.M. Fakhrutdinov38 ,D. Fakoudis102 , S. Falciano76a , L.F. Falda Ulhoa Coelho37 , F. Fallavollita112 , G. Falsetti44a,44b ,J. Faltova136 , C. Fan165 , K.Y. Fan65b , Y. Fan14 , Y. Fang14,114c , M. Fanti72a,72b , M. Faraj70a,70b ,Z. Farazpay99 , A. Farbin8 , A. Farilla78a , T. Farooque109 , S.M. Farrington53 , F. Fassi36e , D. Fassouliotis9 ,M. Faucci Giannelli77a,77b , W.J. Fawcett33 , L. Fayard67 , P. Federic136 , P. Federicova134 , O.L. Fedin38,a ,M. Feickert173 , L. Feligioni104 , D.E. Fellers126 , C. Feng63b , Z. Feng117 , M.J. Fenton162 , L. Ferencz49 ,R.A.M. Ferguson93 , S.I. Fernandez Luengo140f , P. Fernandez Martinez68 , M.J.V. Fernoux104 , J. Ferrando93 ,A. Ferrari164 , P. Ferrari116,117 , R. Ferrari74a , D. Ferrere57 , C. Ferretti108 , D. 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Zwalinski371 Department of Physics, University of Adelaide, Adelaide, Australia2 Department of Physics, University of Alberta, Edmonton, AB, Canada3 (a)Department of Physics, Ankara University, Ankara, Turkey; (b)Division of Physics, TOBB University of Economicsand Technology, Ankara, Turkey4 LAPP, Université Savoie Mont Blanc, CNRS/IN2P3, Annecy, France5 APC, Université Paris Cité, CNRS/IN2P3, Paris, France6 High Energy Physics Division, Argonne National Laboratory, Argonne, IL, USA7 Department of Physics, University of Arizona, Tucson, AZ, USA8 Department of Physics, University of Texas at Arlington, Arlington, TX, USA9 Physics Department, National and Kapodistrian University of Athens, Athens, Greece10 Physics Department, National Technical University of Athens, Zografou, Greece11 Department of Physics, University of Texas at Austin, Austin, TX, USA12 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan13 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain14 Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China15 Physics Department, Tsinghua University, Beijing, China16 Institute of Physics, University of Belgrade, Belgrade, Serbia17 Department for Physics and Technology, University of Bergen, Bergen, Norway123 153 Page 30 of 34 Eur. Phys. J. C (2025) 85:153 18 (a)Physics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; (b)University of California, Berkeley,CA, USA19 Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany20 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern,Switzerland21 School of Physics and Astronomy, University of Birmingham, Birmingham, UK22 (a)Department of Physics, Bogazici University, Istanbul, Turkey; (b)Department of Physics Engineering, GaziantepUniversity, Gaziantep, Turkey; (c)Department of Physics, Istanbul University, Istanbul, Turkey23 (a)Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño, Bogotá, Colombia; (b)Departamentode Física, Universidad Nacional de Colombia, Bogotá, Colombia24 (a)Dipartimento di Fisica e Astronomia A. Righi, Universitá di Bologna, Bologna, Italy; (b)INFN Sezione di Bologna,Bologna, Italy25 Physikalisches Institut, Universität Bonn, Bonn, Germany26 Department of Physics, Boston University, Boston, MA, USA27 Department of Physics, Brandeis University, Waltham, MA, USA28 (a)Transilvania University of Brasov, Brasov, Romania; (b)Horia Hulubei National Institute of Physics and NuclearEngineering, Bucharest, Romania; (c)Department of Physics, Alexandru Ioan Cuza University of Iasi,Iasi, Romania; (d)National Institute for Research and Development of Isotopic and Molecular Technologies, PhysicsDepartment, Cluj-Napoca, Romania; (e)National University of Science and Technology Politechnica, Bucharest,Romania; (f)West University in Timisoara, Timisoara, Romania; (g)Faculty of Physics, University of Bucharest,Bucharest, Romania29 (a)Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia; (b)Department ofSubnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic30 Physics Department, Brookhaven National Laboratory, Upton, NY, USA31 Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Física, y CONICET, Institutode Físca de Buenos Aires (IFIBA), Buenos Aires, Argentina32 California State University, Los Angeles, CA, USA33 Cavendish Laboratory, University of Cambridge, Cambridge, UK34 (a)Department of Physics, University of Cape Town, Cape Town, South Africa; (b)iThemba Labs, Western Cape, SouthAfrica; (c)Department of Mechanical Engineering Science, University of Johannesburg,Johannesburg, South Africa; (d)National Institute of Physics, University of the Philippines Diliman (Philippines), QuezonCity, Philippines; (e)University of South Africa, Department of Physics, Pretoria, South Africa; (f)University of Zululand,KwaDlangezwa, South Africa; (g)School of Physics, University of the Witwatersrand, Johannesburg, South Africa35 Department of Physics, Carleton University, Ottawa, ON, Canada36 (a)Faculté des Sciences Ain Chock, Université Hassan II de Casablanca, Casablanca, Morocco; (b)Faculté des Sciences,Université Ibn-Tofail, Kénitra, Morocco; (c)Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech,Marrakech, Morocco; (d)LPMR, Faculté des Sciences, Université Mohamed Premier, Oujda, Morocco; (e)Faculté dessciences, Université Mohammed V, Rabat, Morocco; (f)Institute of Applied Physics, Mohammed VI PolytechnicUniversity, Ben Guerir, Morocco37 CERN, Geneva, Switzerland38 Affiliated with an institute covered by a cooperation agreement with CERN, Geneva, Switzerland39 Affiliated with an international laboratory covered by a cooperation agreement with CERN, Geneva, Switzerland40 Enrico Fermi Institute, University of Chicago, Chicago, IL, USA41 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France42 Nevis Laboratory, Columbia University, Irvington, NY, USA43 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark44 (a)Dipartimento di Fisica, Università della Calabria, Rende, Italy; (b)INFN Gruppo Collegato di Cosenza, LaboratoriNazionali di Frascati, Italy45 Physics Department, Southern Methodist University, Dallas, TX, USA46 Physics Department, University of Texas at Dallas, Richardson, TX, USA47 National Centre for Scientific Research “Demokritos”, Agia Paraskevi, Greece48 (a)Department of Physics, Stockholm University, Stockholm, Sweden; (b)Oskar Klein Centre, Stockholm, Sweden49 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen, Germany123Eur. Phys. J. C (2025) 85:153 Page 31 of 34 153 50 Fakultät Physik , Technische Universität Dortmund, Dortmund, Germany51 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden, Germany52 Department of Physics, Duke University, Durham, NC, USA53 SUPA-School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK54 INFN e Laboratori Nazionali di Frascati, Frascati, Italy55 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany56 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany57 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Geneva, Switzerland58 (a)Dipartimento di Fisica, Università di Genova, Genoa, Italy; (b)INFN Sezione di Genova, Genoa, Italy59 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany60 SUPA-School of Physics and Astronomy, University of Glasgow, Glasgow, UK61 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France62 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge, MA, USA63 (a)Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Scienceand Technology of China, Hefei, China; (b)Institute of Frontier and Interdisciplinary Science and Key Laboratory ofParticle Physics and Particle Irradiation (MOE), Shandong University, Qingdao, China; (c)School of Physics andAstronomy, Shanghai Jiao Tong University, Key Laboratory for Particle Astrophysics and Cosmology (MOE), SKLPPC,Shanghai, China; (d)Tsung-Dao Lee Institute, Shanghai, China; (e)School of Physics, Zhengzhou University, China64 (a)Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany; (b)Physikalisches Institut,Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany65 (a)Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong; (b)Department of Physics,University of Hong Kong, Hong Kong, China; (c)Department of Physics and Institute for Advanced Study, Hong KongUniversity of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China66 Department of Physics, National Tsing Hua University, Hsinchu, Taiwan67 IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405 Orsay, France68 Centro Nacional de Microelectrónica (IMB-CNM-CSIC), Barcelona, Spain69 Department of Physics, Indiana University, Bloomington, IN, USA70 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine, Italy; (b)ICTP, Trieste, Italy; (c)Dipartimento Politecnicodi Ingegneria e Architettura, Università di Udine, Udine, Italy71 (a)INFN Sezione di Lecce, Lecce, Italy; (b)Dipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy72 (a)INFN Sezione di Milano, Milan, Italy; (b)Dipartimento di Fisica, Università di Milano, Milan, Italy73 (a)INFN Sezione di Napoli, Naples, Italy; (b)Dipartimento di Fisica, Università di Napoli, Naples, Italy74 (a)INFN Sezione di Pavia, Pavia, Italy; (b)Dipartimento di Fisica, Università di Pavia, Pavia, Italy75 (a)INFN Sezione di Pisa, Pisa, Italy; (b)Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy76 (a)INFN Sezione di Roma, Rome, Italy; (b)Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy77 (a)INFN Sezione di Roma Tor Vergata, Rome, Italy; (b)Dipartimento di Fisica, Università di Roma Tor Vergata, Rome,Italy78 (a)INFN Sezione di Roma Tre, Rome, Italy; (b)Dipartimento di Matematica e Fisica, Università Roma Tre, Rome, Italy79 (a)INFN-TIFPA, Rome, Italy; (b)Università degli Studi di Trento, Trento, Italy80 Universität Innsbruck, Department of Astro and Particle Physics, Innsbruck, Austria81 University of Iowa, Iowa City, IA, USA82 Department of Physics and Astronomy, Iowa State University, Ames, IA, USA83 Istinye University, Sariyer, Istanbul, Turkey84 (a)Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF),Juiz de Fora, Brazil; (b)Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro, Brazil; (c)Instituto deFísica, Universidade de São Paulo, São Paulo, Brazil; (d)Rio de Janeiro State University, Rio de Janeiro, Brazil; (e)Federal University of Bahia, Bahia, Brazil85 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan86 Graduate School of Science, Kobe University, Kobe, Japan87 (a)AGH University of Krakow, Faculty of Physics and Applied Computer Science, Krakow, Poland; (b)MarianSmoluchowski Institute of Physics, Jagiellonian University, Krakow, Poland88 Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland89 Faculty of Science, Kyoto University, Kyoto, Japan123 153 Page 32 of 34 Eur. Phys. J. C (2025) 85:153 90 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka , Japan91 L2IT, Université de Toulouse, CNRS/IN2P3, UPS, Toulouse, France92 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina93 Physics Department, Lancaster University, Lancaster, UK94 Oliver Lodge Laboratory, University of Liverpool, Liverpool, UK95 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana,Ljubljana, Slovenia96 School of Physics and Astronomy, Queen Mary University of London, London, UK97 Department of Physics, Royal Holloway University of London, Egham, UK98 Department of Physics and Astronomy, University College London, London, UK99 Louisiana Tech University, Ruston, LA, USA100 Fysiska institutionen, Lunds universitet, Lund, Sweden101 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid, Spain102 Institut für Physik, Universität Mainz, Mainz, Germany103 School of Physics and Astronomy, University of Manchester, Manchester, UK104 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France105 Department of Physics, University of Massachusetts, Amherst, MA, USA106 Department of Physics, McGill University, Montreal, QC, Canada107 School of Physics, University of Melbourne, Victoria, Australia108 Department of Physics, University of Michigan, Ann Arbor, MI, USA109 Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA110 Group of Particle Physics, University of Montreal, Montreal, QC, Canada111 Fakultät für Physik, Ludwig-Maximilians-Universität München, München, Germany112 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München, Germany113 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan114 (a)Department of Physics, Nanjing University, Nanjing, China; (b)School of Science, Shenzhen Campus of Sun Yat-senUniversity, Shenzhen, China; (c)University of Chinese Academy of Science (UCAS), Beijing, China115 Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA116 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen, The Netherlands117 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, The Netherlands118 Department of Physics, Northern Illinois University, DeKalb, IL, USA119 (a)New York University Abu Dhabi, Abu Dhabi, United Arab Emirates; (b)United Arab Emirates University, Al Ain,United Arab Emirates120 Department of Physics, New York University, New York, NY, USA121 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo, Japan122 Ohio State University, Columbus, OH, USA123 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK, USA124 Department of Physics, Oklahoma State University, Stillwater, OK, USA125 Palacký University, Joint Laboratory of Optics, Olomouc, Czech Republic126 Institute for Fundamental Science, University of Oregon, Eugene, OR, USA127 Graduate School of Science, Osaka University, Osaka, Japan128 Department of Physics, University of Oslo, Oslo, Norway129 Department of Physics, Oxford University, Oxford, UK130 LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris, France131 Department of Physics, University of Pennsylvania, Philadelphia, PA, USA132 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA133 (a)Laboratório de Instrumentação e Física Experimental de Partículas - LIP, Lisbon, Portugal; (b)Departamento de Física,Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; (c)Departamento de Física, Universidade de Coimbra,Coimbra, Portugal; (d)Centro de Física Nuclear da Universidade de Lisboa, Lisbon, Portugal; (e)Departamento de Física,Universidade do Minho, Braga, Portugal; (f)Departamento de Física Teórica y del Cosmos, Universidad de Granada,Granada, Spain; (g)Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal134 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic135 Czech Technical University in Prague, Prague, Czech Republic123Eur. Phys. J. C (2025) 85:153 Page 33 of 34 153 136 Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic137 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, UK138 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France139 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, USA140 (a)Departamento de Física, Pontificia Universidad Católica de Chile, Santiago, Chile; (b)Millennium Institute forSubatomic physics at high energy frontier (SAPHIR), Santiago, Chile; (c)Instituto de Investigación Multidisciplinario enCiencia y Tecnología y Departamento de Física, Universidad de La Serena, La Serena, Chile; (d)Universidad AndresBello, Department of Physics, Santiago, Chile; (e)Instituto de Alta Investigación, Universidad de Tarapacá,Arica, Chile; (f)Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso, Chile141 Department of Physics, Institute of Science, Tokyo, Japan142 Department of Physics, University of Washington, Seattle, WA, USA143 Department of Physics and Astronomy, University of Sheffield, Sheffield, UK144 Department of Physics, Shinshu University, Nagano, Japan145 Department Physik, Universität Siegen, Siegen, Germany146 Department of Physics, Simon Fraser University, Burnaby, BC, Canada147 SLAC National Accelerator Laboratory, Stanford, CA, USA148 Department of Physics, Royal Institute of Technology, Stockholm, Sweden149 Departments of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA150 Department of Physics and Astronomy, University of Sussex, Brighton, UK151 School of Physics, University of Sydney, Sydney, Australia152 Institute of Physics, Academia Sinica, Taipei, Taiwan153 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia; (b)High EnergyPhysics Institute, Tbilisi State University, Tbilisi, Georgia; (c)University of Georgia, Tbilisi, Georgia154 Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel155 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel156 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece157 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo, Japan158 Department of Physics, University of Toronto, Toronto, ON, Canada159 (a)TRIUMF, Vancouver, BC, Canada; (b)Department of Physics and Astronomy, York University, Toronto, ON, Canada160 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences,University of Tsukuba, Tsukuba, Japan161 Department of Physics and Astronomy, Tufts University, Medford, MA, USA162 Department of Physics and Astronomy, University of California Irvine, Irvine, CA, USA163 University of Sharjah, Sharjah, United Arab Emirates164 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden165 Department of Physics, University of Illinois, Urbana, IL, USA166 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Valencia, Spain167 Department of Physics, University of British Columbia, Vancouver, BC, Canada168 Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada169 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany170 Department of Physics, University of Warwick, Coventry, UK171 Waseda University, Tokyo, Japan172 Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot, Israel173 Department of Physics, University of Wisconsin, Madison, WI, USA174 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal,Germany175 Department of Physics, Yale University, New Haven, CT, USAa Also Affiliated with an Institute Covered by a Cooperation Agreement with CERN, Geneva, Switzerlandb Also at An-Najah National University, Nablus, Palestinec Also at Borough of Manhattan Community College, City University of New York, New York, NY, USAd Also at Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki, Greecee Also at CERN, Geneva, Switzerland123 153 Page 34 of 34 Eur. Phys. J. C (2025) 85:153 f Also at CMD-AC UNEC Research Center, Azerbaijan State University of Economics (UNEC), Azerbaijang Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève, Switzerlandh Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spaini Also at Department of Financial and Management Engineering, University of the Aegean, Chios, Greecej Also at Department of Physics, California State University, Sacramento, USAk Also at Department of Physics, King’s College London, London, UKl Also at Department of Physics, Stanford University, Stanford, CA, USAm Also at Department of Physics, Stellenbosch University, South African Also at Department of Physics, University of Fribourg, Fribourg, Switzerlando Also at Department of Physics, University of Thessaly, Greecep Also at Department of Physics, Westmont College, Santa Barbara, USAq Also at Faculty of Physics, Sofia University, ’St. Kliment Ohridski’, Sofia, Bulgariar Also at Hellenic Open University, Patras, Greeces Also at Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabiat Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spainu Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germanyv Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia,Bulgariaw Also at Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir, Moroccox Also at Institute of Particle Physics (IPP), Ottawa, Canaday Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijanz Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgiaaa Also at National Institute of Physics, University of the Philippines Diliman (Philippines), Philippinesab Also at Technical University of Munich, Munich, Germanyac Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, Chinaad Also at TRIUMF, Vancouver, BC, Canadaae Also at Università di Napoli Parthenope, Naples, Italyaf Also at University of Colorado Boulder, Department of Physics, Colorado, USAag Also at Washington College, Chestertown, MD, USAah Also at Yeditepe University, Physics Department, Istanbul, Turkey∗Deceased123
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Search for a light charged Higgs boson in t → H±b decays, with H± → cs, in pp collisions at √s = 13 TeV with the ATLAS detector
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