Eur. Phys. J. C (2023) 83:438 https://doi.org/10.1140/epjc/s10052-023-11427-9 Regular Article - Experimental Physics Measurement of the nuclear modification factor of b-jets in 5.02 TeV Pb+Pb collisions with the ATLAS detector ATLAS Collaboration� CERN, 1211 Geneva 23, Switzerland Received: 29 April 2022 / Accepted: 9 August 2022 / Published online: 25 May 2023 © CERN for the benefit of the ATLAS collaboration 2023 Abstract This paper presents a measurement of b-jet pro- duction in Pb+Pb and pp collisions at √ sNN = 5.02 TeV with the ATLAS detector at the LHC. The measurement uses 260 pb−1 of pp collisions collected in 2017 and 1.4 nb−1 of Pb+Pb collisions collected in 2018. In both collision sys- tems, jets are reconstructed via the anti-kt algorithm. The b-jets are identified from a sample of jets containing muons from the semileptonic decay of b-quarks using template fits of the muon momentum relative to the jet axis. In pp colli- sions, b-jets are reconstructed for radius parameters R = 0.2 and R = 0.4, and only R = 0.2 jets are used in Pb+Pb colli- sions. For comparison, inclusive R = 0.2 jets are also mea- sured using 1.7 nb−1 of Pb+Pb collisions collected in 2018 and the same pp collision data as the b-jet measurement. The nuclear modification factor, RAA, is calculated for both b-jets and inclusive jets with R = 0.2 over the transverse momen- tum range of 80–290 GeV. The nuclear modification factor for b-jets decreases from peripheral to central collisions. The ratio of the b-jet RAA to inclusive jet RAA is also presented and suggests that the RAA for b-jets is larger than that for inclusive jets in central Pb+Pb collisions. The measurements are compared with theoretical calculations and suggest a role for mass and colour-charge effects in partonic energy loss in heavy-ion collisions. Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . 1 2 ATLAS detector . . . . . . . . . . . . . . . . . . . 3 3 Data selection and Monte Carlo samples . . . . . . 3 4 Analysis . . . . . . . . . . . . . . . . . . . . . . . 4 4.1 Jet reconstruction . . . . . . . . . . . . . . . . 4 4.2 Muon reconstruction . . . . . . . . . . . . . . 5 4.3 b-jet yield reconstruction . . . . . . . . . . . . 5 4.3.1 Templates and fitting . . . . . . . . . . . 5 4.3.2 Comparing muon momentum distribu- tions in the data and simulations . . . . . 6 � e-mail: atlas.publications@cern.ch 4.4 Corrections to the raw spectra . . . . . . . . . . 6 4.5 Observables . . . . . . . . . . . . . . . . . . . 8 5 Systematic uncertainties . . . . . . . . . . . . . . . 9 6 Results . . . . . . . . . . . . . . . . . . . . . . . . 13 6.1 Cross-section in pp collisions . . . . . . . . . . 13 6.2 Per-event yields and RAA in Pb+Pb collisions . 14 7 Summary . . . . . . . . . . . . . . . . . . . . . . . 16 References . . . . . . . . . . . . . . . . . . . . . . . . 17 1 Introduction The primary physics aim of the heavy-ion programme at the Large Hadron Collider (LHC) is to produce and study the quark–gluon plasma (QGP), the high-temperature state of quantum-chromodynamic (QCD) matter in which quarks and gluons are no longer confined within protons and neutrons (for a recent review, see Ref. [1]). Measurements of jets aris- ing from hard-scattered partons produced in the early stages of heavy-ion collisions provide information about the short- distance-scale interactions of high-energy partons with the QGP. The overall rate of jets in central Pb+Pb collisions at a given transverse momentum, pT , is found to be significantly lower than expectations based on pp collisions, up to a pT of approximately 1 TeV [2–4]. The showering partons which give rise to jets undergo medium-induced gluon radiation and elastic scattering off the QGP constituents; both processes broaden the angular distribution of the energy from the parton shower [5]. This leads to the measurement of lower-energy jets than in pp collisions as some of the parton’s energy is moved outside of the jet cone. The interactions between high-energy partons and the QGP are expected to depend on the parton’s QCD colour charge and mass. Thus, jets originating from b-quarks, b- jets, (for a recent review, see Ref. [6]) are of particular interest because the quark mass is large compared to light quarks. Additionally, the QCD colour charge is controlled; this contrasts with inclusive jets, which mostly originate from a mixture of light quarks and gluons. The b-jets produced 123 http://crossmark.crossref.org/dialog/?doi=10.1140/epjc/s10052-023-11427-9&domain=pdf mailto:atlas.publications@cern.ch 438 Page 2 of 32 Eur. Phys. J. C (2023) 83 :438 in hadronic collisions consist of jets in which the b-quark is directly produced in the hard scattering (e.g. gg → bb̄) and jets in which a hard-scattered gluon splits into a bb̄ pair (g → bb̄) as part of the showering process. Inclusive jets and b-jets are expected to be sensitive to dif- ferent effects in the QGP. Medium-induced gluon radiation is expected to be suppressed for heavy quarks by the dead- cone effect [7]. As the velocity of the b-quark increases the importance of the dead-cone effect decreases. Calculations for Pb+Pb collisions show that the dead-cone effect reduces the suppression of B-mesons up to transverse momenta of approximately 50 GeV [8]. This does not simply translate to a specific jet pT range becauseb-quarks in jets from gluon split- ting tend to have lower momentum than those directly from hard scattering at the same jet pT . Differences in the inter- actions between the QGP and the developing parton shower between b-jets and inclusive jets could also arise from differ- ences in how quarks and gluons interact with the QGP [9]. Measurements of heavy-quark jets in heavy-ion collisions are also expected to be sensitive to the mixture of radiative and collisional energy loss in the QGP [10–12]. Due to these different effects, which are expected to lead to differences between b-jets and inclusive jets in Pb+Pb collisions, it is important to compare the b-jet and inclusive-jet suppression in Pb+Pb collisions over as wide a kinematic range as possi- ble. Prior measurements have investigated the effects of energy loss for heavy quarks. Many measurements are based on elec- trons or muons from the semileptonic decay of charm and bottom hadrons [13–17]. Other measurements have partially [18–21] or fully [22–27] reconstructed the heavy-flavour hadrons. One limitation of these measurements is that they do not provide direct information about the total jet energy. This is important because the fragmentation functions of b- jets are very different from those of inclusive jets [28]; thus, it is not possible to directly compare the suppression of b- quarks with that of light quarks and gluons without full jet measurements. CMS has measured the suppression of b-jets in Pb+Pb collisions at 2.76 TeV and found it to be consistent with that of inclusive jets over the jet transverse momentum, pjet T , range of 70–250 GeV [29], although the uncertainties are large. For the measurements reported in this article, jets are clus- tered with the anti-kt algorithm [30] using radius parameters R = 0.2 (in both Pb+Pb and pp collisions) and R = 0.4 (only in pp collisions). At generator level, a jet is considered as a b-jet if a b-hadron with pT > 5 GeV is found within an angular distance1 of �R = 0.3 from the jet axis. The b-jets 1 ATLAS uses a right-handed coordinate system with its origin at the nominal 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 centre of the LHC ring, and the y-axis points upward. Cylindrical are identified statistically from a sample of jets containing muons. The muons from b-hadron decays are distinguished from muons arising from charm- and light-hadron decays by the difference between their transverse momenta, pμ T , relative to the jet+μ axis. The jet+μ axis is defined in the transverse plane by �ujet+μ T = �pμ T + �pjet T ∣ ∣ ∣ �pμ T + �pjet T ∣ ∣ ∣ (1) (where the vector notation indicates the vector quantity is meant instead of the magnitude of the vector), and the trans- verse momentum of the muon relative to the jet axis, prel T , is defined as prel T = ∣ ∣ ∣ �pμ T × �ujet+μ T ∣ ∣ ∣ . (2) This method was used previously by ATLAS for pp colli- sions at 7 TeV and the results were compared with those based on the reconstruction of secondary vertices [31]. Only R = 0.2 jets are used in Pb+Pb collisions because they have better angular resolution and have a smaller contribution from accidental jet–muon overlaps in Pb+Pb collisions; both of these features improve the performance of the prel T method. This paper reports measurements of inclusive jet and b-jet cross-sections in pp collisions and the per-event yield for inclusive jets and b-jets in Pb+Pb collisions at 5.02 TeV. In both collision systems, the spectra are corrected to the generator level (just before the b-hadron decays for b-jets). For R = 0.2 jets the nuclear modification factor, RAA, is calculated for b-jets as R b-jet AA ≡ 1 Nevt d2N b-jet AA dpT dy ∣ ∣ ∣ ∣ ∣ ∣ cent / 〈TAA〉d2σ b-jet pp dpT dy where d2N b-jet AA /dpT dy is the yield of b-jets in Pb+Pb col- lisions for the centrality, pT , and rapidity range of interest; d2σ b-jet pp /dpT dy is the b-jet cross-section in pp collisions; 〈TAA〉 is the nuclear thickness function [32]; and Nevt is the number of minimum-bias (MB) Pb+Pb events for the central- Footnote 1 continued coordinates (r, φ) are used in the transverse plane, φ being the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle θ as η = − ln tan(θ/2). The rapidity is defined as y = 0.5 ln[(E + pz)/(E − pz)] where E and pz are the energy and z-component of the momentum along the beam direction, respec- tively. Transverse momentum and transverse energy are defined as pT = p sin θ and ET = E sin θ , respectively. The angular distance between two objects with relative differences �η in pseudorapidity and �φ in azimuth is given by �R = √ (�η)2 + (�φ)2. 123 Eur. Phys. J. C (2023) 83 :438 Page 3 of 32 438 ity selection under consideration. Analogously, the nuclear modification factor for inclusive jets is defined as Rinclusive jet AA ≡ 1 Nevt d2N inclusive jet AA dpT dy ∣ ∣ ∣ ∣ ∣ cent / 〈TAA〉d2σ inclusive jet pp dpT dy where the yield and cross-section are for inclusive jets and the other quantities remain the same. The measurements are performed in Pb+Pb collisions at√ sNN = 5.02 TeV collected during 2018, with an inte- grated luminosity of 1.4 nb−1 for the b-jet sample and 1.7 nb−1 for the inclusive jet sample, and in pp collisions at √ s = 5.02 TeV collected during 2017, with an integrated luminosity of 260 pb−1. The measurements are presented for jets with 80 < pT < 290 GeV and |y| < 2.1. The paper is structured as follows. Section 2 describes the ATLAS detector, and Sect. 3 discusses the selection pro- cedure applied to the data. The data analysis is presented in Sect. 4 and the systematic uncertainties are presented in Sect. 5. The results and a summary are presented in Sects. 6 and 7. 2 ATLAS detector The ATLAS detector [33] at the LHC covers nearly the entire solid angle around the collision point. It consists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic and hadronic calorimeters, and a muon spectrometer incorporating three large superconduct- ing toroidal magnets. The inner-detector system (ID) is immersed in a 2 T axial magnetic field and provides charged-particle tracking in the range |η| < 2.5. The high-granularity silicon pixel detec- tor covers the vertex region, and is composed of four layers including the insertable B-layer [34,35]. It is followed by the silicon microstrip tracker, which usually provides four two-dimensional measurement points per track. These sili- con detectors are complemented by the transition radiation tracker (TRT), which enables radially extended track recon- struction up to |η| = 2.0. 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) electromagnetic calorime- ters, with an additional thin LAr presampler covering |η| < 1.8 to correct for energy loss in material upstream of the calorimeters. The hadronic calorimeters have three sampling layers longitudinal in shower depth in |η| < 1.7 and four sampling layers in 1.5 < |η| < 3.2, with a slight overlap in η. The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modules (FCal) optimised for electromagnetic and hadronic measurements respectively. The muon spectrometer (MS) comprises separate trigger and high-precision tracking chambers measuring the deflec- tion of muons in a magnetic field generated by the super- conducting air-core toroids. The field integral of the toroids ranges between 2.0 and 6.0 T m across most of the detec- tor. A set of precision chambers covers the region |η| < 2.7 with three layers of monitored drift tubes, complemented by cathode strip chambers in the forward region, where the background is highest. The muon trigger system covers the range |η| < 2.4 with resistive plate chambers in the barrel, and thin gap chambers in the endcap regions. The zero-degree calorimeters (ZDCs) are located sym- metrically at z = ±140 m and cover |η| > 8.3 during the Pb+Pb data-taking period. The ZDCs use tungsten plates as absorbers and quartz rods sandwiched between the tungsten plates as the active medium. In Pb+Pb collisions the ZDCs primarily measure spectator neutrons, which are neutrons that do not interact hadronically when the incident nuclei col- lide. A ZDC coincidence trigger is implemented by requiring the pulse height from each ZDC to be above a threshold set to accept the energy of a single neutron. A two-level trigger system is used to select interesting events [36]. The first-level trigger is implemented in hard- ware and uses a subset of detector information, including ZDC coincidences in Pb+Pb collisions, to reduce the event rate to a design value of at most 100 kHz. This is followed by a software-based high-level trigger which reduces the event rate to several kHz. An extensive software suite [37] is used in the reconstruction and analysis of real and simulated data, in detector operations, and in the trigger and data acquisition systems of the experiment. 3 Data selection and Monte Carlo samples All events are required to have at least one reconstructed vertex, to have been collected during stable beam conditions, and to satisfy detector and data-quality requirements [38]. These events were selected by two sets of triggers: single- jet triggers, and muon–jet triggers requiring a muon spatially matched to a jet at the trigger level [39]. In both Pb+Pb and pp collisions, the muon–jet triggers require a muon with pT > 4 GeV matched to an R = 0.4 jet with various pT thresholds. The spatial matching of the jet to the muon is made within a cone of size �R = 0.5 around the jet axis; this matching is fully efficient. All events are in a kinematic range where the jet trigger is fully efficient. The overlap area of two colliding nuclei in Pb+Pb colli- sions is characterized by the total transverse energy deposited in the FCal [40]. This analysis uses three centrality intervals which are defined according to successive percentiles of the 123 438 Page 4 of 32 Eur. Phys. J. C (2023) 83 :438 Table 1 The 〈TAA〉 and 〈Ncoll〉 values and uncertainties in each central- ity bin. These are the results from the Glauber modelling of the summed transverse energy in the forward calorimeters, ∑ EFCal T Centrality (%) 〈TAA〉 (mb−1) 〈Ncoll〉 0–20 18.84 ± 0.18 1319 ± 90 20–50 5.41 ± 0.15 378 ± 23 50–80 0.69 ± 0.05 48 ± 4 ∑ EFCal T distribution obtained in MB collisions. The cen- trality regions used in this analysis, going from the most central (largest ∑ EFCal T ) collisions to the peripheral (lowest ∑ EFCal T ) collisions are 0–20%, 20–50%, and 50–80%. The values of 〈TAA〉 and the number of binary nucleon–nucleon collisions, 〈Ncoll〉, for each centrality interval are evaluated by a Monte Carlo (MC) Glauber model analysis [32,41] of the ∑ EFCal T distribution, and are shown in Table 1. A small fraction of the triggered Pb+Pb events (< 0.5%) contain multiple collisions, known as ‘pile-up’. The expected anti-correlation between ∑ EFCal T and the number of neu- trons detected in the ZDCs is used to reject these pile-up events. The pile-up contribution is not rejected in pp colli- sions. This analysis uses several MC samples to evaluate the per- formance of the detector and the analysis procedure, and to correct the measured distributions for detector effects. All MC samples were produced with the full ATLAS detector simulation based on the GEANT44 toolkit [42,43]. Dijet samples were generated with Pythia8 [44], using the param- eter values of the A14 tune [45] with the NNPDF23lo set of parton distribution functions (PDFs) [46]. These were gen- erated both with no additional requirements and with the requirement of a muon with pT > 3 GeV at the genera- tor level (such as from heavy-flavour hadron decays). The EVTGEN package [47] was used to fully simulate b-hadron decays. In pp collisions, pile-up from additional interactions in the same bunch crossing was generated by Pythia8, using the parameter values of the A3 tune [48] with the NNPDF23lo PDFs; the distribution of the number of extra collisions was matched to that of data. In Pb+Pb collisions the simulated events were overlaid with events from a dedicated sample of Pb+Pb data events. This sample was recorded with a combi- nation of MB triggers and total energy triggers to increase the number of events from central collisions. This ‘data overlay’ sample was reweighted on an event-by-event basis to obtain the same centrality distribution as in the jet-triggered data sample. For muon reconstruction studies, prompt (pp → J/ψ → μμ) and non-prompt (pp → bb → J/ψ → μμ) samples of J/ψ events were produced with Pythia8 and corrected for electromagnetic radiation with Photos [49]. The A14 tune and CTEQ6L1 PDFs [50] were used. Generator-level pp Herwig++ [51] events, using the UEEE5 tune [52] and the CTEQ6L1 PDFs [50], were used to study systematic uncer- tainties. 4 Analysis 4.1 Jet reconstruction All jets are reconstructed using procedures which follow those used by ATLAS for previous jet measurements in Pb+Pb collisions [2,53]. Jets are reconstructed using the anti-kt algorithm [30] implemented in the FastJet software package [54]. In both pp and Pb+Pb collisions, jets with R = 0.2 and R = 0.4 are formed by clustering calorimetric towers of spatial size �η×�φ = 0.1×π/32. The energies in the towers are obtained by summing the energies of calorime- ter cells at the electromagnetic energy scale [55] within the tower boundaries. In Pb+Pb collisions, a background sub- traction procedure is applied to estimate, within each event, the underlying event (UE) average transverse energy density, ρ(η, φ), where the φ dependence is due to global azimuthal correlations in the particle production from hydrodynamic flow [40]. The modulation accounts for the contribution to the UE of the second-, third-, and fourth-order azimuthal anisotropy harmonics characterized by values of flow coeffi- cients vUE n [40]. Additionally, the UE is also corrected for η- and φ-dependent non-uniformities of the detector response by correction factors derived in MB Pb+Pb data. In pp colli- sions, the same background subtraction procedure is applied without the φ-dependent modulation and without the correc- tion for η- and φ-dependent non-uniformities to remove the pile-up contribution to the jet. An iterative procedure is used to remove the impact of jets on the estimated ρ and vUE n values. The first estimate of the average transverse energy density of the UE, ρ(η), is evaluated in 0.1 intervals of η, excluding towers within �R = 0.4 of ‘seed’ jets. In the first subtraction step, the seeds are defined to be an union of R = 0.2 jets and R = 0.4 track- jets. Track-jets are reconstructed by applying the anti-kt algo- rithm with R = 0.4 to charged particles with pT > 4 GeV. The R = 0.2 jets must pass a requirement on the minimum value of the tower ET and on a ratio of maximum tower ET to average tower ET, while the track-jets are required to have pT > 7 GeV. The background is then subtracted from each tower constituent and the jet kinematics are recalcu- lated. After the first iteration, the ρ and vn values are updated by excluding from the UE determination the regions within �R = 0.4 of both the track-jets and the newly reconstructed R = 0.2 jets with pT > 25 GeV (8 GeV) in Pb+Pb (pp) collisions. The updated ρ and vUE n values are used to update the jet kinematic properties in the second iteration. 123 Eur. Phys. J. C (2023) 83 :438 Page 5 of 32 438 Jet η- and pT-dependent correction factors derived in sim- ulations are applied to the measured jet energy to correct for the calorimeter energy response [56]. An additional correc- tion based on in situ studies of jets recoiling against pho- tons, Z bosons, and jets in other regions of the calorime- ter is applied [57]. This calibration is followed by a cross- calibration which relates the jet energy scale of jets recon- structed by the procedure outlined in this section to the jet energy scale in 13 TeV pp collisions [58]. Jets are defined at the truth level in the MC sample before detector simulation by applying the anti-kt algorithm with the appropriate R value to stable particles with a proper life- time greater than 30 ps, but excluding muons and neutri- nos, which do not leave significant energy deposits in the calorimeter. The �R between the truth jet and reconstructed jet is required to be �R < 0.15 (0.30) for R = 0.2 (R = 0.4) jets. The corrections for muons and neutrinos from semilep- tonic decays are discussed below. 4.2 Muon reconstruction Muon candidates in both Pb+Pb and pp collisions are formed by combining charged-particle tracks reconstructed in the ID and the MS that pass the ‘tight’ selection requirements detailed in Ref. [59], except the requirement on the number of TRT hits. Muons are selected with pμ T > 4 GeV and |ημ| < 2.4 requirements. If the muon is required in the trigger, the data is corrected to account for the muon trigger inefficiency, and the reconstructed muon must be within �R = 0.01 of the trigger muon object. The muon selection is the same in both pp collisions and Pb+Pb collisions. Muons and jets are judged to be associated if �R(jet, μ) is less than the jet radius, R. If more than one muon passes this selection, the muon with the largest momentum is used. The muon trigger efficiency with respect to reconstructed muons is estimated from pp data by using the tag-and-probe method [16,60] in bins of pμ T and ημ. A small centrality dependence of the muon trigger efficiency was observed in Pb+Pb data; this is corrected by an extra factor, which is the Pb+Pb to pp data-driven efficiency ratio, applied to Pb+Pb data as a function of the centrality. In pp collisions, the trigger efficiency plateaus at 78% for pμ T > 6 GeV and |ημ| < 1.05, and at 90% for pμ T > 9 GeV and |ημ| > 1.05. In Pb+Pb collisions, the efficiency is lower than in pp collisions by 9% in the most central collisions; the efficiency in peripheral Pb+Pb collisions is the same as in pp collisions. The muon reconstruction efficiency is estimated from sim- ulated prompt and non-prompt J/ψ → μμ events using the tag-and-probe method [16,59], in fine bins of pμ T and ημ. Mis-modelling of the reconstruction performance in simula- tion, quantified by the ratio of measured efficiencies in pp data and the simulation, is accounted for by applying a multi- plicative correction which changes the efficiency by less than 5%. For pμ T > 10 GeV in pp collisions, the muon reconstruc- tion efficiency plateaus at 90% for |ημ| < 1.05 and at 95% for |ημ| > 1.05. No difference between the muon reconstruction efficiencies in pp and Pb+Pb collisions was observed and the same values were used in both collision systems. 4.3 b-jet yield reconstruction 4.3.1 Templates and fitting At the generator level, the jet flavour is defined by matching jets to hadrons with pT > 5 GeV. The jet is considered a b-jet if a b-hadron is found within �R = 0.3 of the jet axis; otherwise, if a c-hadron is found within the same distance the jet is labelled as a c-jet. All other jets are considered to be light-jets. Since the muons in the momentum range of interest gen- erally do not stop in the calorimeter, their momentum is not included in the calorimetric jet pT . In order to better char- acterize the jets, the jet+μ scale is defined as the pT of the sum of the muon and jet four-vectors. The prel T distributions in the data and MC samples are constructed for selections in this jet+μ object at the reconstruction level, pjet+μ T,reco. In order to form a jet+μ pair, jets are required to have a mini- mum calorimetric pjet T,reco of 40 GeV (58 GeV) in pp (Pb+Pb) collisions. In semileptonic b-hadron decays, the pT of the lepton rel- ative to the jet+μ-axis (see Eqs. (1) and (2)), prel T , is used to distinguish between b-, c-, and light-jets[31]. Due to the large mass of b-hadrons relative to hadrons containing charm or light quarks, their decay products are more energetic. Con- sequently, muons originating from b-hadron decays have a harder prel T spectrum than muons in c-jets and light-jets. The templates distributions of prel T for b- and c-jets are extracted from the muon-filtered MC samples. The b-hadron mixture is taken from Tevatron measurements [61], which are consistent with those from the LHCb Collaboration [62]. The c-hadron mixture is taken from default Pythia8 simu- lation. The light-jet template is obtained from track-jet pairs in the data inclusive-jet sample as in Refs. [31,63] (in the same centrality class as the jet+μ pair for Pb+Pb collisions). The tracks are selected with the standard selection, with no additional requirements on the distance of closest approach to the vertex. The pT spectra of the tracks are reweighted to reproduce the muon pT spectrum from light-jets from the inclusive dijet MC sample. In pp collisions, these three templates are inputs to a tem- plate fit of the data prel T spectrum to obtain the fractions of b-, c-, and light-jets. A binned maximum-likelihood fit is per- formed using the RooFit framework [64]. The model for the fit is defined as a normalized sum of three templates: light- 123 438 Page 6 of 32 Eur. Phys. J. C (2023) 83 :438 jets (Fl,i (prel T )), c-jets (Fc,i (prel T )), and b-jets(Fb,i (prel T )). The pp fit model, Mpp, is structured as Mpp i ( prel T ) = fb,i Fb,i ( prel T ) + ( 1 − fb,i ) × [ fc,i Fc,i ( prel T ) + ( 1 − fc,i ) Fl,i ( prel T )] where fb,i is the fraction of b-jets in the total sample, fc,i is the fraction of c-jets in the non-b-jet sample, and the index i denotes the pT bin. The fit model has two free parameters, fb,i and fc,i for each pjet+μ T,reco bin. In Pb+Pb collisions the model has an additional term which accounts for combinatoric muon–jet pairs. These are muon–jet pairs which pass the �R matching requirements, but the muon and jet do not come from the same hard scatter- ing. These are the most common in central Pb+Pb collisions, where the multiplicity of jets and muons is the highest. In pp collisions, this effect is negligibly small. For the combina- toric pairs, both the shape, Fmix,i , and the amplitude, fmix,i , are determined by event mixing in the Pb+Pb data. This pro- cedure uses a muon from one event and a jet from another event; all the analysis selections are applied, including the requirement on the �R separation between the muon and jet. Events which are mixed together are required to have ∑ EFCal T values that agree within 0.05 TeV and vertex z- positions that agree within 10 mm. The nominal data sample and mixed-event sample are found to agree well in the large �R region where there is no correlated signal. The Pb+Pb model, MPb+Pb, is structured as MPb+Pb i ( prel T ) = ( 1 − fmix,i ) × Mpp i ( prel T ) + fmix,i × Fmix,i ( prel T ) . The background fraction is largest in central collisions and is always less than 1.5%. The data prel T distributions are resampled 500 times and the mean and width of the range of results are used to set the central value and uncertainties in the fit. The statistical uncertainty of the templates is taken to be part of the statisti- cal uncertainty of the yield. Example fits for pp and Pb+Pb collisions are shown in Figs. 1 and 2, for low and high pjet+μ T,reco respectively. The raw b-jet pT spectrum is constructed in each Pb+Pb centrality bin and in pp collisions by taking each fb,i value and multiplying it by the number of jets in i-th bin in the total pjet+μ T,reco spectrum. 4.3.2 Comparing muon momentum distributions in the data and simulations The b-jet fragmentation function in Pb+Pb collisions has not been measured. Significant modification to the b-hadron lon- gitudinal momentum due to jet quenching could bias the b-jet template. In order to ensure that the simulations reproduce the data sufficiently well, a check of the longitudinal momen- tum distribution of the muon with respect to the jet+μ axis is performed. The fitting results as a function of centrality and pjet+μ T,reco from the previous subsection are used to weight the jet+μ pairs from the MC samples (used for the charm and bottom templates) and those used in constructing the data-driven light-jet template to have the same flavour frac- tions as the data. Muons which have a transverse distance to the primary vertex, d0, greater than 0.25 mm are selected; this requirement further enhances the heavy-flavour fractions relative to the light jets. For these jet+μ pairs, the distribution of z ≡ pμ T cos(θ) /pjet+μ T,reco where θ is the angle between the muon and the jet+μ axis, is constructed. This combined distribution from the pairs in the charm-, bottom-, and light-jet templates is compared with the data distribution for the same quantity. Since the light- jet template is data-driven, it should agree between the data and the template, and thus the comparison between data and simulations is sensitive to the degree to which the MC mod- elling of the charm and bottom templates agrees with the data. These distributions are shown in Fig. 3 for pp and 0– 20% centrality Pb+Pb collisions. The data and the simula- tions (including the data-driven light-jet template) agree in both pp and Pb+Pb collisions. Thus, within the current uncer- tainties, the longitudinal momentum fraction of the muon is unmodified in Pb+Pb collisions and the prel T distributions are largely sensitive to the b-hadron decay kinematics. 4.4 Corrections to the raw spectra The raw jet spectra are constructed from all the measured jets for the inclusive jets and from the muon–jet pairs, scaled by the fb,i values, for the b-jets after correction for the muon trigger efficiency. The raw spectra are unfolded to account for bin migrations due to the finite jet energy resolution, and any reconstruction inefficiency for the jets and muons (and muon and neutrino energies in the b-jet sample). Both the b-jet and inclusive jet pT spectra are unfolded using the one- dimensional (1D) Bayesian unfolding [65] from theRooUn- fold software package [66]. The b-jet response matrices are built from the muon-filtered MC samples using truth- level b-jets (including the muon and neutrino momenta) that are matched to reconstructed jets in simulations and have |yb-jet truth | < 2.1. The inclusive jet response matrices are built from the dijet MC samples using all truth jets and not includ- ing any muon or neutrino momenta. The response matrices are generated separately for pp collisions and for each centrality interval in Pb+Pb colli- sions. To better represent the data, the response matrices are 123 Eur. Phys. J. C (2023) 83 :438 Page 7 of 32 438 Fig. 1 The prel T distributions in pp collisions (top left) and 50–80% (top right), 20–50% (bottom left), and 0–20% (bottom right) centrality Pb+Pb collisions. The data are shown for the range 80.7 < pjet+μ T,reco < 95.0 GeV. The stacked histograms show the fit results. Middle and bottom panels of each plot show the pulls and data-to-fit ratios, respectively. The error bars in the ratio plots show the statistical uncertainties of the data and MC samples reweighted along the truth-pjet T axis by the reconstruction- level data to simulation ratio. The number of iterations in the unfolding was chosen such that the result is stable when changing the number of iterations while minimizing the amplification of statistical uncertainties. Four iterations are used in all cases presented here. The reconstructed pjet+μ T,reco values are required to be larger than 72 GeV in Pb+Pb col- lisions and 64 GeV in pp collisions for both R = 0.2 and R = 0.4 jets. The inclusive-jet minimum reconstructed pT is required to be larger than 72 GeV for both Pb+Pb and pp col- lisions. Reconstructed jets below these thresholds that match to truth-pT values in the measurement region are corrected via the unfolding procedure. Fully unfolded results are presented starting from 80 GeV for both categories of jets. As described in Sect. 3, the main MC sample used in the b-jet analysis required the truth-muon pT to be larger than 3 GeV. The missing part of the cross-section from b-jets with muons below this threshold is addressed through an accep- tance correction based on the Pythia8 dijet MC samples which is applied after unfolding. The size of the correction is approximately 22% (25%) at 80 GeV and 19% (23%) at 250 GeV for R = 0.2 (R = 0.4) jets. 123 438 Page 8 of 32 Eur. Phys. J. C (2023) 83 :438 Fig. 2 The prel T distributions in pp collisions (top left) and 50–80% (top right), 20–50% (bottom left), and 0–20% (bottom right) centrality Pb+Pb collisions. The data are shown for the range 113.4 < pjet+μ T,reco < 142.3 GeV. The stacked histograms show the fit results. Middle and bottom panels of each plot show the pulls and data-to-fit ratios, respectively. The error bars in the ratio plots show the statistical uncertainties of the data and MC samples 4.5 Observables The b-jet cross-section in pp collisions is defined as d2σb-jet dpT dy = 1 L intB NUnfol. b-jet �pT�y (3) where L int is the integrated luminosity andB is the branching ratio which includes direct (b → μ) and cascade (b → c → μ) semileptonic decays. The value ofB is (20.6±0.6)% [28]. The quantity NUnfol. b-jet is the b-jet yield after the unfolding and acceptance correction described above, and �pT and �y are the widths of the pT and y bins. Following the same approach, the per-event yield of b-jets measured in Pb+Pb collisions is calculated as 1 Nevt d2Nb-jet dpT dy ∣ ∣ ∣ ∣ ∣ cent = 1 NevtB NUnfol. b-jet �pT�y ∣ ∣ ∣ ∣ ∣ cent , (4) where Nevt is the number of MB events in the centrality class ‘cent’. The inclusive jet cross-section and per-event yields are defined in the same way as in Eq. (3) and Eq. (4) but without the B factor. 123 Eur. Phys. J. C (2023) 83 :438 Page 9 of 32 438 Fig. 3 The z distributions for data and simulations (including the data- driven light-jet template) for pp collisions (top row) and 0–20% cen- trality Pb+Pb collisions (bottom row) for two pjet+μ T,reco selections. In each figure the top panel shows the distributions themselves and the bottom panel shows the ratio of data to simulations. The error bars in the ratio plots show the statistical uncertainties on the data and MC samples 5 Systematic uncertainties The systematic uncertainties of the jet cross-sections and per- event yields common to both the inclusive jet and the b-jet measurements arise from the jet energy scale and resolution, the unfolding procedure, luminosity (pp only), and 〈TAA〉 determination (Pb+Pb only). There are additional contribu- tions to the b-jet measurements from the modelling of the prel T distributions, the fitting procedure, the branching ratio of b-hadrons to muons, and the muon performance. The uncer- tainties are discussed in detail below. The systematic uncertainty of the jet energy scale (JES) has five parts. First, there is a centrality-independent base- line component that is determined from in situ studies of the calorimeter response for jets reconstructed with the pro- cedure used in 13 TeV pp collisions [55,67], including an additional term for b-jet energy scale [55]. The second is a centrality-independent component which accounts for the relative energy scale difference between the jet reconstruc- tion procedure used in this paper and that in 13 TeV pp collisions [58]. Potential inaccuracies in the MC description of the relative abundances of jets initiated by quarks and gluons and of the calorimetric response to quark and gluon jets are accounted for by a third, JES flavour, component; this component is estimated independently for the inclusive- jet and b-jet energy scale by varying the quark and gluon fractions from their Pythia8 values to those extracted from Herwig++. The fourth, centrality-dependent, component (in Pb+Pb collisions only) accounts for a different structure [68], and possibly a different detector response, for jets in Pb+Pb collisions that is not modelled by the simulations. It is eval- uated by the method used for 2015 and 2011 data [58] that compares the calorimetric jet pT and the sum of the trans- verse momentum of charged particles within the jet in data and MC samples. The size of the centrality-dependent uncer- tainty in the JES reaches 1.2% in the most central collisions; it is smaller for more peripheral collisions. The systematic uncertainties from the JES discussed above are derived for R = 0.4 jets and applied to both R = 0.4 and R = 0.2 jets. An additional component applies only to R = 0.2 jets and accounts for a potential uncertainty difference between R = 0.4 and R = 0.2 jets. The uncertainty is assessed by comparing the ratio of R = 0.2 jet pT to R = 0.4 jet pT in data and simulations. 123 438 Page 10 of 32 Eur. Phys. J. C (2023) 83 :438 Fig. 4 The relative systematic uncertainties for several categories, as a function of b-jet pT for (top) the pp cross-section for R = 0.4 (left) and R = 0.2 (right) jets, and (bottom) the Pb+Pb per-event-yield in peripheral (left) and central (right) collisions for R = 0.2 jets The uncertainty due to the jet energy resolution (JER) is evaluated by repeating the unfolding procedure with mod- ified response matrices, where an additional contribution is added to the resolution of the reconstructed pT using a Gaus- sian smearing procedure. The smearing factor is evaluated using an in situ technique in 13 TeV pp data that involves studies of dijet pT balance [69,70]. Additionally, an uncer- tainty is included to account for differences between the tower-based jet reconstruction and the jet reconstruction used in analyses of 13 TeV pp data, as well as differences in cali- bration procedures. Similarly to the JES, an additional uncer- tainty in the JER accounting for differences between R = 0.2 and R = 0.4 jets is added. The resulting uncertainty from the JER is symmetrized. Uncertainties related to muons are associated with the trig- ger and reconstruction efficiency measurement. The system- atic uncertainties are estimated by varying the tag-and-probe method as described in Refs. [59,60]. Additionally, the sta- tistical uncertainty of the factor used to correct the central- ity dependence of the trigger efficiency in Pb+Pb collisions and the difference between the data-driven reconstruction efficiencies in Pb+Pb and pp collisions are taken as muon systematic uncertainties that apply only to Pb+Pb collisions. The uncertainties in modelling the prel T distributions come from several sources, which are common to pp and Pb+Pb collisions. For the fraction of b-hadrons which arise from gluon splitting, the analysis uses that from Pythia8 simula- tion as the central value. In pp collisions, the gluon-splitting fraction is reweighted to the value obtained from the Her- wig++ sample [71]. In Pb+Pb collisions, there is no infor- mation about the modification of the gluon-splitting contri- bution to b-jets. Additionally, the fragmentation functions of b-jets in Pb+Pb collisions have not been measured. In order to cover both of these effects, the gluon-splitting fraction is conservatively varied between zero and 100%. For the c- jets, the gluon-splitting uncertainty is estimated by varying the Pythia8 value of the gluon-splitting fraction by a factor of two, based on measurements in Ref. [72]. The fraction of muons which arise from b-hadron decays which include an intermediate D-meson is varied in accord with Ref. [28]. The fractions of the various b-hadron species are varied accord- ing to the world average values in Ref. [61]. The fraction of c-baryons was varied according to ALICE measurement [27] with negligible impact on the results. The modelling of the muon momentum in the b-hadron rest frame is crucial for the prel T method. The modelling in Pythia8 is used for the central values. The Pythia8 distributions were compared 123 Eur. Phys. J. C (2023) 83 :438 Page 11 of 32 438 Fig. 5 Relative size of systematic uncertainties for several categories, as a function of jet pT for b-jet RAA (left column) and inclusive jet RAA (right column), in peripheral (top row), semi-central (middle row) and central (bottom row) Pb+Pb collisions with the measurement from DELPHI [73] and the difference between Pythia8 and the DELPHI measurement is used as a systematic uncertainty. The uncertainty in the light-jet tem- plate is evaluated by using muons with a distance of closest approach to the collision vertex smaller than 0.01 mm, to min- imize the contribution from heavy-flavour jets, and remaking the templates. In R = 0.2 jets in pp collisions, the dominant components of modelling uncertainties stem from the light- jet template and the fraction of b-hadrons; for R = 0.4 jets, the light-jet template uncertainty dominates. For Pb+Pb col- lisions, the dominant modelling uncertainties stem from the light-jet template and gluon-splitting components. The uncertainty in the unfolding procedure is determined in all cases by constructing response matrices from the MC distributions without the reweighting factors that are used to match the MC distributions to those in data. The uncer- tainty due to using a particular MC model in unfolding b-jets is addressed by reweighting the 2D (pjet T ,pμ T ) distribution in Pythia8 to that observed in Herwig++. The inclusive jet analysis has an uncertainty associated with the non-closure of the unfolding procedure when the MC sample is divided 123 438 Page 12 of 32 Eur. Phys. J. C (2023) 83 :438 and one portion is used in place of the data and the other por- tion used to generate a response matrix; in the b-jet analysis the closure was found to be consistent with unity within the statistical uncertainties and no uncertainty is added. Uncertainties from the finite size of the MC samples are combined with the data statistical uncertainties. A system- atic uncertainty associated with the template fit procedure is addressed by allowing the templates to deform in Pb+Pb col- lisions by convolving the nominal template with a Gaussian function where the width parameter is free. Deformations of the templates account for mismodelling due to the large UE, such as via the jet position resolution, muon momen- tum resolution, or other effects. A similar uncertainty was applied in Ref. [16]. The small non-closure of the fitting pro- cedure, when the fb,i extraction is tested in simulations, is also included in the systematic uncertainties. For each uncertainty component discussed above, the entire analysis procedure is repeated with the variation under consideration and the resulting changes are added in quadra- ture to form the total systematic uncertainty of the mea- surement. A summary of the systematic uncertainties for the R = 0.4 and R = 0.2 b-jet cross-sections is shown in Fig. 4. For the R = 0.4 jets, the largest contribution is the modelling component, while for R = 0.2 jets, the jet and modelling components have similar magnitudes. The integrated luminosity determined for 2017 pp data was calibrated using data from dedicated beam-separation scans, also known as van der Meer scans [74]. Sources of sys- tematic uncertainty similar to those examined in the 2012 pp luminosity calibration [74] were studied in order to assess the systematic uncertainties for the 2017 data. The combination of these systematic uncertainties results in a relative uncer- tainty of 1.6%. The uncertainty of the mean nuclear thick- ness function arises from geometric modelling uncertain- ties (nucleon–nucleon inelastic cross-section, Woods–Saxon parameterization of the nucleon distribution) and the uncer- tainty in the fraction of selected inelastic Pb+Pb collisions. The values of these uncertainties are taken from Ref. [2]. The branching ratio for b-hadrons into muons is (20.6 ± 0.6)% and is taken from Ref. [28]. The uncertainties which are common to pp and Pb+Pb collisions are treated as correlated when determining the uncertainty in the RAA value, with the exception of gluon splitting, where the uncertainties account for possible pro- duction and fragmentation mechanism differences between pp and Pb+Pb collisions, and the unfolding. Similarly, the uncertainties which are common to inclusive jets and b-jets cancel out when ratios of cross-sections or RAA values are taken; the remaining uncertainties are the JES flavour com- ponent, the b-jet-specific JES uncertainty, and the unfolding. A summary of the systematic uncertainties for inclusive jet and b-jet RAA is shown in Fig. 5. The uncertainties in the Fig. 6 Relative size of systematic uncertainties of the ratio R b-jet AA /Rinclusive jet AA shown for the most relevant components, as a func- tion of jet pT for 50–80% (top), 20–50% (middle), and 0–20% (bottom) centrality Pb+Pb collisions R b-jet AA /Rinclusive jet AA ratio are shown in Fig. 6; the uncertain- ties in the gluon-splitting contribution and deformation of the templates in Pb+Pb collisions dominate in most cases. 123 Eur. Phys. J. C (2023) 83 :438 Page 13 of 32 438 Fig. 7 (Left) Differential cross-section for b-jet production for R = 0.2 and R = 0.4 jets with |y| < 2.1 as a function of pT in 5.02 TeV pp data. (right) Ratio of the predictions to the measured b-jet cross-section in pp collisions at 5.02 TeV for R = 0.4 (top) and R = 0.2 (bottom) jets. The R = 0.4 jets are compared with calculations from Ref. [75] and R = 0.2 jets are compared with calculations from Refs. [11,12,75,76]. Both cross-sections are compared with Pythia8 and Herwig++ cal- culations. The bands around unity represent the total uncertainty of the data. The text provides additional discussion Fig. 8 (Left) Cross-section of R = 0.2 b-jet and inclusive jet pro- duction in pp collisions at 5.02 TeV, and (right) the b-jet to inclusive jet cross-section ratio, together with Pythia8 simulation and measure- ments from the CMS Collaboration at 7 TeV for R = 0.5 jets [78]. For the current measurement, the boxes represent the systematic uncer- tainties and the error bars represent the statistical uncertainties. For the CMS data the bars represent the total uncertainty 6 Results 6.1 Cross-section in pp collisions Figure 7 shows the b-jet cross-section as a function of b-jet pT in pp collisions at 5.02 TeV for R = 0.2 and R = 0.4 jets with |y| < 2.1. Additionally, b-jet cross-sections for both R = 0.2 and R = 0.4 jets are compared with theory and generator calculations. The data is in good agreement with the calculations by Li and Vitev [75] for both R = 0.4 and R = 0.2 jets. This calculation is based on semi-inclusive jet functions where the cross-section is expressed in terms of the parton distribution functions, the hard kernel and the jet functions. The jet functions have terms in ln(R), which are resummed. The SHERPA 2.2.4 calculations [12] for R = 0.2 jets underestimate the measuredb-jet cross-sections by an amount which increases with pT . A calculation [11,76] which is based on Pythia8 where the PDFs and αs(MZ ) is set with pSet = 8 [44] is approximately consistent with the upper edge of the uncertainty band of the R = 0.2 jet yields. The Pythia8 calculation using the NNPDF23lo PDF [46] and the A14 tune [45] is 20–30% higher than data for both R = 123 438 Page 14 of 32 Eur. Phys. J. C (2023) 83 :438 Fig. 9 Per-event yields scaled by 〈TAA〉 in Pb+Pb collisions for three centrality classes for R = 0.2 b-jets (left) and inclusive jets (right). The boxes show the systematic uncertainties and the bars represent the sta- tistical uncertainties. The different centrality classes are offset by the factors shown on the plot for clarity. The values of the pp cross-sections are shown by the black lines; they are offset by the factors shown on the plot. The size of the TAA uncertainties are noted in the legend 0.4 and R = 0.2 jets. The Herwig++ calculations [77] using the NNPDF30nlo PDF underestimate the measured cross- sections at both jet radii. There is no uncertainty estimate for the generator calculations, and the sensitivity of the results to the choice of parameter values has not been investigated. Figure 8 shows a comparison of the b-jet and inclusive jet cross-sections for R = 0.2 jets and their ratio. The figure also shows the same ratio from the Pythia8 MC sample; it agrees with the data. The cross-section ratio measured at 7 TeV for R = 0.5 jets with |y| < 0.5 by the CMS Collaboration [78] is also shown; the jet radii in this measurement are larger than in the present analysis but the results are qualitatively similar. No significant pT dependence of the b-jet to inclusive jet cross-section ratio is observed. 6.2 Per-event yields and RAA in Pb+Pb collisions The inclusive jet and b-jet per-event yields in Pb+Pb colli- sions scaled by 〈TAA〉 are shown in Fig. 9 for the three cen- trality classes used in this analysis overlaid with the values of the pp cross-sections. Figure 10 shows a direct comparison of the inclusive jet and b-jet RAA for each centrality class. The RAA values for both types of jets decrease going from peripheral to central collisions. Both are consistent with unity in peripheral collisions. In central and semi-central collisions the RAA central values for inclusive jets are lower than those for b-jets. A difference between the slopes of the inclusive jet and b-jet differential cross-sections could cause the RAA values to differ between the two categories of jets; however, the ratio of the b-jet to inclusive jet cross-section, shown in Fig. 8, does not vary with pT . The RAA values for both inclusive jets and b-jets are com- pared with two theory calculations. The first calculation is the LIDO model [11,76,79], which includes both energy loss and diffusion of heavy quarks. The dead-cone effect for b-jets is included and the medium is implemented via (2+1)D viscous hydrodynamics with averaged initial condi- tions. The parameter which controls the coupling between the jet and the medium, μmin, is varied between 1.3πT and 1.8πT , where T is the temperature of the QGP in the model, in the calculation shown here. The choice of μmin values is motivated by comparisons with other jet measurements. These parameters have been shown [79] to provide a rea- sonable description of measurements of the RAA values of B- and D-mesons [22,23]. The calculation by Dai et al. in Ref. [12] is based on a Langevin transport model describing the evolution of b-quarks and their collisional energy loss and a higher-twist description of radiative energy loss for both heavy and light partons. This model also includes the dead-cone effect for b-jets and also uses a (2+1)D viscous hydrodynamic medium with averaged initial conditions. The parameter controlling the coupling of the jet to the medium, q0, is set to be 1.2 GeV 2/fm. The LIDO model shows good agreement with the data for both inclusive jet and b-jet RAA, although with the inclusive jets on the low side and the b-jet case on the high side, for all three centralities considering model and data uncertainties. The calculation in Ref. [12] is below the measured RAA in central and semi-central colli- sions for both b-jets and inclusive jets. In order to assess any difference between the b-jet and inclusive jet RAA, the ratio R b-jet AA /Rinclusive jet AA is presented in 123 Eur. Phys. J. C (2023) 83 :438 Page 15 of 32 438 Fig. 10 b-jet (filled points) RAA for three centrality classes compared with the inclusive jet RAA (open points) at 5.02 TeV. Both RAA mea- surements are compared with theory calculations [11,12,76]. The boxes represent the systematic uncertainties and the error bars represent the statistical uncertainties. For the LIDO calculation, the width of the band shows the variation of the μmin parameter from 1.3πT (lower edge) to 1.8πT (upper edge). The boxes at unity represent the scale uncertainties from 〈TAA〉 and the luminosity determination Fig. 11 for each centrality class. This provides a more precise comparison of the RAA values than in Fig. 10. The results suggest that in central collisions the suppression of b-jets is less than that of inclusive jets, with an overall significance Fig. 11 Ratio of b-jet RAA to the inclusive jet RAA for each centrality class. Ratios are compared with theory calculations [11,12,76]. The boxes represent the systematic uncertainties and the error bars represent the statistical uncertainties. For the LIDO calculation, the width of the band shows the variation of the μmin parameter from 1.3πT (upper edge) to 1.8πT (lower edge) of approximately 1.7σ . The calculation by Dai et al. [12] agrees well with the ratio in all centrality classes, while LIDO model calculations tend to overestimate the double ratio at low pT , especially in central collisions, but agree well with the data at higher pT . In the LIDO model, the difference between the inclusive jet and b-jet RAA values is not expected 123 438 Page 16 of 32 Eur. Phys. J. C (2023) 83 :438 to be entirely a result of the large mass of the b-quark, and qualitatively similar differences are seen between the two RAA values in that model with and without the inclusion of the dead-cone effect [79]. Differences in the internal structure of the b-jets, such as those that come from gluon-splitting processes, are also expected to be important in determining the b-jet RAA value. Based on the Pythia8 MC samples, the fractions of jets which are initiated by gluons in the inclusive jet and b-jet samples have opposite trends with pT . The fraction increases from 23% to 45% over the range of 80 to 250 GeV for the b-jets and decreases from 61% to 47% over the same range for the inclusive jets. These results are compatible with previous results from CMS [29] (although both the collision energy and jet radius are different) but have better precision. Additional measure- ments of the fragmentation functions of b-jets in heavy-ion collisions would improve the uncertainties in future measure- ments using the method used in this article. 7 Summary This paper reports cross-sections for b-jets and inclusive jets in Pb+Pb and pp collisions, both at √ sNN = 5.02 TeV, and recorded by the ATLAS detector at the LHC. The measure- ment uses three datasets: 1.4 nb−1 and 1.7 nb−1 of Pb+Pb collisions collected in 2018 for R = 0.2 b-jets and inclusive jets respectively, and 260 pb−1 of pp collisions collected in 2017 for R = 0.2 and R = 0.4 b-jets and R = 0.2 inclu- sive jets. The b-jet cross-section in pp collisions is com- pared with a theoretical calculation and Monte Carlo gener- ator predictions. The b-jet and inclusive jet per-event yields for R = 0.2 jets and the corresponding nuclear modifica- tion factor, RAA, are also reported for Pb+Pb collisions at the same per-nucleon collision energy. The RAA values are found to decrease with increasing collision centrality for both b-jets and inclusive jets. In order to more directly compare the suppression of b-jets and inclusive jets, the ratio of the RAA values is presented. The central values of this ratio suggest that the RAA for b-jets is larger than that for inclusive jets in central Pb+Pb collisions. The observed differences may arise primarily from the different mixture of quark and gluon jets in the inclusive jets and b-jets, and the b-quark mass effect may be subdominant in the kinematic range measured here. However, the current systematic uncertainties do not permit a more definitive statement. This highlights the need for more precise measurements of this quantity, mainly the necessity to measure the fragmentation functions of b-jets in heavy-ion collisions. The measurements are compared with theoretical calculations and suggest a role for mass and colour-charge effects in partonic energy loss in heavy-ion collisions. Acknowledgements We thank CERN for the very successful oper- ation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowl- edge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Aus- tralia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and 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, Netherlands; RCN, Norway; MEiN, Poland; FCT, Por- tugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mech- anism 2014–2021, Norway; NCN and NAWA, Poland; La Caixa Bank- ing Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Lever- hulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Den- mark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Tai- wan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [80]. Data Availability Statement This manuscript has no associated data or the data will not be deposited. [Authors’ comment: All ATLAS sci- entific output is published in journals, and preliminary results are made available in Conference Notes. All are openly available, without restric- tion on use by external parties beyond copyright law and the standard conditions agreed by CERN. Data associated with journal publications are also made available: tables and data from plots (e.g. cross section values, likelihood profiles, selection efficiencies, cross section limits, ...) are stored in appropriate repositories such as HEPDATA (http://hepdata. cedar.ac.uk/). ATLAS also strives to make additional material related to the paper available that allows a reinterpretation of the data in the context of new theoretical models. For example, an extended encap- sulation of the analysis is often provided for measurements in the framework of RIVET (http://rivet.hepforge.org/). This information is taken from the ATLAS Data Access Policy, which is a public docu- ment that can be downloaded from http://opendata.cern.ch/record/413 [opendata.cern.ch].]. 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Zhou105, C. Zhou168 , H. Zhou7 , N. Zhou62c , Y. Zhou7, C. G. Zhu62b , C. Zhu14a,14d , H. L. Zhu62a , H. Zhu14a , J. Zhu105 , Y. Zhu62a , X. Zhuang14a , K. Zhukov37 , V. Zhulanov37 , N. I. Zimine38 , J. Zinsser63b , M. Ziolkowski140 , L. Živković15 , A. Zoccoli23a,23b , K. Zoch56 , T. G. Zorbas138 , O. Zormpa46 , W. Zou41 , L. Zwalinski36 1 Department of Physics, University of Adelaide, Adelaide, Australia 2 Department of Physics, University of Alberta, Edmonton, AB, Canada 3 (a)Department of Physics, Ankara University, Ankara, Türkiye; (b)Division of Physics, TOBB University of Economics and Technology, Ankara, Türkiye 4 LAPP, Univ. Savoie Mont Blanc, CNRS/IN2P3, Annecy, France 5 APC, Université Paris Cité, CNRS/IN2P3, Paris, France 6 High Energy Physics Division, Argonne National Laboratory, Argonne, IL, USA 7 Department of Physics, University of Arizona, Tucson, AZ, USA 8 Department of Physics, University of Texas at Arlington, Arlington, TX, USA 9 Physics Department, National and Kapodistrian University of Athens, Athens, Greece 10 Physics Department, National Technical University of Athens, Zografou, Greece 11 Department of Physics, University of Texas at Austin, Austin, TX, USA 123 http://orcid.org/0000-0002-3285-7004 http://orcid.org/0000-0003-1631-2714 http://orcid.org/0000-0002-9780-099X http://orcid.org/0000-0003-0855-0958 http://orcid.org/0000-0002-1351-6757 http://orcid.org/0000-0001-5284-2451 http://orcid.org/0000-0003-2432-3309 http://orcid.org/0000-0003-1827-2955 http://orcid.org/0000-0002-5956-4244 http://orcid.org/0000-0002-2598-2659 http://orcid.org/0000-0002-3368-3413 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438 Page 28 of 32 Eur. 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C (2023) 83 :438 12 Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 13 Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain 14 (a)Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China; (b)Physics Department, Tsinghua University, Beijing, China; (c)Department of Physics, Nanjing University, Nanjing, China; (d)University of Chinese Academy of Science (UCAS), Beijing, China 15 Institute of Physics, University of Belgrade, Belgrade, Serbia 16 Department for Physics and Technology, University of Bergen, Bergen, Norway 17 (a)Physics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; (b)University of California, Berkeley, CA, USA 18 Institut für Physik, Humboldt Universität zu Berlin, Berlin, Germany 19 Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 20 School of Physics and Astronomy, University of Birmingham, Birmingham, UK 21 (a)Department of Physics, Bogazici University, Istanbul, Türkiye; (b)Department of Physics Engineering, Gaziantep University, Gaziantep, Türkiye; (c)Department of Physics, Istanbul University, Istanbul, Türkiye; (d)Istinye University, Sariyer, Istanbul, Türkiye 22 (a)Facultad de Ciencias y Centro de Investigaciónes, Universidad Antonio Nariño, Bogotá, Colombia; (b)Departamento de Física, Universidad Nacional de Colombia, Bogotá, Colombia 23 (a)Dipartimento di Fisica e Astronomia A. Righi, Università di Bologna, Bologna, Italy; (b)INFN Sezione di Bologna, Bologna, Italy 24 Physikalisches Institut, Universität Bonn, Bonn, Germany 25 Department of Physics, Boston University, Boston, MA, USA 26 Department of Physics, Brandeis University, Waltham, MA, USA 27 (a)Transilvania University of Brasov, Brasov, Romania; (b)Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania; (c)Department of Physics, Alexandru Ioan Cuza University of Iasi, Iasi, Romania; (d)Physics Department, National Institute for Research and Development of Isotopic and Molecular Technologies, Cluj-Napoca, Romania; (e)University Politehnica Bucharest, Bucharest, Romania; (f)West University in Timisoara, Timisoara, Romania 28 (a)Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovak Republic; (b)Department of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic 29 Physics Department, Brookhaven National Laboratory, Upton, NY, USA 30 Departamento de Física, y CONICET, Facultad de Ciencias Exactas y Naturales, Instituto de Física de Buenos Aires (IFIBA), Universidad de Buenos Aires, Buenos Aires, Argentina 31 California State University, CA, USA 32 Cavendish Laboratory, University of Cambridge, Cambridge, UK 33 (a)Department of Physics, University of Cape Town, Cape Town, South Africa; (b)iThemba Labs, Western Cape, South Africa; (c)Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa; (d)National Institute of Physics, University of the Philippines, Diliman, Philippines; (e)Department of Physics, University of South Africa, Pretoria, South Africa; (f)University of Zululand, KwaDlangezwa, South Africa; (g)School of Physics, University of the Witwatersrand, Johannesburg, South Africa 34 Department of Physics, Carleton University, Ottawa, ON, Canada 35 (a)Faculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies, Université Hassan II, Casablanca, Morocco; (b)Faculté des Sciences, Université Ibn-Tofail, Kénitra, Morocco; (c)Faculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech, Morocco; (d)LPMR, Faculté des Sciences, Université Mohamed Premier, Oujda, Morocco; (e)Faculté des sciences, Université Mohammed V, Rabat, Morocco; (f)Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir, Morocco 36 CERN, Geneva, Switzerland 37 Affiliated with an institute covered by a cooperation agreement with CERN, Geneva, Switzerland 38 Affiliated with an international laboratory covered by a cooperation agreement with CERN, Geneva, Switzerland 39 Enrico Fermi Institute, University of Chicago, Chicago, IL, USA 40 LPC, Université Clermont Auvergne, CNRS/IN2P3, Clermont-Ferrand, France 41 Nevis Laboratory, Columbia University, Irvington, NY, USA 42 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 123 Eur. 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C (2023) 83 :438 Page 29 of 32 438 43 (a)Dipartimento di Fisica, Università della Calabria, Rende, Italy; (b)INFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati, Frascati, Italy 44 Physics Department, Southern Methodist University, Dallas, TX, USA 45 Physics Department, University of Texas at Dallas, Richardson, TX, USA 46 National Centre for Scientific Research “Demokritos”, Agia Paraskevi, Greece 47 (a)Department of Physics, Stockholm University, Stockholm, Sweden; (b)Oskar Klein Centre, Stockholm, Sweden 48 Deutsches Elektronen-Synchrotron DESY, Hamburg and Zeuthen, Germany 49 Fakultät Physik , Technische Universität Dortmund, Dortmund, Germany 50 Institut für Kern- und Teilchenphysik, Technische Universität Dresden, Dresden, Germany 51 Department of Physics, Duke University, Durham, NC, USA 52 SUPA-School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK 53 INFN e Laboratori Nazionali di Frascati, Frascati, Italy 54 Physikalisches Institut, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany 55 II. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, Germany 56 Département de Physique Nucléaire et Corpusculaire, Université de Genève, Geneva, Switzerland 57 (a)Dipartimento di Fisica, Università di Genova, Genoa, Italy; (b)INFN Sezione di Genova, Genoa, Italy 58 II. Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany 59 SUPA-School of Physics and Astronomy, University of Glasgow, Glasgow, UK 60 LPSC, Université Grenoble Alpes, CNRS/IN2P3, Grenoble INP, Grenoble, France 61 Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge, MA, USA 62 (a)Department of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, China; (b)Institute of Frontier and Interdisciplinary Science and Key Laboratory of Particle Physics and Particle Irradiation (MOE), Shandong University, Qingdao, China; (c)School of Physics and Astronomy, Shanghai Jiao Tong University, Key Laboratory for Particle Astrophysics and Cosmology (MOE), SKLPPC, Shanghai, China; (d)Tsung-Dao Lee Institute, Shanghai, China 63 (a)Kirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany; (b)Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 64 (a)Department of Physics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong; (b)Department of Physics, University of Hong Kong, Pok Fu Lam, Hong Kong; (c)Department of Physics and Institute for Advanced Study, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China 65 Department of Physics, National Tsing Hua University, Hsinchu, Taiwan 66 IJCLab, Université Paris-Saclay, CNRS/IN2P3, 91405 Orsay, France 67 Department of Physics, Indiana University, Bloomington, IN, USA 68 (a)INFN Gruppo Collegato di Udine, Sezione di Trieste, Udine, Italy; (b)ICTP, Trieste, Italy; (c)Dipartimento Politecnico di Ingegneria e Architettura, Università di Udine, Udine, Italy 69 (a)INFN Sezione di Lecce, Lecce, Italy; (b)Dipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy 70 (a)INFN Sezione di Milano, Milan, Italy; (b)Dipartimento di Fisica, Università di Milano, Milan, Italy 71 (a)INFN Sezione di Napoli, Naples, Italy; (b)Dipartimento di Fisica, Università di Napoli, Naples, Italy 72 (a)INFN Sezione di Pavia, Pavia, Italy; (b)Dipartimento di Fisica, Università di Pavia, Pavia, Italy 73 (a)INFN Sezione di Pisa, Pisa, Italy; (b)Dipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy 74 (a)INFN Sezione di Roma, Rome, Italy; (b)Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy 75 (a)INFN Sezione di Roma Tor Vergata, Rome, Italy; (b)Dipartimento di Fisica, Università di Roma Tor Vergata, Rome, Italy 76 (a)INFN Sezione di Roma Tre, Rome, Italy; (b)Dipartimento di Matematica e Fisica, Università Roma Tre, Rome, Italy 77 (a)INFN-TIFPA, Povo, Italy; (b)Università degli Studi di Trento, Trento, Italy 78 Department of Astro and Particle Physics, Universität Innsbruck, Innsbruck, Austria 79 University of Iowa, Iowa City, IA, USA 80 Department of Physics and Astronomy, Iowa State University, Ames, IA, USA 81 (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 de Física, Universidade de São Paulo, São Paulo, Brazil; (d)Rio de Janeiro State University, Rio de Janeiro, Brazil 82 KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 83 Graduate School of Science, Kobe University, Kobe, Japan 123 438 Page 30 of 32 Eur. Phys. J. C (2023) 83 :438 84 (a)Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Kraków, Poland; (b)Marian Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland 85 Institute of Nuclear Physics Polish Academy of Sciences, Kraków, Poland 86 Faculty of Science, Kyoto University, Kyoto, Japan 87 Kyoto University of Education, Kyoto, Japan 88 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka, Japan 89 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 90 Physics Department, Lancaster University, Lancaster, UK 91 Oliver Lodge Laboratory, University of Liverpool, Liverpool, UK 92 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana, Slovenia 93 School of Physics and Astronomy, Queen Mary University of London, London, UK 94 Department of Physics, Royal Holloway University of London, Egham, UK 95 Department of Physics and Astronomy, University College London, London, UK 96 Louisiana Tech University, Ruston, LA, USA 97 Fysiska institutionen, Lunds universitet, Lund, Sweden 98 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid, Spain 99 Institut für Physik, Universität Mainz, Mainz, Germany 100 School of Physics and Astronomy, University of Manchester, Manchester, UK 101 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France 102 Department of Physics, University of Massachusetts, Amherst, MA, USA 103 Department of Physics, McGill University, Montreal, QC, Canada 104 School of Physics, University of Melbourne, Victoria, Australia 105 Department of Physics, University of Michigan, Ann Arbor, MI, USA 106 Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA 107 Group of Particle Physics, University of Montreal, Montreal, QC, Canada 108 Fakultät für Physik, Ludwig-Maximilians-Universität München, Munich, Germany 109 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), Munich, Germany 110 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan 111 Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA 112 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen, The Netherlands 113 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, The Netherlands 114 Department of Physics, Northern Illinois University, DeKalb, IL, USA 115 (a)New York University Abu Dhabi, Abu Dhabi, United Arab Emirates; (b)United Arab Emirates University, Al Ain, United Arab Emirates; (c)University of Sharjah, Sharjah, United Arab Emirates 116 Department of Physics, New York University, New York, NY, USA 117 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo, Japan 118 Ohio State University, Columbus, OH, USA 119 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman, OK, USA 120 Department of Physics, Oklahoma State University, Stillwater, OK, USA 121 Joint Laboratory of Optics, Palacký University, Olomouc, Czech Republic 122 Institute for Fundamental Science, University of Oregon, Eugene, OR, USA 123 Graduate School of Science, Osaka University, Osaka, Japan 124 Department of Physics, University of Oslo, Oslo, Norway 125 Department of Physics, Oxford University, Oxford, UK 126 LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris, France 127 Department of Physics, University of Pennsylvania, Philadelphia, PA, USA 128 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA 129 (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)Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal 123 Eur. Phys. J. C (2023) 83 :438 Page 31 of 32 438 130 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 131 Czech Technical University in Prague, Prague, Czech Republic 132 Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic 133 Particle Physics Department, Rutherford Appleton Laboratory, Didcot, UK 134 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France 135 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, USA 136 (a)Departamento de Física, Pontificia Universidad Católica de Chile, Santiago, Chile; (b)Millennium Institute for Subatomic physics at high energy frontier (SAPHIR), Santiago, Chile; (c)Instituto de Investigación Multidisciplinario en Ciencia y Tecnología y Departamento de Física, Universidad de La Serena, La Serena, Chile; (d)Department of Physics, Universidad Andres Bello, Santiago, Chile; (e)Instituto de Alta Investigación, Universidad de Tarapacá, Arica, Chile; (f)Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso, Chile 137 Department of Physics, University of Washington, Seattle, WA, USA 138 Department of Physics and Astronomy, University of Sheffield, Sheffield, UK 139 Department of Physics, Shinshu University, Nagano, Japan 140 Department Physik, Universität Siegen, Siegen, Germany 141 Department of Physics, Simon Fraser University, Burnaby, BC, Canada 142 SLAC National Accelerator Laboratory, Stanford, CA, USA 143 Department of Physics, Royal Institute of Technology, Stockholm, Sweden 144 Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA 145 Department of Physics and Astronomy, University of Sussex, Brighton, UK 146 School of Physics, University of Sydney, Sydney, Australia 147 Institute of Physics, Academia Sinica, Taipei, Taiwan 148 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia; (b)High Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia; (c)University of Georgia, Tbilisi, Georgia 149 Department of Physics, Technion, Israel Institute of Technology, Haifa, Israel 150 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 151 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 152 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo, Japan 153 Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 154 Department of Physics, University of Toronto, Toronto, ON, Canada 155 (a)TRIUMF, Vancouver, BC, Canada; (b)Department of Physics and Astronomy, York University, Toronto, ON, Canada 156 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan 157 Department of Physics and Astronomy, Tufts University, Medford, MA, USA 158 Department of Physics and Astronomy, University of California Irvine, Irvine, CA, USA 159 Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 160 Department of Physics, University of Illinois, Urbana, IL, USA 161 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia-CSIC, Valencia, Spain 162 Department of Physics, University of British Columbia, Vancouver, BC, Canada 163 Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada 164 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany 165 Department of Physics, University of Warwick, Coventry, UK 166 Waseda University, Tokyo, Japan 167 Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot, Israel 168 Department of Physics, University of Wisconsin, Madison, WI, USA 169 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 170 Department of Physics, Yale University, New Haven, CT, USA a Also Affiliated with an Institute covered by a cooperation agreement with CERN., Geneva, Switzerland b Also at Borough of Manhattan Community College, City University of New York, New York, NY, USA c Also at Bruno Kessler Foundation, Trento, Italy d Also at Center for High Energy Physics, Peking University, Beijing, China 123 438 Page 32 of 32 Eur. Phys. J. C (2023) 83 :438 e Also at Centro Studi e Ricerche Enrico Fermi, Rome, Italy f Also at CERN, Geneva, Switzerland g Also at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Geneva, Switzerland h Also at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain i Also at Department of Financial and Management Engineering, University of the Aegean, Chios, Greece j Also at Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA k Also at Department of Physics and Astronomy, University of Louisville, Louisville, KY, USA l Also at Department of Physics, Ben Gurion University of the Negev, Beer Sheva, Israel m Also at Department of Physics, California State University, East Bay, USA n Also at Department of Physics, California State University, Sacramento, USA o Also at Department of Physics, King’s College London, London, UK p Also at Department of Physics, University of Fribourg, Fribourg, Switzerland q Also at Department of Physics, University of Thessaly, Thessaly, Greece r Also at Department of Physics, Westmont College, Santa Barbara, USA s Also at Hellenic Open University, Patras, Greece t Also at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona, Spain u Also at Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany v Also at Institute of Particle Physics (IPP), Toronto, Canada w Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan x Also at Institute of Theoretical Physics, Ilia State University, Tbilisi, Georgia y Also at Lawrence Livermore National Laboratory, Livermore, USA z Also at Physics Department, An-Najah National University, Nablus, Palestine aa Also at The City College of New York, New York, NY, USA ab Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China ac Also at TRIUMF, Vancouver, BC, Canada ad Also at Università di Napoli Parthenope, Naples, Italy ae Also at University of Chinese Academy of Sciences (UCAS), Beijing, China af Also at Department of Physics, University of Colorado Boulder, Colorado, USA ag Also at Yeditepe University, Physics Department, Istanbul, Türkiye ∗ Deceased 123 Measurement of the nuclear modification factor of b-jets in 5.02 TeV Pb+Pb collisions with the ATLAS detector Abstract 1 Introduction 2 ATLAS detector 3 Data selection and Monte Carlo samples 4 Analysis 4.1 Jet reconstruction 4.2 Muon reconstruction 4.3 b-jet yield reconstruction 4.3.1 Templates and fitting 4.3.2 Comparing muon momentum distributions in the data and simulations 4.4 Corrections to the raw spectra 4.5 Observables 5 Systematic uncertainties 6 Results 6.1 Cross-section in pp collisions 6.2 Per-event yields and RAA in Pb+Pb collisions 7 Summary Acknowledgements References