Measurement of the radius dependence of charged-particle jet suppression in Pb–Pb collisions at √sNN = 5.02 TeV

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Measurement of the radius dependence of charged-particle jet suppression in Pb–Pb collisions at √sNN = 5.02 TeV © 2023 The Author(s). Published by Elsevier B.V. Funded by SCOAP³. Published version ALICE Collaboration ALICE Collaboration. (2024). Measurement of the radius dependence of charged-particle jet suppression in Pb–Pb collisions at √sNN = 5.02 TeV. Physics Letters B, 849, Article 138412. https://doi.org/10.1016/j.physletb.2023.138412 2024 Phys. Lett. B 849 (2024) 138412 Available online 21 December 2023 0370-2693/© 2023 The Author(s). Published by Elsevier B.V. Funded by SCOAP³. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Contents lists available at ScienceDirect Physics Letters B journal homepage: www.elsevier.com/locate/physletb Letter Measurement of the radius dependence of charged-particle jet suppression in Pb–Pb collisions at √𝑠NN =5.02 TeV .ALICE Collaboration⋆ A R T I C L E I N F O A B S T R A C T Editor: M. Doser Dataset link: https:// www .hepdata .net /record /ins2637686 The ALICE Collaboration reports a differential measurement of inclusive jet suppression using pp and Pb–Pb collision data at a center-of-mass energy per nucleon–nucleon collision √𝑠NN =5.02 TeV. Charged-particle jets are reconstructed using the anti-𝑘Talgorithm with resolution parameters 𝑅 =0.2, 0.3, 0.4, 0.5, and 0.6 in pp collisions and 𝑅 =0.2, 0.4, 0.6 in central (0–10%), semi-central (30–50%), and peripheral (60–80%) Pb–Pb collisions. Anovel approach based on machine learning is employed to mitigate the influence of jet background. This enables measurements of inclusive jet suppression in new regions of phase space, including down to the lowest jet 𝑝T≥40 GeV/𝑐at 𝑅 =0.6in central Pb–Pb collisions. This is an important step for discriminating different models of jet quenching in the quark–gluon plasma. The transverse momentum spectra, nuclear modification factors, derived cross section, and nuclear modification factor ratios for different jet resolution parameters of charged-particle jets are presented and compared to model predictions. Amild dependence of the nuclear modification factor ratios on collision centrality and resolution parameter is observed. The results are compared to a variety of jet-quenching models with varying levels of agreement. 1. Introduction Lattice quantum chromodynamics (QCD) calculations predict that strongly-interacting matter at very high temperature exists in a phase called the quark–gluon plasma (QGP), where the partonic constituents, quarks and gluons, are not confined to hadrons. There is compelling evidence from observations reported by experiments at the Relativistic Heavy Ion Collider (RHIC) [1–4]and at the Large Hadron Collider (LHC) [5–17]that the QGP is created in high-energy nuclear collisions. High momentum transfer (hard) QCD scatterings of partons occur early in the heavy-ion collision evolution, producing high transverse momentum (𝑝T) partons that propagate through the medium and eventually fragment into collimated sprays of hadrons known as jets. Since jet production in proton–proton (pp) collisions is well described by perturbative QCD [18–21], measuring modifications to jet production and jet properties in heavy-ion collisions offers a powerful way to characterize the properties of the QGP. The high-𝑝Tpartons within the jet experience in-medium interactions through elastic scatterings and induced gluon radiation, a phenomenon called jet quenching (see Ref. [22]for a recent review). Jet quenching leads to several observable consequences: parton energy loss, modification of the jet substructure, and medium-induced acoplanarity. Jet quenching has been measured via inclusive yield and correlation measurements of high-𝑝Thadrons ⋆E-mail address: alice -publications @cern .ch. and reconstructed jets, semi-inclusive jet measurements, jet shapes, and recently via jet substructure measurements at RHIC [23–36]and at the LHC [7,15,16,37–56]. Jet energy loss results in a suppression of the jet yield at a fixed value of the jet 𝑝T. Jet suppression is quantified using the nuclear modification factor, 𝑅AA =1 ⟨𝑇AA⟩ d2𝑁∕d𝑝Td𝜂 d2𝜎pp∕d𝑝Td𝜂,(1) which is the ratio of the measured per-event inclusive jet yield in heavyion (AA) collisions and the inclusive cross sections in pp collisions scaled by the nuclear overlap in a given centrality class 𝑇AA [57]. The value of 𝑅AA is expected to be one in the absence of nuclear effects. It is important to measure jet suppression over a wide range of parameters, including the jet 𝑝Tand the resolution parameter (so-called radius), 𝑅, of the clustering algorithm since the influence of in-medium effects and the medium response are expected to vary with these parameters [58–61]. Measuring jets at large 𝑅is especially interesting because more of the larger-angle medium-induced modification will be recovered relative to jets with smaller 𝑅. Additionally, the contribution of the medium response relative to other effects is expected to vary with 𝑅[58]. These competing effects may help to discriminate the mechanisms underlying energy loss and elucidate the energy transhttps://doi.org/10.1016/j.physletb.2023.138412 Received 12 April 2023; Received in revised form 13 December 2023; Accepted 18 December 2023 Physics Letters B 849 (2024) 138412 2 ALICE Collaboration port properties of the QGP. While jet suppression has been measured over a large range in jet 𝑝Tat both the LHC and RHIC [62–66], of particular interest are the low-𝑝Tand large-𝑅regions. A recent measurement by the CMS collaboration [67]studied jet suppression up to 𝑅 =1.0for jets with high 𝑝T>200 GeV/𝑐. Measurements of jet suppression as a function of 𝑅were found to have excellent discriminating power when compared to various jet quenching models [67]. However, no significant radial dependence was observed, which implies that there may be a combination of competing jet-quenching effects. The ATLAS collaboration [68]also studied the 𝑅-dependence of the ratios of jet spectra measured in central and peripheral collisions (𝑅CP) at lower 𝑝T, 40 <𝑝 T<200 GeV/𝑐, and found a dependence on 𝑅where jets with larger 𝑅up to 𝑅 =0.5exhibit less suppression. Measuring the 𝑅- dependence of energy loss [58–61]at low 𝑝Twill probe the expectation that the 𝑅-dependence is larger in this region [58], and will connect to inclusive jet measurements at RHIC [62]. The ALICE experiment at the LHC is well-suited to perform jet measurements at low jet 𝑝Tat the LHC due to high-precision tracking in the Time Projection Chamber (TPC) [69]and Inner Tracking System (ITS) [70]. However, reconstructing the jet 𝑝Tin nucleus–nucleus collisions is challenging due to the large background fluctuations from the underlying event (UE), which can be a significant fraction of the jet 𝑝Titself. Jet measurements in heavy-ion collisions require a procedure to account for this background which involves both a correction of the jet 𝑝Tand a suppression of combinatorial (or fake) jets. One common procedure applied for the jet 𝑝T-smearing is to correct for the average background via a pedestal subtraction of the eventwise momentum density (herein referred to as the area-based or AB method [71,72]). This is accompanied by an additional correction for event-averaged residual smearing effects via an unfolding procedure. Contributions from combinatorial jets to the inclusive jet yield can be further suppressed via additional requirements on the jet acceptance, such as a leading hadron 𝑝Trequirement. The drawback of such requirements is a bias of the jet population. A generalization of this procedure is to utilize machine learning (ML) techniques to include multi-dimensional information in calculating the corrected jet momentum, as explored in Ref. [73]. This approach uses specific properties of the jet and its constituents in addition to the area-based corrected jet 𝑝Tto reduce the residual fluctuations and remove combinatorial jets from the inclusive jet yield. An unfolding procedure must be applied to correct for the contributions of residual smearing effects, which can be performed down to lower jet 𝑝T due to the improved jet 𝑝Tresolution. Unique to this procedure is that the correction for the jet energy scale and the removal of combinatorial jets from the inclusive jet yield is done in one step. This additional constraining power is achieved by creating a mapping between the jet properties and the corrected jet 𝑝T, which may also provide the opportunity to measure jets in Pb–Pb collisions with lower jet 𝑝Tand larger 𝑅 than is possible with the AB method. However, including constituent information in the training of the ML model introduces a dependence on fragmentation patterns of the training sample, which may differ from those in Pb–Pb collision data, whose effect on the results needs to be addressed. In this manuscript, we present an analysis of inclusive chargedparticle jet production at a center-of-mass energy per nucleon–nucleon collision of √𝑠NN =5.02 TeV. Jets are measured with resolution parameters 𝑅 =0.2, 0.3, 0.4, 0.5, and 0.6 in pp and 𝑅 =0.2, 0.4, and 0.6 in Pb–Pb collisions. The jets in Pb–Pb collisions are also measured for different centrality classes. The analysis of the Pb–Pb collision data in the 0–10% and the 30–50% centrality classes utilizes the novel approach to the correction of the underlying event contribution based on the ML techniques described above, while the analysis of the Pb–Pb collision data in the 60–80% uses the standard area-based subtraction. The jet transverse momentum spectra, jet nuclear modification factors, as well as ratios of jet cross sections and 𝑅AA are presented and compared to model calculations in central (0–10%), semi-central (30–50%), and peripheral (60–80%) Pb–Pb collisions. The dependence on the jet fragmentation model used to train the ML algorithm was studied and incorporated as a systematic uncertainty. The new analysis extends previous measurements of inclusive charged-particle jet suppression at the LHC to both lower 𝑝Tand larger 𝑅, measuring jets down to 𝑝T=30GeV/𝑐for 𝑅 =0.4and to 𝑝T=40GeV/𝑐for 𝑅 =0.6. The article is structured as follows: details on the detector and data reconstruction are given in Sec. 2. The jet reconstruction is described in Sec. 3. The jet background correction method based on ML techniques is introduced in Sec. 4. The dependence of the new background estimator on the fragmentation pattern used in the training and unfolding is discussed in Sec. 5. The systematic uncertainties are discussed in Sec. 6. The results and comparison with model calculations are presented in Sec. 7. A summary concludes the paper in Sec. 8. Appendix Bdescribes the insensitivity of the ML correction to correlated background fluctuations. 2. Experimental setup A detailed description of the ALICE detector can be found in Ref. [74], and its performance is described in Ref. [75]. The analyzed dataset for Pb–Pb collisions at √𝑠NN =5.02 TeV was collected in 2015, with online triggers that utilize the hit multiplicity measured by forward V0 detectors. The V0 detectors are segmented scintillators covering the full azimuth over the pseudorapidity ranges 2.8 <𝜂<5.1(V0A) and −3.7 <𝜂<−1.7(V0C). The accepted events, reconstructed as described in Ref. [76], were required to have a reconstructed primary vertex within ±10 cm from the nominal interaction point along the beam axis and the obtained sample corresponds to an integrated luminosity of about 6.5 μb−1. Events were characterized with V0 multiplicities corresponding to the 0–10%, 30–50%, and 60–80% centrality ranges using the centrality determination described in Ref. [77]. The 0–10% centrality range corresponds to the most central 10% of the Pb–Pb inelastic cross section and 60–80% corresponds to more peripheral collisions. The analyzed dataset for pp collisions at √𝑠=5.02 TeV was collected in 2017 during Run 2 of the LHC with an integrated luminosity of about 19 nb−1 [78]. Events were triggered using the V0 detector by having signals in both the V0A and V0C. Accepted events were required to have a reconstructed primary vertex within ±10 cm from the nominal interaction point along the beam axis, the same as the events in Pb–Pb collisions. This analysis utilizes the ALICE tracking system in the central rapidity region, which is located inside a large solenoidal magnet with a field strength of 0.5T aligned with the beam axis. This system consists of the ITS, a high-precision six-layer cylindrical silicon detector with the innermost layer at 3.9cm and the outermost layer at 43 cm radial distance from the beam axis; and the TPC with radial extent of 85–247 cm, which provides up to 159 independent space points per track. To ensure good track-momentum resolution for jet reconstruction, reconstructed tracks are required to have at least three hits in the ITS. For tracks without any hit in the Silicon Pixel Detector (SPD), comprising the two innermost layers of the ITS, the location of the primary vertex is used to constrain the track. This approach improves the track momentum resolution and reduces the azimuthal variation in the track reconstruction efficiency arising from the non-uniform SPD acceptance. Accepted tracks are required to have 𝑝T>0.15 GeV/𝑐and |𝜂| <0.9, and to have at least 70 TPC space-points, comprising no fewer than 80% of the geometrically findable space-points in the TPC. For pp collisions, the single-track reconstruction efficiency is estimated using pp events generated with the PYTHIA 8 (Monash 2013 tune) [79] generator together with the GEANT 3-based detector simulation and response model of ALICE [80]. The efficiency is approximately 67% for track 𝑝T=0.15 GeV/𝑐, rising to approximately 84% at track 𝑝T=1GeV/𝑐and remaining above 75% at higher track 𝑝T[64]. The tracking efficiency in 0–10% Pb–Pb collisions as compared to that in Physics Letters B 849 (2024) 138412 3 ALICE Collaboration pp collisions was estimated by comparing central to peripheral HIJING+GEANT 3[81]events, resulting in an approximately 2% reduction in the tracking efficiency as compared to pp, independent of the track 𝑝T. The momentum resolution in pp collisions at the primary vertex, which is determined on a track-by-track basis using a Kalman filter approach [82], is about 1% at a track 𝑝Tof 1GeV/𝑐and about 4% at 50 GeV/𝑐. In heavy-ion collisions, the momentum resolution at high track 𝑝Tis approximately 10–15% worse than in pp collisions. The contamination by secondary particles [83]produced in particle–material interactions, conversions, and weak decays of long-lived particles, is a few percent of the yield. For the reference spectra from pp collisions, the jet measurement is carried out as described in Ref. [84]on the 2017 pp dataset at √𝑠=5.02 TeV. This dataset is 10 times larger than the dataset used in Ref. [84], extending the charged-particle jet spectra for all considered 𝑅values up to jet 𝑝T= 140 GeV/𝑐. The values for the ⟨𝑇AA⟩in the nuclear modification factor Eq. (1)were computed in a Glauber model [85]to be 23.26 ±0.168, 3.917 ±0.0645, and 0.4188 ±0.0106 mb−1 in central (0–10%), semicentral (30–50%), and peripheral (60–80%) collisions, respectively. 3. Jet reconstruction and unfolding Charged-particle jets are reconstructed using the anti-𝑘Talgorithm with 𝐸-scheme recombination [86,87]in the FastJet package [72]with resolution parameters 𝑅 =0.2, 0.3, 0.4, 0.5, and 0.6 in pp collisions and 𝑅 =0.2, 0.4, and 0.6in Pb–Pb collisions. Jet candidates are accepted for further analysis if their axis, defined using the standard axis [88], is reconstructed within the pseudorapidity range |𝜂jet| <0.9 −𝑅to assure that the nominal jet cone is fully contained within the track acceptance of |𝜂| <0.9. Ajet area cut of 𝐴jet >0.56𝜋𝑅2is applied to suppress contamination by non-physical jets [36,49,63]. Jets containing a track with 𝑝T>100 GeV/𝑐are additionally removed due to reduced momentum resolution in this region. In this paper, jets are corrected with either an area-based or ML-based background correction. The transverse momentum of reconstructed jets is affected both by residual fluctuations from the UE remaining due to imperfect background subtraction as well as detector effects (mainly the tracking efficiency and the track 𝑝Tresolution). To account for these effects, pp collision events were simulated with the PYTHIA 8 generator using the Monash 2013 tune [89] (particle-level) and the particles were then propagated through a model of the ALICE detector using GEANT 3 particle transport framework [80] (detector-level). These events were then embedded into Pb–Pb minimum bias data to form hybrid events (hybrid-level). In these events, the same detector configuration is simulated as that utilized during the data taking of the above-mentioned Pb–Pb dataset. To account for a reduction in tracking efficiency for central and semi-central Pb–Pb collisions relative to pp and peripheral Pb–Pb collisions (where the tracking efficiency is similar), 2% of tracks were randomly rejected, independent of the track 𝑝T. Particle-level and hybrid-level jets are matched by the following two-step procedure. First, the hybrid-level jet is matched geometrically to a detector-level jet, where only matches with a maximum distance of 0.75 ×𝑅are accepted. The hybrid and detector-level jets are additionally required to share particles responsible for at least 50% of the jet 𝑝T. Then the detector-level jet is matched geometrically with a maximum distance of 0.75 ×𝑅to a particle-level jet. These matched jets form a correspondence between the true and reconstructed-level (hybrid-level) jet 𝑝T, which is then used to fill a response matrix to reflect this mapping. The jet reconstruction efficiency, defined as the ratio of the number of accepted detector-level jets geometrically matched to a particle-level jet and the number of particle-level jets in a given 𝑝true Tinterval, 𝜀rec(𝑝true T,ch jet )=𝑁matched(𝑝true T,ch jet )∕𝑁generated(𝑝true T,ch jet ),(2) accounts also for the efficiency of matching jets. The jet reconstruction efficiency is high (above 95%) in all regions of phase space. It is used to correct the unfolded spectrum. The spectra are unfolded using the iterative Bayesian approach [90] implemented in the RooUnfold package [91], with the response matrix described above. The prior distribution for the unfolding is the PYTHIA particle-level distribution. The number of iterations, which is the regularization parameter, was selected to be the value where the unfolded result becomes stable compared to further iterations, balanced against the increasing statistical errors. This selected value is referred to as the nominal result, while a variation on this value is taken as a systematic uncertainty (see Section 6). Avalue of 8 iterations is used for the nominal result for the most central collisions. The lower limit in the measured transverse jet momentum that serves as input to the unfolding procedure corresponds to five times the width of the distribution of residual fluctuations remaining after background subtraction, to avoid contamination from combinatorial jets [63]. Some particle-level jets will migrate outside of the measured kinematic range, which is corrected with the so-called kinematic efficiency correction. Arequirement of a minimum kinematic efficiency of 60% is also imposed on the considered jet 𝑝Tintervals, while lower efficiency regions are rejected. 4. Machine learning-based background correction To expand the jet 𝑝Tand 𝑅reach of the measurement, anovel estimator based on machine learning is used to correct the 𝑝T-smearing effects caused by the background. This new background estimator, introduced and described in detail in Ref. [73], is used for the first time and follows an alternative approach to the established area-based method. The method utilizes the properties of each individual jet candidate to assign a correction for the background contribution to the measured 𝑝Tof the jet. While the background is dominated by low-𝑝T particles, the particles in the jet signal are distributed towards higher 𝑝Tconstituents [71,92]. However, the relationship between the relevant input features of the jet candidate and the true jet 𝑝Tis complex. Machine learning techniques are powerful tools to approximate this mapping by learning from simulation instead of deriving the relation from expert knowledge alone. This problem represents a regression task, which aims to predict a reconstructed jet 𝑝Tvalue for each jet candidate. The main physics motivation and performance are described here, while further details on the implementation and validation of this approach are described in Appendix A. Measurements of jet shapes have shown that certain features of quenched jets are similar to unmodified jets in vacuum, notably that the core of the jet is mildly modified due to quenching [51,54]. This observation motivates the strategy adopted in this analysis to train on jets produced by the PYTHIA 8 generator for pp collisions. We assess the systematic uncertainty due to possible variation in the fragmentation in Pb–Pb collisions with respect to pp collisions generated with PYTHIA 8 in Sec. 5. Simulated jets with known transverse momentum are embedded into real Pb–Pb events to compare the ML background estimator to the area-based estimator. The left panel of Fig. 1shows the distribution of the residual difference (𝛿𝑝T) between background-corrected 𝑝T and target detector-level probe 𝑝T, which measures how precisely the background is approximated. The plot compares 𝛿𝑝Tdistributions of the area-based and ML-based background estimators for 𝑅 =0.4. The right panel of Fig. 1provides the standard deviation of the residual distributions of both background estimators versus 𝑅for different centralities. The ML-based estimator has an approximately two times narrower 𝛿𝑝T distribution than the area-based estimator, indicating a reduction in residual fluctuations. The performance of the ML-based estimator also has no dependence on the angle between the jet axis and the event plane, which is briefly discussed in Appendix B, indicating that the estimator is insensitive to correlated fluctuations in the background. Physics Letters B 849 (2024) 138412 4 ALICE Collaboration Fig. 1. Residual 𝑝T-distributions of embedded jet probes of known transverse momentum into Pb–Pb collision data. Left: Comparison of the distributions for the area-based and ML-based background estimators. Note the lines connecting the points do not represent a fit and are only present to guide the eye. Right: Radius dependence of the width of the distributions where the error bars come from the uncertainty in calculating the width. 5. Quenched jet fragmentation dependence The machine learning-based background estimator is trained on jets generated with PYTHIA 8 simulations for pp collisions, where the fragmentation is known to differ quantitatively from the fragmentation in Pb–Pb data to which the estimator is applied [53,54,93,94]. The inclusion of specific fragmentation properties in the learning step of the ML-based background correction procedure introduces an explicit fragmentation dependence. In comparison, although the area-based correction itself is not strongly fragmentation-dependent, this method is often combined with a requirement on the 𝑝Tof the leading charged constituent to suppress the background contribution, which biases the fragmentation of the jet sample. There are three points where the ML-based procedure is sensitive to jet fragmentation: the measured input spectra, the response matrix, and the training. In this section, we explore this dependence using model studies and quantify it as a component of the systematic uncertainty in the measured spectra. In these studies, the training and the response matrix were varied, allowing for the effect of a different fragmentation to be quantified through the full procedure. The procedure to estimate the systematic uncertainties on the inclusive jet spectra due to this fragmentation dependence is discussed in the following. The results are summarized in Sec. 6. The sensitivity of the ML method to the fragmentation distribution of the Monte Carlo sample used for training generated with PYTHIA 8 is explored by modifying the fragmentation distribution in physicsmotivated ways. One way to vary the fragmentation model is by utilizing quark or gluon-initiated jets. Quark jets tend to be narrower and have fewer constituents, each of them carrying a significant fraction of the jet’s momentum (harder fragmentation), while gluon jets tend to be wider and have more constituents carrying smaller fractions of the jet’s momentum, 𝑧 =𝑝T,track 𝑝T,jet . In practice, the inclusive jet population contains a mixture of quark and gluon jets, so using only quark or gluon-initiated jets provides significant variation to the fragmentation. Recent analyses suggest that the properties of quenched jets, excluding the enhancement at low 𝑧, may result primarily from the different quenching of quarks and gluons [95]. Additionally, in-medium parton interactions lead to a variety of physical effects. Phenomenological modifications are performed by branching off additional hadrons from existing jet constituents, changing the final-state hadron distribution. The modifications are governed by tunable parameters specifying 𝑝loss, the probability of branching off a particle; 𝑓loss, the fraction of the jet constituent 𝑝Tto radiate; and Δ𝑅, the maximum angle of the emission relative to the jet constituent. For each jet constituent, a particle is radiated with probability 𝑝loss, carrying 𝑝T=𝑓loss𝑝const. Tat an angle randomly sampled from a uniform distribution between 0 and Δ𝑅. Three different shower modifications were studied using this framework, with each variation modifying the final-state hadron distribution to model a different aspect of in-medium jet modification. Below is a summary of all the fragmentation modifications used in this analysis: 1. Quark Only: jets originating from a quark in the PYTHIA 8 simulation are used. 2. Gluon Only: jets originating from a gluon in the PYTHIA 8 simulation are used. 3. Fractional Collinear: the radiated particle carries a specific fraction of the original constituent’s energy and is emitted predominantly within the jet cone by setting Δ𝑅to 0.1, 0.2, and 0.4 for 𝑅 =0.2, 0.4, and 0.6, respectively. The three-momentum of the radiated particle is then subtracted from the original jet constituent three-momentum, and the radiated particle is added to the list of jet constituents if it falls within the jet cone. 4. Fractional Large Angle: the radiated particle carries a specific fraction of the original constituent’s energy and is frequently emitted outside the jet cone by setting Δ𝑅to 0.4, 0.6, and 0.8 for 𝑅 =0.2, 0.4, and 0.6, respectively. The three-momentum of the radiated particle is then subtracted from the original jet constituent three-momentum, and the radiated particle is added to the list of jet constituents if it falls within the jet cone. 5. Medium Response: the emission occurs as described for the Fractional Collinear case, but the original jet constituent 𝑝Tis unmodified, emulating the addition of particles from the medium into the jet. The kinematic modifications vary both the momentum scale and the angular distribution of jet constituents and, thereby, the jet distributions themselves. Existing measurements guided the values of the tunable parameters used in the phenomenological modifications. Specifically, the 𝑝loss values were determined by evaluating the excess particle yield for jets in Pb–Pb collisions compared to those in pp collisions using the jet radial profiles for 𝑅 =0.4inclusive jets above 100 GeV/𝑐[96]. Each modification attributes the 𝑝loss value to individual effects, whereas in reality, the overall observed modification combines contributions from all of them. Therefore, this approach overestimates the contribution of each individual effect, which is a conservative choice to account for the Physics Letters B 849 (2024) 138412 5 ALICE Collaboration Fig. 2. Left: Comparison of toy model modifications for 𝑅 =0.4jets using the 𝑅mod as defined in Eq. (3)Right: The ratio of the modified to unmodified fragmentation functions at low jet 𝑝T(40 <𝑝 T,true <100 GeV∕𝑐, lower right panel) and high jet 𝑝T(100 <𝑝 T,true <200 GeV∕𝑐, upper right panel) for 0–10% central Pb–Pb collisions. In the fractional collinear and fractional large angle case, 𝑓loss = 25% and 𝑝loss = 100%. In the medium response case, 𝑓loss = 10% and 𝑝loss = 50%. The ratio of the fragmentation functions measured in Pb–Pb and pp collisions are shown for jets with 𝑅 =0.4and 𝑝T>100 GeV/𝑐[53] (ATLAS), and for jets with 𝑅 =0.3and 𝑝T>30 GeV/𝑐recoiling from a photon with 𝐸T>60 GeV/𝑐[94](CMS). fact that the 𝑝loss values are not extrapolated when applied to lower energy jets. The excess yield outside of the jet cone was used to fix the value of 𝑝loss for the Fractional Large Angle model, and the excess inside of the jet cone was used to fix the value of 𝑝loss for the Fractional Collinear and Medium Response models. The modifications were then compared to existing fragmentation measurements, as discussed below. The values of 𝑓loss were set to 25% and 10%, which provide a charged hadron 𝑅AA of comparable magnitude to the measured values in the 0–10% and 30–50% centrality ranges, respectively [97]. We compare the modified and the unmodified jet distributions by their ratio, 𝑅mod, 𝑅mod =Ymodified Yunmodified ,(3) shown for 𝑅 =0.4jets as a function of 𝑝Tin the left panel of Fig. 2. The medium response adds energy to the jet cone, resulting in 𝑅mod >1. For the fractional collinear model, the jet does not lose energy most of the time, which results in 𝑅mod ≈1. In the case of fractional large-angle radiation, the jet will lose energy, resulting in 𝑅mod <1. Note that the modifications shown here do not directly translate into the associated systematic uncertainty, which instead corresponds to the propagation of this yield modification through the ML-based correction and unfolding. To quantify the modifications introduced by the various fragmentation scenarios, the ratio between modified and unmodified jet fragmentation functions as a function of 𝑧is shown in the right panels of Fig. 2. Both panels include comparisons to the measured ratio of fragmentation functions in Pb–Pb and pp collisions for 𝑅 =0.4inclusive jets with 𝑝T>100 GeV/𝑐from ATLAS [53]and 𝑅 =0.3photon-tagged jets with 𝑝T>30 GeV/𝑐from CMS [94]. The kinematic region of the ATLAS measurement is the only one where the fragmentation function of inclusive jets has been measured, so this is a possible region to check that the toy modifications cover the full phase space of modifications as observed in the data. The CMS measurement is a quark-dominated sample and does not fully describe the phase space measured in this analysis, but it is still useful for the purpose of comparing the magnitude of the induced variations in the toy models. The top right panel shows the modifications for 𝑅 =0.4jets in the 0–10% centrality class with 100 <𝑝 T<200 GeV/𝑐. The Medium Response and Fractional Collinear models describe the measured low-𝑧enhancement of soft particles. The quark-only case describes the intermediate-𝑧suppression and high-𝑧en- hancement. Additionally, the ratio between modified and unmodified jet fragmentation functions is shown in the bottom right panel of Fig. 2 for 𝑅 =0.4jets with 40 <𝑝 T<100 GeV/𝑐in 0–10% central collisions. The fragmentation in this region has not been measured, so no direct comparison is possible, but the features are qualitatively the same as for the 𝑅 =0.4jets at high 𝑝T, albeit with more significant modification. The toy model variations introduced here cover the modification of the photon-recoiling jet fragmentation measured by CMS from pp to Pb–Pb collisions over a similar kinematic region. For each variation, both the training and the response matrix were varied to quantify the effect of a different fragmentation model through the full analysis chain. To ensure realistic variations for the systematic uncertainties, the unfolded spectrum corrected using the ML estimator was refolded with the response matrix filled with jets corrected using the AB method. The result was then compared to the spectrum obtained with the AB method at the hybrid level to ensure the result was similar to the one that would have been achieved with the AB method. For a variation to be considered, we required an agreement comparable to the size of the unfolding uncertainties. In principle, any unfolded heavy-ion measurement could have a fragmentation bias inherent to the unfolding procedure and, by definition, assumes a fragmentation model. The method developed for this paper introduces a new range of variations that could be considered for such studies in the future. Unique to the ML method is the fragmentation dependence of the training, but the results varied minimally when only the training sample fragmentation was varied, indicating that the main effect is due to the fragmentation in the response matrix. 6. Systematic uncertainties The systematic uncertainties of the inclusive jet spectra arise from the tracking efficiency, the unfolding procedure, and the model dependence in the ML method. The full systematic uncertainty is given by the quadratic sum of the individual uncertainties, where the single uncertainties are taken to be symmetric about the nominal value unless Physics Letters B 849 (2024) 138412 6 ALICE Collaboration Table 1 Relative systematic uncertainties (%) for jet spectra for 0–10% central Pb–Pb collisions and all resolution parameters. The maximum uncertainties for low 𝑝T(𝑝T,jet <50 GeV/𝑐) and high 𝑝T(𝑝T,jet >50 GeV/𝑐) are shown. The direction of asymmetric uncertainties is indicated with a +or −sign. The combined uncertainty is the sum in quadrature of individual uncertainties. Resolution parameter (𝑅)0.20.40.6 𝑝TLow 𝑝THigh 𝑝TLow 𝑝THigh 𝑝TLow 𝑝THigh 𝑝T Tracking eff. 21 18 24 12 12 34 Regularization param. (< 2) (< 2) (< 2) 2 2 (< 2) Unfolding prior 6 16 8 (< 2) 4 (< 2) Measured 𝑝Trange 46 4 8 (< 2) 6 8 Fractional Collinear +30 +12 +12 +16 +8 +20 Fractional Large Angle +10 +10 +6 +10 +8 +14 Fractional Medium Response +28 +14 +20 +14 +22 +14 Quarks/Gluon -8 -6 -12 -12 -14 -12 Combined 58 32 32 26 28 44 Table 2 Relative systematic uncertainties (%) for jet spectra for 30–50% central Pb–Pb collisions and all resolution parameters. The maximum uncertainties for low 𝑝T(𝑝T,jet <50 GeV/𝑐) and high 𝑝T(𝑝T,jet >50 GeV/𝑐) are shown. The direction of asymmetric uncertainties is indicated with a +or −sign. The combined uncertainty is the sum in quadrature of individual uncertainties. Resolution parameter (𝑅)0.20.40.6 𝑝TLow 𝑝THigh 𝑝TLow 𝑝THigh 𝑝TLow 𝑝THigh 𝑝T Tracking eff. 10 12 12 16 12 14 Regularization param. (<2) (<2) (<2) (<2) (<2) (<2) Unfolding prior (<2) 462(<2) 6 Measured 𝑝Trange 28 (<2) 10 (<2) 20 (<2) Fractional Collinear +12 +6 +22 +14 +26 +24 Fractional Large Angle +8 +8 +8 +10 +26 +24 Fractional Medium Response +8 +8 +14 +10 +22 +20 Quarks/Gluon -8 -6 -8 -6 -12 -6 Combined 34 22 32 26 42 40 Table 3 Relative systematic uncertainties (%) for jet spectra for 60–80% central Pb–Pb collisions and all used resolution parameters. The maximum uncertainties for low 𝑝T(𝑝T,jet < 50 GeV/𝑐) and high 𝑝T(𝑝T,jet >50 GeV/𝑐) are shown. In this centrality interval the spectra are measured with the area-based method, the uncertainties related to the fragmentation functions adopted in the machine learning algorithm are not included for this case. The combined uncertainty is the sum in quadrature of individual uncertainties. Resolution parameter (𝑅)0.20.40.6 𝑝TLow 𝑝THigh 𝑝TLow 𝑝THigh 𝑝TLow 𝑝THigh 𝑝T Tracking eff. 4 10 2 10 10 14 Regularization param. (<2) (<2) (<2) (<2) (<2) (<2) Unfolding prior 6 (<2) 10 6 (<2) 8 Measured 𝑝Trange 32 4 6 4 36 14 Quarks/Gluon -2 -4 -4 -4 (<−2) -4 Combined 46 14 14 10 50 16 otherwise specified. The following sources were taken into account for the measurements in Pb–Pb collisions: Tracking efficiency uncertainty: the loss of tracks due to tracking efficiency less than unity results in a reduction in jet 𝑝T, which corresponds to a significant reduction in measured yield in a given 𝑝T interval due to the steeply falling jet spectrum. Tracking efficiency is consequently one of the largest sources of systematic uncertainty in this measurement. Detailed studies of the tracking efficiency have been performed to determine an appropriate variation for the systematic uncertainty [75,98]. Those studies motivate the systematic variation, which corresponds to a modified response matrix where 4% of the tracks are randomly discarded. Regularization parameter: to regularize in the Bayesian unfolding procedure, anumber of iterations is chosen for the nominal result where the unfolded results are stable. For the systematic uncertainty, variations of the parameter by ±1 are taken into account. Prior: for the Bayesian unfolding procedure, aprior distribution is needed. The PYTHIA 8 jet 𝑝Tspectrum was taken as the nominal prior. For the systematic uncertainty the sensitivity of the unfolded result to the prior was evaluated by scaling the PYTHIA 8 spectrum by the parameterized ratio of the hybrid-level MC to Pb–Pb collision data, accounting for any shape differences between the two. Then, the difference between the result unfolded by the scaled response and the nominal response was taken as a systematic uncertainty. Measured 𝑝T-range: the minimum transverse momentum of the jets that enter the unfolding procedure is determined by the requirement that it is five times the residual fluctuations 𝜎, which suppresses the fake jet yield (see Sec 3). The low-𝑝Tcut-off of the data that serves Physics Letters B 849 (2024) 138412 7 ALICE Collaboration Fig. 3. The 𝑝T-differential inclusive charged-particle jet yield distributions as a function of 𝑝Tfor different values of 𝑅in three centrality classes: Top Left: 0–10%, top right: 30–50%, bottom left: 60–80%. The peripheral spectra were measured using the area-based method for the background correction. All other reported spectra were corrected with the ML-based background estimator. In the bottom right panel, the production cross sections in pp collisions are shown. The vertical bars denote statistical uncertainties and the vertical extent of the boxes denotes systematic uncertainties. Note that the data points are plotted horizontally at the bin center. as input to the unfolding procedure is varied by ±5 GeV/𝑐. Small-𝑅 jets are expected to be most sensitive to this cut due to their low 𝑝T,jet reach. Fragmentation: the background estimator is trained using the jet spectrum from simulated pp collisions utilizing PYTHIA 8 where the clustered hadrons were constructed following the Lund fragmentation model as discussed at length in Sec. 5. The systematic uncertainty was estimated from the variations in the results obtained using different fragmentation models. In particular, the following alternatives were considered: q/g fragmentation, Medium Response, Fractional Collinear, and Fractional Large Angle. The variations are added in quadrature and the corresponding uncertainties are considered to be asymmetric. For the measurements in pp collisions, several sources of uncertainty were taken into account. For the unfolding, the SVD [99]algorithm was used for the central value of the results, and the relative variation in the results when using the Bayesian method was taken as the uncertainty. The regularization parameters were further varied by ±1 from the nominal values, as in the Pb–Pb case. The unfolding uncertainties, including the algorithm and regularization variations, are estimated from the root-mean-square of these variations. The tracking efficiency uncertainty was estimated in a similar manner as for the Pb–Pb spectra, but in this case, disregarding 3% of the tracks due to the smaller systematic uncertainty on the tracking efficiency in pp collisions. Additionally, there is an uncertainty from secondary track contamination due to weak decays, which was estimated by comparing the secondary track fraction in data and MC. Note that the SVD and secondary track contamination uncertainties are small in Pb–Pb collisions, and therefore are neglected. A summary of the systematic uncertainties discussed above for the spectra is given in Tables 1,2, and 3for 0–10%, 30–50%, and 60–80% Pb–Pb collisions, respectively. The uncertainties of the 𝑅AA are calculated using the systematic uncertainties of the Pb–Pb spectra and the uncertainties of the pp reference (evaluated as in [84]). Included in the 𝑅AA uncertainty is an additional uncertainty associated with the calculation of the ⟨𝑇AA⟩, given in Ref. [85]. The systematic uncertainties on the 𝑅AA double ratios and the cross section ratios for different 𝑅values are evaluated by separately treating correlated and uncorrelated uncertainties. All unfolding uncertainties for the spectra in Pb–Pb collisions are treated as uncorrelated and added in quadrature. The tracking uncertainty and the uncertainties due to the fragmentation dependence are treated as correlated by evaluating the double ratio for each variation and calculating the difference from the nominal double ratio. The deviations from the nominal value for each of these cases are then added in quadrature to obtain the final correlated uncertainty on the 𝑅AA double ratios and the cross section ratios. 7. Results In this section, the 𝑝T-differential inclusive charged-particle jet production yields (Fig. 3), the nuclear modification factors 𝑅AA (Figs. 4,5,6), the ratios of the jet cross sections for different 𝑅(Fig. 7), Physics Letters B 849 (2024) 138412 8 ALICE Collaboration Fig. 4. Nuclear modification factors of inclusive charged-particle jets as a function of 𝑝Tfor 𝑅 =0.2, 𝑅 =0.4, and 𝑅 =0.6, shown for 0–10%, 30–50% and 60–80% central Pb–Pb collisions for the ML-based method compared to results obtained with the area-based method where applicable. and the 𝑅AA double ratios representing the change of nuclear modification with respect to resolution parameter of 𝑅 =0.2(Fig. 8) are reported. The 𝑝T-differential charged-particle jet cross section in pp collisions is shown in the bottom right panel of Fig. 3for a broad range of 𝑅values from 𝑅 =0.2to 𝑅 =0.6. Jets with larger 𝑅capture more of the jet’s energy, shifting the spectra to the right and resulting in an increased yield at fixed jet 𝑝T. The effect is largest at low transverse momenta. The charged-particle jet spectra in Pb–Pb collisions are presented as an event-normalized yield divided by the average nuclear thickness ⟨𝑇AA⟩[85]of the given centrality class 1 ⟨𝑇AA⟩ d2𝑁ch jet d𝑝T,ch jet d𝜂jet [mb (GeV∕𝑐)−1].(4) These spectra are shown in the first three panels of Fig. 3for the three considered centrality classes and the three values of 𝑅. The uncertainty from the Glauber calculation to derive ⟨𝑇AA⟩is included in the normalization uncertainty of the measurements. The spectra for central and semi-central collisions are measured using the ML-based method, while the area-based method is used for peripheral (60–80%) collisions. While the performance of the ML-based correction is comparable to the AB method in peripheral collisions, the AB correction has the benefit of reduced systematic uncertainties due to the absence of fragmentation uncertainties for this method. The area-based method does not include these fragmentation uncertainties because the effect of the fragmentation biases is small [36,49,62]. Fig. 4shows the nuclear modification factors using both the new ML- based estimator and the established area-based estimator for the 0–10%, Physics Letters B 849 (2024) 138412 15 ALICE Collaboration [48] ATLAS Collaboration, G. Aad, et al., Measurements of the nuclear modification factor for jets in Pb–Pb collisions at √𝑠NN =2.76 TeV with the ATLAS Detector, Phys. Rev. Lett. 114 (2015) 072302, arXiv :1411 .2357 [hep -ex]. [49] ALICE Collaboration, J. Adam, et al., Measurement of jet quenching with semiinclusive hadron-jet distributions in central Pb–Pb collisions at √𝑠NN =2.76 TeV, J. High Energy Phys. 09 (2015) 170, arXiv :1506 .03984 [nucl -ex]. [50] ALICE Collaboration, S. Acharya, et al., First measurement of jet mass in Pb–Pb and p–Pb collisions at the LHC, Phys. Lett. B 776 (2018) 249–264, arXiv :1702 .00804 [nucl -ex]. [51] ALICE Collaboration, S. Acharya, et al., Medium modification of the shape of smallradius jets in central Pb–Pb collisions at √𝑠NN =2.76 TeV, J. High Energy Phys. 10 (2018) 139, arXiv :1807 .06854 [nucl -ex]. [52] CMS Collaboration, A.M. Sirunyan, et al., Measurement of the groomed jet mass in Pb–Pb and pp collisions at √𝑠NN =5.02 TeV, J. High Energy Phys. 10 (2018) 161, arXiv :1805 .05145 [hep -ex]. [53] ATLAS Collaboration, M. Aaboud, et al., Measurement of jet fragmentation in Pb+Pb and 𝑝𝑝 collisions at √𝑠𝑁𝑁 =5.02 TeV with the ATLAS detector, Phys. Rev. C 98 (2018) 024908, arXiv :1805 .05424 [nucl -ex]. [54] ALICE Collaboration, S. Acharya, et al., Measurement of jet radial profiles in Pb– Pb collisions at √𝑠NN =2.76 TeV, Phys. Lett. B 796 (2019) 204–219, arXiv :1904 . 13118 [nucl -ex]. [55] CMS Collaboration, A.M. Sirunyan, et al., Measurement of the Splitting Function in 𝑝𝑝 and Pb–Pb Collisions at √𝑠NN =5.02 TeV, Phys. Rev. Lett. 120 (2018) 142302, arXiv :1708 .09429 [nucl -ex]. [56] ALICE Collaboration, Measurement of inclusive and leading subjet fragmentation in pp and Pb–Pb collisions at √𝑠NN =5.02 TeV, arXiv :2204 .10270 [nucl -ex]. [57] D. d’Enterria, C. Loizides, Progress in the Glauber model at collider energies, Annu. Rev. Nucl. Part. Sci. 71 (2021) 315–344, arXiv :2011 .14909 [hep -ph]. [58] D. Pablos, Jet suppression from a small to intermediate to large radius, Phys. Rev. Lett. 124 (2020) 052301, arXiv :1907 .12301 [hep -ph]. [59] R. Kunnawalkam Elayavalli, K.C. Zapp, Medium response in JEWEL and its impact on jet shape observables in heavy ion collisions, J. High Energy Phys. 07 (2017) 141, arXiv :1707 .01539 [hep -ph]. [60] H.T. Li, I. Vitev, Inclusive heavy flavor jet production with semi-inclusive jet functions: from proton to heavy-ion collisions, J. High Energy Phys. 07 (2019) 148, arXiv :1811 .07905 [hep -ph]. [61] Y. He, S. Cao, W. Chen, T. Luo, L.-G. Pang, X.-N. Wang, Interplaying mechanisms behind single inclusive jet suppression in heavy-ion collisions, Phys. Rev. C 99 (2019) 054911, arXiv :1809 .02525 [nucl -th]. [62] STAR Collaboration, J. Adam, et al., Measurement of inclusive charged-particle jet production in Au–Au collisions at √𝑠NN = 200 GeV, Phys. Rev. C 102 (2020) 054913, arXiv :2006 .00582 [nucl -ex]. [63] ALICE Collaboration, B. Abelev, et al., Measurement of charged jet suppression in Pb–Pb collisions at √𝑠NN =2.76 TeV, J. High Energy Phys. 03 (2014) 013, arXiv :1311 .0633 [nucl -ex]. [64] ALICE Collaboration, S. Acharya, et al., Measurements of inclusive jet spectra in pp and central Pb–Pb collisions at √𝑠NN =5.02 TeV, Phys. Rev. C 101 (2020) 034911, arXiv :1909 .09718 [nucl -ex]. [65] ATLAS Collaboration, G. Aad, et al., Measurements of the nuclear modification factor for jets in Pb–Pb collisions at √𝑠NN TeV with the ATLAS Detector, Phys. Rev. Lett. 114 (2015) 072302, arXiv :1411 .2357 [hep -ex]. [66] CMS Collaboration, V. Khachatryan, et al., Measurement of inclusive jet cross sections in pp and Pb–Pb collisions at √𝑠NN =2.76 TeV, Phys. Rev. C 96 (2017) 015202, arXiv :1609 .05383 [nucl -ex]. [67] CMS Collaboration, A.M. Sirunyan, et al., First measurement of large area jet transverse momentum spectra in heavy-ion collisions, J. High Energy Phys. 05 (2021) 284, arXiv :2102 .13080 [hep -ex]. [68] ATLAS Collaboration, G. Aad, et al., Measurement of the jet radius and transverse momentum dependence of inclusive jet suppression in lead-lead collisions at √𝑠𝑁𝑁 =2.76 TeV with the ATLAS detector, Phys. Lett. B 719 (2013) 220–241, arXiv :1208 .1967 [hep -ex]. [69] J. Alme, et al., The ALICE TPC, alarge 3-dimensional tracking device with fast readout for ultra-high multiplicity events, Nucl. Instrum. Methods A 622 (2010) 316–367, arXiv :1001 .1950 [physics .ins -det]. [70] ALICE Collaboration, K. Aamodt, et al., Alignment of the ALICE Inner Tracking System with cosmic-ray tracks, J. Instrum. 5 (2010) P03003, arXiv :1001 .0502 [physics .ins -det]. [71] ALICE Collaboration, B. Abelev, et al., Measurement of event background fluctuations for charged particle jet reconstruction in Pb–Pb collisions at √𝑠NN = 2.76 TeV, J. High Energy Phys. 1203 (2012) 053, arXiv :1201 .2423 [hep -ex]. [72] M. Cacciari, G.P. Salam, G. Soyez, FastJet User Manual, Eur. Phys. J. C 72 (2012) 1896, arXiv :1111 .6097 [hep -ph]. [73] R. Haake, C. Loizides, Machine learning based jet momentum reconstruction in heavy-ion collisions, Phys. Rev. C 99 (2019) 064904, arXiv :1810 .06324 [nucl -ex]. [74] ALICE Collaboration, K. Aamodt, et al., The ALICE experiment at the CERN LHC, J. Instrum. 3 (2008) S08002. [75] ALICE Collaboration, B.B. Abelev, et al., Performance of the ALICE experiment at the CERN LHC, Int. J. Mod. Phys. A 29 (2014) 1430044, arXiv :1402 .4476 [nucl - ex]. [76] ALICE Collaboration, B. Abelev, et al., Centrality dependence of charged particle production at large transverse momentum in Pb–Pb collisions at √𝑠NN =2.76 TeV, Phys. Lett. B 720 (2013) 52–62, arXiv :1208 .2711 [hep -ex]. [77] ALICE Collaboration, B. Abelev, et al., Centrality determination of Pb–Pb collisions at √𝑠NN =2.76 TeV with ALICE, Phys. Rev. C 88 (2013) 044909, arXiv :1301 .4361 [nucl -ex]. [78] ALICE Collaboration, ALICE 2017 luminosity determination for pp collisions at √𝑠=5TeV, http://cds .cern .ch /record /2648933. [79] T. Sjöstrand, S. Ask, J.R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C.O. Rasmussen, P.Z. Skands, An introduction to PYTHIA 8.2, Comput. Phys. Commun. 191 (2015) 159–177, arXiv :1410 .3012 [hep -ph]. [80] R. Brun, F. Bruyant, F. Carminati, S. Giani, M. Maire, A. McPherson, G. Patrick, L. Urban, GEANT: detector description and simulation tool; Oct 1994, in: CERN Program Library, CERN, Geneva, 1993, https://cds .cern .ch /record /1082634, Long Writeup W5013. [81] X.-N. Wang, M. Gyulassy, HIJING: a Monte Carlo model for multiple jet production in pp, pA and AA collisions, Phys. Rev. D 44 (1991) 3501–3516. [82] ALICE Collaboration, Y. Belikov, K. Safarík, B. Batyunya, Kalman Filtering Application for Track Recognition and Reconstruction in ALICE Tracking System, Tech. rep., CERN, Geneva, 1997, http://cds .cern .ch /record /689414. [83] ALICE Collaboration, S. Acharya, et al., The ALICE definition of primary particles, ALICE-PUBLIC-2017-005, https://cds .cern .ch /record /2270008, Jun 2017. [84] ALICE Collaboration, S. Acharya, et al., Measurement of charged jet cross section in pp collisions at √𝑠=5.02 TeV, Phys. Rev. D 100 (2019) 092004, arXiv :1905 .02536 [nucl -ex]. [85] C. Loizides, J. Kamin, D. d’Enterria, Improved Monte Carlo Glauber predictions at present and future nuclear colliders, Phys. Rev. C 97 (2018) 054910, arXiv : 1710 .07098; Erratum: Phys. Rev. C 99 (1) (2019) 019901. [86] M. Cacciari, G.P. Salam, G. Soyez, The Anti-k(t) jet clustering algorithm, J. High Energy Phys. 0804 (2008) 063, arXiv :0802 .1189 [hep -ph]. [87] M. Cacciari, G.P. Salam, G. Soyez, The catchment area of jets, J. High Energy Phys. 04 (2008) 005. [88] P. Cal, D. Neill, F. Ringer, W.J. Waalewijn, Calculating the angle between jet axes, J. High Energy Phys. 04 (2020) 211, arXiv :1911 .06840 [hep -ph]. [89] P. Skands, S. Carrazza, J. Rojo, Tuning PYTHIA 8.1: the Monash 2013 Tune, Eur. Phys. J. C 74 (2014) 3024, arXiv :1404 .5630 [hep -ph]. [90] G. D’Agostini, Improved iterative Bayesian unfolding, in: Alliance Workshop on Unfolding and Data Correction, vol. 10, 2010, arXiv :1010 .0632 [physics .data -an]. [91] T. Adye, Unfolding algorithms and tests using RooUnfold, in: PHYSTAT 2011, CERN, Geneva, 2011, pp. 313–318, arXiv :1105 .1160 [physics .data -an]. [92] ALICE Collaboration, S. Acharya, et al., Charged jet cross section and fragmentation in proton–proton collisions at √𝑠=7TeV, Phys. Rev. D 99 (2019) 012016, arXiv :1809 .03232 [nucl -ex]. [93] CMS Collaboration, S. Chatrchyan, et al., Measurement of jet fragmentation in Pb–Pb and pp collisions at √𝑠𝑁𝑁 =2.76 TeV, Phys. Rev. C 90 (2014) 024908, arXiv :1406 .0932 [nucl -ex]. [94] CMS Collaboration, A.M. Sirunyan, et al., Observation of medium-induced modifications of jet fragmentation in Pb–Pb collisions at √𝑠NN =5.02 TeV using isolated photon-tagged jets, Phys. Rev. Lett. 121 (2018) 242301, arXiv :1801 .04895 [hep -ex]. [95] M. Spousta, B. Cole, Interpreting single jet measurements in Pb–Pb collisions at the LHC, Eur. Phys. J. C 76 (2016) 50, arXiv :1504 .05169 [hep -ph]. [96] CMS Collaboration, A.M. Sirunyan, et al., Jet properties in Pb–Pb and pp collisions at √𝑠NN =5.02 TeV, J. High Energy Phys. 05 (2018) 006, arXiv :1803 .00042 [nucl - ex]. [97] ALICE Collaboration, S. Acharya, et al., Transverse momentum spectra and nuclear modification factors of charged particles in pp, p–Pb and Pb–Pb collisions at the LHC, J. High Energy Phys. 11 (2018) 013, arXiv :1802 .09145 [nucl -ex]. [98] ALICE Collaboration, B. Abelev, et al., Measurement of charged jet suppression in Pb–Pb collisions at √𝑠NN =2.76 TeV, J. High Energy Phys. 1403 (2014) 013, arXiv :1311 .0633 [nucl -ex]. [99] A. Hocker, V. Kartvelishvili, SVD approach to data unfolding, Nucl. Instrum. Methods A 372 (1996) 469–481, arXiv :hep -ph /9509307 [hep -ph]. [100] J. Casalderrey-Solana, D.C. Gulhan, J.G. Milhano, D. Pablos, K. Rajagopal, A hybrid strong/weak coupling approach to jet quenching, J. High Energy Phys. 10 (2014) 019, arXiv :1405 .3864 [hep -ph]; Erratum: J. High Energy Phys. 09 (2015) 175. [101] Y. He, T. Luo, X.-N. Wang, Y. Zhu, Linear Boltzmann transport for jet propagation in the quark-gluon plasma: elastic processes and medium recoil, Phys. Rev. C 91 (2015) 054908, arXiv :1503 .03313 [nucl -th]; Erratum: Phys. Rev. C 97 (2018) 019902. [102] W. Ke, X.-N. Wang, QGP modification to single inclusive jets in a calibrated transport model, J. High Energy Phys. 05 (2021) 041, arXiv :2010 .13680 [hep -ph]. [103] J.H. Putschke, et al., The JETSCAPE framework, arXiv :1903 .07706 [nucl -th]. [104] S. Cao, A. Majumder, Nuclear modification of leading hadrons and jets within a virtuality ordered parton shower, Phys. Rev. C 101 (2020) 024903, arXiv :1712 . 10055 [nucl -th]. [105] Y. Mehtar-Tani, D. Pablos, K. Tywoniuk, Cone-size dependence of jet suppression in heavy-ion collisions, Phys. Rev. Lett. 127 (2021) 252301, arXiv :2101 .01742 [hep -ph]. [106] B. Schenke, C. Gale, S. Jeon, MARTINI: an event generator for relativistic heavy-ion collisions, Phys. Rev. C 80 (2009) 054913, arXiv :0909 .2037 [hep -ph]. Physics Letters B 849 (2024) 138412 16 ALICE Collaboration [107] K.C. Zapp, F. Krauss, U.A. Wiedemann, A perturbative framework for jet quenching, J. High Energy Phys. 1303 (2013) 080, arXiv :1212 .1599 [hep -ph]. [108] K.C. Zapp, JEWEL 2.0.0: directions for use, Eur. Phys. J. C 74 (2014) 2762, arXiv : 1311 .0048 [hep -ph]. [109] ALICE Collaboration, B. Abelev, et al., Measurement of the inclusive differential jet cross section in pp collisions at √𝑠=2.76 TeV, Phys. Lett. B 722 (2013) 262–272, arXiv :1301 .3475 [nucl -ex]. [110] ALICE Collaboration, S. Acharya, et al., Measurement of the groomed jet radius and momentum splitting fraction in pp and Pb–Pb collisions at √𝑠NN =5.02 TeV, Phys. Rev. Lett. 128 (2022) 102001, arXiv :2107 .12984 [nucl -ex]. [111] ATLAS Collaboration, Measurement of substructure-dependent jet suppression in Pb+Pb collisions at 5.02 TeV with the ATLAS detector, arXiv :2211 .11470 [nucl - ex]. [112] J.-W. Qiu, F. Ringer, N. Sato, P. Zurita, Factorization of jet cross sections in heavyion collisions, Phys. Rev. Lett. 122 (2019) 252301, arXiv :1903 .01993 [hep -ph]. [113] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice Hall PTR, Upper Saddle River, NJ, USA, 1998. [114] L. Breiman, Random forests, Mach. Learn. 45 (Oct 2001) 5–32. [115] V. Nair, G. Hinton, Rectified linear units improve restricted Boltzmann machines, in: Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10, Omnipress, USA, 2010, pp. 807–814. [116] D. Kingma, J. Ba, Adam: a method for stochastic optimization, CoRR (2014), arXiv : 1412 .6980. [117] G. Louppe, Understanding random forests: from theory to practice, https://arxiv . org /abs /1407 .7502, 2014. ALICE Collaboration S. Acharya126,, D. Adamová 86,, A. Adler 70, G. Aglieri Rinella 32,, M. Agnello 29,, N. Agrawal 51,, Z. Ahammed134,, S. Ahmad 15,, S.U. Ahn 71,, I. Ahuja 37,, A. Akindinov140,, M. Al-Turany 97,, D. Aleksandrov140,, B. Alessandro 56,, H.M. Alfanda 6,, R. Alfaro Molina 67,, B. Ali15,, A. Alici 25,, N. Alizadehvandchali115,, A. Alkin 32,, J. Alme 20,, G. Alocco52,, T. Alt 64,, I. Altsybeev 140,, J.R. Alvarado44,, M.N. Anaam 6,, C. Andrei 45,, A. Andronic125,, V. Anguelov94,, F. Antinori 54,, P. Antonioli51,, N. Apadula 74,, L. Aphecetche 103,, H. Appelshäuser 64,, C. Arata73,, S. Arcelli 25,, M. Aresti 52,, R. Arnaldi56,, J.G.M.C.A. Arneiro 110,, I.C. Arsene 19,, M. Arslandok 137,, A. Augustinus32,, R. Averbeck 97,, M.D. Azmi 15,, A. Badalà 53,, J. Bae 104,, Y.W. Baek 40,, X. Bai119,, R. Bailhache64,, Y. Bailung 48,, A. Balbino 29,, A. Baldisseri 129,, B. Balis 2,, D. Banerjee 4,, Z. Banoo91,, R. Barbera 26,, F. Barile 31,, L. Barioglio 95,, M. Barlou 78, G.G. Barnaföldi 46,, L.S. Barnby85,, V. Barret 126,, L. Barreto 110,, C. Bartels 118,, K. Barth 32,, E. Bartsch 64,, N. Bastid126,, S. Basu75,, G. Batigne 103,, D. Battistini 95,, B. Batyunya 141,, D. Bauri 47, J.L. Bazo Alba 101,, I.G. Bearden 83,, C. Beattie 137,, P. Becht 97,, D. Behera 48,, I. Belikov128,, A.D.C. Bell Hechavarria 125,, F. Bellini 25,, R. Bellwied115,, S. Belokurova140,, V. Belyaev 140,, G. Bencedi 46,, S. Beole 24,, Y. Berdnikov140,, A. Berdnikova94,, L. Bergmann 94,, M.G. Besoiu 63,, L. Betev 32,, P.P. Bhaduri134,, A. Bhasin91,, M.A. Bhat 4,, B. Bhattacharjee 41,, L. Bianchi 24,, N. Bianchi49,, J. Bielˇ cík35,, J. Bielˇ cíková86,, J. Biernat 107,, A.P. Bigot 128,, A. Bilandzic 95,, G. Biro46,, S. Biswas 4,, N. Bize 103,, J.T. Blair108,, D. Blau 140,, M.B. Blidaru 97,, N. Bluhme 38, C. Blume 64,, G. Boca 21,55,, F. Bock 87,, T. Bodova20,, A. Bogdanov 140, S. Boi 22,, J. Bok 58,, L. Boldizsár 46,, M. Bombara37,, P.M. Bond 32,, G. Bonomi133,55,, H. Borel 129,, A. Borissov 140,, A.G. Borquez Carcamo 94,, H. Bossi137,, E. Botta 24,, Y.E.M. Bouziani 64,, L. Bratrud64,, P. Braun-Munzinger 97,, M. Bregant 110,, M. Broz 35,, G.E. Bruno96,31,, M.D. Buckland 23,, D. Budnikov 140,, H. Buesching 64,, S. Bufalino29,, P. Buhler 102,, Z. Buthelezi 68,122,, A. Bylinkin20,, S.A. Bysiak 107, M. Cai 6,, H. Caines137,, A. Caliva 97,, E. Calvo Villar101,, J.M.M. Camacho 109,, P. Camerini 23,, F.D.M. Canedo 110,, S.L. Cantway 137,, M. Carabas 113,, A.A. Carballo32,, F. Carnesecchi 32,, R. Caron 127,, L.A.D. Carvalho 110,, J. Castillo Castellanos129,, F. Catalano32,24,, C. Ceballos Sanchez 141,, I. Chakaberia 74,, P. Chakraborty 47,, S. Chandra134,, S. Chapeland32,, M. Chartier 118,, S. Chattopadhyay134,, S. Chattopadhyay 99,, T. Cheng 97,6,, C. Cheshkov127,, B. Cheynis 127,, V. Chibante Barroso32,, D.D. Chinellato 111,, E.S. Chizzali95, ,II, J. Cho58,, S. Cho 58,, P. Chochula 32,, P. Christakoglou 84,, C.H. Christensen83,, P. Christiansen 75,, T. Chujo124,, M. Ciacco 29,, C. Cicalo 52,, F. Cindolo 51,, M.R. Ciupek 97, G. Clai 51,III, F. Colamaria 50,, J.S. Colburn100, D. Colella96,31,, M. Colocci 25,, M. Concas 56, ,IV, G. Conesa Balbastre 73,, Z. Conesa del Valle130,, G. Contin 23,, J.G. Contreras 35,, M.L. Coquet 129,, T.M. Cormier 87,I, P. Cortese 132,56,, M.R. Cosentino112,, F. Costa 32,, S. Costanza 21,55,, C. Cot 130,, J. Crkovská 94,, P. Crochet126,, R. Cruz-Torres 74,, P. Cui 6,, A. Dainese54,, M.C. Danisch 94,, A. Danu 63,, P. Das80,, P. Das 4,, Physics Letters B 849 (2024) 138412 17 ALICE Collaboration S. Das4,, A.R. Dash 125,, S. Dash 47,, A. De Caro 28,, G. de Cataldo 50,, J. de Cuveland38, A. De Falco22,, D. De Gruttola28,, N. De Marco 56,, C. De Martin 23,, S. De Pasquale 28,, R. Deb 133,, S. Deb48,, R.J. Debski2,, K.R. Deja 135, R. Del Grande 95,, L. Dello Stritto28,, W. Deng 6,, P. Dhankher 18,, D. Di Bari 31,, A. Di Mauro 32,, B. Diab129,, R.A. Diaz 141,7,, T. Dietel 114,, Y. Ding 6,, R. Divià32,, D.U. Dixit18,, Ø. Djuvsland 20, U. Dmitrieva 140,, A. Dobrin 63,, B. Dönigus 64,, J.M. Dubinski135,, A. Dubla97,, S. Dudi 90,, P. Dupieux 126,, M. Durkac 106, N. Dzalaiova 12, T.M. Eder 125,, R.J. Ehlers 74,, F. Eisenhut 64,, D. Elia50,, B. Erazmus 103,, F. Ercolessi25,, F. Erhardt 89,, M.R. Ersdal 20, B. Espagnon130,, G. Eulisse 32,, D. Evans 100,, S. Evdokimov 140,, L. Fabbietti95,, M. Faggin 27,, J. Faivre73,, F. Fan 6,, W. Fan 74,, A. Fantoni 49,, M. Fasel 87,, P. Fecchio29, A. Feliciello56,, G. Feofilov140,, A. Fernández Téllez 44,, L. Ferrandi 110,, M.B. Ferrer 32,, A. Ferrero 129,, C. Ferrero 56,, A. Ferretti 24,, V.J.G. Feuillard 94,, V. Filova35,, D. Finogeev 140,, F.M. Fionda 52,, F. Flor 115,, A.N. Flores108,, S. Foertsch 68,, I. Fokin 94,, S. Fokin 140,, E. Fragiacomo 57,, E. Frajna46,, U. Fuchs 32,, N. Funicello28,, C. Furget 73,, A. Furs140,, T. Fusayasu 98,, J.J. Gaardhøje 83,, M. Gagliardi 24,, A.M. Gago101,, C.D. Galvan 109,, D.R. Gangadharan 115,, P. Ganoti 78,, C. Garabatos97,, T. García Chávez44,, E. Garcia-Solis 9,, C. Gargiulo 32,, K. Garner 125, P. Gasik 97,, A. Gautam 117,, M.B. Gay Ducati66,, M. Germain 103,, A. Ghimouz 124, C. Ghosh 134, M. Giacalone51,25,, P. Giubellino 97,56,, P. Giubilato27,, A.M.C. Glaenzer 129,, P. Glässel 94,, E. Glimos 121,, D.J.Q. Goh76, V. Gonzalez 136,, M. Gorgon2,, S. Gotovac 33, V. Grabski 67,, L.K. Graczykowski 135,, E. Grecka 86,, A. Grelli 59,, C. Grigoras32,, V. Grigoriev140,, S. Grigoryan 141,1,, F. Grosa 32,, J.F. Grosse-Oetringhaus 32,, R. Grosso97,, D. Grund 35,, G.G. Guardiano 111,, R. Guernane 73,, M. Guilbaud 103,, K. Gulbrandsen 83,, T. Gündem 64,, T. Gunji123,, W. Guo6,, A. Gupta 91,, R. Gupta 91,, R. Gupta 48,, K. Gwizdziel 135,, L. Gyulai46,, R. Haake 137,, M.K. Habib 97, C. Hadjidakis 130,, F.U. Haider 91,, H. Hamagaki 76,, A. Hamdi74,, M. Hamid 6, Y. Han 138,, R. Hannigan 108,, J. Hansen75,, M.R. Haque 135,, J.W. Harris137,, A. Harton 9,, H. Hassan 87,, D. Hatzifotiadou 51,, P. Hauer42,, L.B. Havener 137,, S.T. Heckel 95,, E. Hellbär97,, H. Helstrup 34,, M. Hemmer 64,, T. Herman 35,, G. Herrera Corral 8,, F. Herrmann 125, S. Herrmann 127,, K.F. Hetland 34,, B. Heybeck 64,, H. Hillemanns32,, B. Hippolyte 128,, F.W. Hoffmann70,, B. Hofman 59,, B. Hohlweger 84,, G.H. Hong 138,, M. Horst 95,, A. Horzyk2,, Y. Hou6,, P. Hristov 32,, C. Hughes 121,, P. Huhn 64, L.M. Huhta 116,, T.J. Humanic 88,, A. Hutson115,, D. Hutter38,, R. Ilkaev 140, H. Ilyas13,, M. Inaba 124,, G.M. Innocenti 32,, M. Ippolitov140,, A. Isakov 86,, T. Isidori117,, M.S. Islam 99,, M. Ivanov 12, M. Ivanov 97,, V. Ivanov 140,, M. Jablonski2,, B. Jacak 74,, N. Jacazio32,, P.M. Jacobs 74,, S. Jadlovska 106, J. Jadlovsky 106, S. Jaelani 82,, C. Jahnke 111,, M.J. Jakubowska135,, M.A. Janik 135,, T. Janson 70, M. Jercic 89, S. Jia 10,, A.A.P. Jimenez 65,, F. Jonas87,125,, J.M. Jowett 32,97,, J. Jung 64,, M. Jung 64,, A. Junique 32,, A. Jusko100,, M.J. Kabus32,135,, J. Kaewjai 105, P. Kalinak60,, A.S. Kalteyer 97,, A. Kalweit32,, V. Kaplin 140,, A. Karasu Uysal72,, D. Karatovic 89,, O. Karavichev 140,, T. Karavicheva 140,, P. Karczmarczyk 135,, E. Karpechev140,, U. Kebschull 70,, R. Keidel 139,, D.L.D. Keijdener 59, M. Keil 32,, B. Ketzer 42,, S.S. Khade48,, A.M. Khan 119,6,, S. Khan 15,, A. Khanzadeev 140,, Y. Kharlov 140,, A. Khatun 117,15,, A. Khuntia107,, M.B. Kidson 114, B. Kileng 34,, B. Kim 104,, C. Kim 16,, D.J. Kim 116,, E.J. Kim 69,, J. Kim138,, J.S. Kim 40,, J. Kim 69,, M. Kim 18,, S. Kim 17,, T. Kim 138,, K. Kimura 92,, S. Kirsch 64,, I. Kisel38,, S. Kiselev 140,, A. Kisiel 135,, J.P. Kitowski 2,, J.L. Klay 5,, J. Klein32,, S. Klein 74,, C. Klein-Bösing125,, M. Kleiner 64,, T. Klemenz95,, A. Kluge 32,, A.G. Knospe 115,, C. Kobdaj 105,, T. Kollegger 97, A. Kondratyev 141,, N. Kondratyeva 140,, E. Kondratyuk 140,, J. Konig 64,, Physics Letters B 849 (2024) 138412 18 ALICE Collaboration S.A. Konigstorfer95,, P.J. Konopka32,, G. Kornakov 135,, M. Korwieser 95,, S.D. Koryciak 2,, A. Kotliarov86,, V. Kovalenko 140,, M. Kowalski 107,, V. Kozhuharov 36,, I. Králik 60,, A. Kravˇ cáková37,, L. Krcal32,38,, M. Krivda 100,60,, F. Krizek 86,, K. Krizkova Gajdosova32,, M. Kroesen 94,, M. Krüger 64,, D.M. Krupova35,, E. Kryshen 140,, V. Kuˇ cera 58,, C. Kuhn128,, P.G. Kuijer 84,, T. Kumaoka 124, D. Kumar134, L. Kumar 90,, N. Kumar90, S. Kumar 31,, S. Kundu 32,, P. Kurashvili 79,, A. Kurepin 140,, A.B. Kurepin 140,, A. Kuryakin 140,, S. Kushpil86,, J. Kvapil 100,, M.J. Kweon 58,, J.Y. Kwon 58,, Y. Kwon138,, S.L. La Pointe 38,, P. La Rocca 26,, A. Lakrathok105, M. Lamanna 32,, R. Langoy120,, P. Larionov32,, E. Laudi 32,, L. Lautner 32,95,, R. Lavicka102,, T. Lazareva 140,, R. Lea133,55,, H. Lee104,, I. Legrand 45,, G. Legras 125,, J. Lehrbach 38,, T.M. Lelek 2, R.C. Lemmon 85,, I. León Monzón109,, M.M. Lesch 95,, E.D. Lesser 18,, P. Lévai 46,, X. Li10, X.L. Li 6, J. Lien120,, R. Lietava100,, I. Likmeta115,, B. Lim 24,, S.H. Lim 16,, V. Lindenstruth 38,, A. Lindner 45, C. Lippmann 97,, A. Liu 18,, D.H. Liu6,, J. Liu 118,, I.M. Lofnes 20,, C. Loizides 87,, S. Lokos 107,, J. Lömker 59,, P. Loncar33,, J.A. Lopez94,, X. Lopez 126,, E. López Torres 7,, P. Lu 97,119,, J.R. Luhder 125,, M. Lunardon27,, G. Luparello57,, Y.G. Ma 39,, A. Maevskaya 140, M. Mager 32,, A. Maire128,, M.V. Makariev 36,, M. Malaev140,, G. Malfattore25,, N.M. Malik 91,, Q.W. Malik 19, S.K. Malik 91,, L. Malinina 141, ,I,VII, D. Mal’Kevich140,, D. Mallick 80,, N. Mallick 48,, G. Mandaglio 30,53,, S.K. Mandal79,, V. Manko 140,, F. Manso126,, V. Manzari 50,, Y. Mao6,, G.V. Margagliotti 23,, A. Margotti 51,, A. Marín97,, C. Markert 108,, P. Martinengo 32,, J.L. Martinez115, M.I. Martínez 44,, G. Martínez García 103,, M.P.P. Martins110,, S. Masciocchi 97,, M. Masera 24,, A. Masoni 52,, L. Massacrier130,, A. Mastroserio131,50,, O. Matonoha 75,, P.F.T. Matuoka 110, A. Matyja 107,, C. Mayer 107,, A.L. Mazuecos32,, F. Mazzaschi 24,, M. Mazzilli 32,, J.E. Mdhluli 122,, A.F. Mechler 64, Y. Melikyan 43,140,, A. Menchaca-Rocha67,, E. Meninno 102,, A.S. Menon 115,, M. Meres 12,, S. Mhlanga 114,68, Y. Miake 124, L. Micheletti56,, L.C. Migliorin 127, D.L. Mihaylov95,, K. Mikhaylov 141,140,, A.N. Mishra 46,, D. Mi´ skowiec97,, A. Modak 4,, A.P. Mohanty 59,, B. Mohanty 80, M. Mohisin Khan 15, ,V, M.A. Molander43,, Z. Moravcova 83,, C. Mordasini 95,, D.A. Moreira De Godoy 125,, I. Morozov 140,, A. Morsch32,, T. Mrnjavac 32,, V. Muccifora 49,, S. Muhuri 134,, J.D. Mulligan 74,, A. Mulliri22,, M.G. Munhoz110,, R.H. Munzer 64,, H. Murakami 123,, S. Murray 114,, L. Musa 32,, J. Musinsky 60,, J.W. Myrcha135,, B. Naik 122,, A.I. Nambrath 18,, B.K. Nandi 47,, R. Nania 51,, E. Nappi 50,, A.F. Nassirpour17,75,, A. Nath 94,, C. Nattrass 121,, M.N. Naydenov 36,, A. Neagu 19, A. Negru 113, L. Nellen65,, S.V. Nesbo 34, G. Neskovic38,, D. Nesterov 140,, B.S. Nielsen 83,, E.G. Nielsen 83,, S. Nikolaev140,, S. Nikulin 140,, V. Nikulin 140,, F. Noferini 51,, S. Noh11,, P. Nomokonov 141,, J. Norman118,, N. Novitzky 124,, P. Nowakowski 135,, A. Nyanin 140,, J. Nystrand20,, M. Ogino 76,, A. Ohlson75,, V.A. Okorokov 140,, J. Oleniacz 135,, A.C. Oliveira Da Silva121,, M.H. Oliver 137,, A. Onnerstad116,, C. Oppedisano 56,, A. Ortiz Velasquez65,, J. Otwinowski 107,, M. Oya 92, K. Oyama76,, Y. Pachmayer94,, S. Padhan 47,, D. Pagano 133,55,, G. Pai´ c65,, S. Paisano-Guzmán44,, A. Palasciano 50,, S. Panebianco129,, H. Park 124,, H. Park 104,, J. Park 58,, J.E. Parkkila 32,, R.N. Patra91, B. Paul 22,, H. Pei6,, T. Peitzmann 59,, X. Peng 6,, M. Pennisi 24,, L.G. Pereira 66,, D. Peresunko 140,, G.M. Perez 7,, S. Perrin 129,, Y. Pestov140, V. Petrᡠcek 35,, V. Petrov140,, M. Petrovici 45,, R.P. Pezzi 103,66,, S. Piano57,, M. Pikna 12,, P. Pillot 103,, O. Pinazza 51,32,, L. Pinsky115, C. Pinto95,, S. Pisano49,, M. Płosko´ n74,, M. Planinic89, F. Pliquett64, M.G. Poghosyan 87,, B. Polichtchouk140,, S. Politano29,, N. Poljak 89,, A. Pop 45,, S. Porteboeuf-Houssais126,, V. Pozdniakov 141,, I.Y. Pozos 44,, K.K. Pradhan48,, S.K. Prasad 4,, S. Prasad 48,, R. Preghenella 51,, F. Prino 56,, C.A. Pruneau 136,, Physics Letters B 849 (2024) 138412 19 ALICE Collaboration I. Pshenichnov140,, M. Puccio 32,, S. Pucillo 24,, Z. Pugelova106, S. Qiu84,, L. Quaglia 24,, R.E. Quishpe115, S. Ragoni 14,, A. Rakotozafindrabe129,, L. Ramello 132,56,, F. Rami 128,, T.A. Rancien 73, M. Rasa26,, S.S. Räsänen 43,, R. Rath 51,, M.P. Rauch 20,, I. Ravasenga 84,, K.F. Read 87,121,, C. Reckziegel 112,, A.R. Redelbach 38,, K. Redlich79, ,VI, C.A. Reetz 97,, H.D. Regules-Medel 44, A. Rehman20, F. Reidt 32,, H.A. Reme-Ness 34,, Z. Rescakova37, K. Reygers 94,, A. Riabov 140,, V. Riabov140,, R. Ricci 28,, M. Richter 19,, A.A. Riedel 95,, W. Riegler 32,, C. Ristea63,, M. Rodríguez Cahuantzi 44,, S.A. Rodríguez Ramírez 44,, K. Røed19,, R. Rogalev 140,, E. Rogochaya 141,, T.S. Rogoschinski64,, D. Rohr 32,, D. Röhrich 20,, P.F. Rojas 44, S. Rojas Torres 35,, P.S. Rokita 135,, G. Romanenko141,, F. Ronchetti 49,, A. Rosano 30,53,, E.D. Rosas 65, K. Roslon 135,, A. Rossi 54,, A. Roy48,, S. Roy 47,, N. Rubini 25,, D. Ruggiano 135,, R. Rui23,, P.G. Russek 2,, R. Russo 84,, A. Rustamov81,, E. Ryabinkin 140,, Y. Ryabov 140,, A. Rybicki107,, H. Rytkonen 116,, W. Rzesa 135,, O.A.M. Saarimaki43,, R. Sadek 103,, S. Sadhu31,, S. Sadovsky 140,, J. Saetre 20,, K. Šafaˇ rík35,, P. Saha41, S.K. Saha4,, S. Saha 80,, B. Sahoo 47,, B. Sahoo 48,, R. Sahoo48,, S. Sahoo 61, D. Sahu 48,, P.K. Sahu61,, J. Saini 134,, K. Sajdakova 37, S. Sakai 124,, M.P. Salvan 97,, S. Sambyal 91,, I. Sanna32,95,, T.B. Saramela110, D. Sarkar 136,, N. Sarkar 134, P. Sarma 41,, V. Sarritzu 22,, V.M. Sarti 95,, M.H.P. Sas137,, J. Schambach 87,, H.S. Scheid 64,, C. Schiaua45,, R. Schicker 94,, A. Schmah94, C. Schmidt97,, H.R. Schmidt 93, M.O. Schmidt32,, M. Schmidt 93, N.V. Schmidt 87,, A.R. Schmier 121,, R. Schotter128,, A. Schröter 38,, J. Schukraft 32,, K. Schwarz 97, K. Schweda97,, G. Scioli 25,, E. Scomparin56,, J.E. Seger 14,, Y. Sekiguchi 123, D. Sekihata 123,, I. Selyuzhenkov 97,140,, S. Senyukov128,, J.J. Seo58,, D. Serebryakov 140,, L. Šerkšnyt ˙ e95,, A. Sevcenco63,, T.J. Shaba 68,, A. Shabetai103,, R. Shahoyan 32, A. Shangaraev 140,, A. Sharma90, B. Sharma91,, D. Sharma 47,, H. Sharma54,107,, M. Sharma 91,, S. Sharma 76,, S. Sharma91,, U. Sharma 91,, A. Shatat 130,, O. Sheibani 115, K. Shigaki 92,, M. Shimomura 77, J. Shin 11, S. Shirinkin 140,, Q. Shou 39,, Y. Sibiriak140,, S. Siddhanta52,, T. Siemiarczuk 79,, T.F. Silva 110,, D. Silvermyr 75,, T. Simantathammakul 105, R. Simeonov36,, B. Singh 91, B. Singh 95,, R. Singh80,, R. Singh91,, R. Singh 48,, S. Singh 15,, V.K. Singh134,, V. Singhal 134,, T. Sinha 99,, B. Sitar 12,, M. Sitta 132,56,, T.B. Skaali19, G. Skorodumovs94,, M. Slupecki 43,, N. Smirnov 137,, R.J.M. Snellings 59,, E.H. Solheim19,, J. Song115,, A. Songmoolnak 105, C. Sonnabend 32,97,, F. Soramel 27,, A.B. Soto-hernandez 88,, R. Spijkers 84,, I. Sputowska107,, J. Staa 75,, J. Stachel 94,, I. Stan 63,, P.J. Steffanic121,, S.F. Stiefelmaier 94,, D. Stocco103,, I. Storehaug 19,, P. Stratmann 125,, S. Strazzi 25,, C.P. Stylianidis84, A.A.P. Suaide110,, C. Suire 130,, M. Sukhanov 140,, M. Suljic 32,, R. Sultanov140,, V. Sumberia 91,, S. Sumowidagdo 82,, S. Swain 61, I. Szarka 12,, M. Szymkowski 135,, S.F. Taghavi 95,, G. Taillepied97,, J. Takahashi111,, G.J. Tambave 80,, S. Tang 126,6,, Z. Tang 119,, J.D. Tapia Takaki 117,, N. Tapus113, L.A. Tarasovicova125,, M.G. Tarzila 45,, G.F. Tassielli 31,, A. Tauro 32,, G. Tejeda Muñoz 44,, A. Telesca32,, L. Terlizzi 24,, C. Terrevoli 115,, S. Thakur 4,, D. Thomas108,, A. Tikhonov 140,, A.R. Timmins115,, M. Tkacik 106, T. Tkacik106,, A. Toia 64,, R. Tokumoto 92, N. Topilskaya 140,, M. Toppi49,, F. Torales-Acosta 18, T. Tork 130,, A.G. Torres Ramos31,, A. Trifiró 30,53,, A.S. Triolo 32,30,53,, S. Tripathy51,, T. Tripathy 47,, S. Trogolo 32,, V. Trubnikov 3,, W.H. Trzaska 116,, T.P. Trzcinski 135,, A. Tumkin140,, R. Turrisi 54,, T.S. Tveter 19,, K. Ullaland20,, B. Ulukutlu 95,, A. Uras127,, M. Urioni55,133,, G.L. Usai22,, M. Vala 37, N. Valle21,, L.V.R. van Doremalen 59, M. van Leeuwen 84,, C.A. van Veen94,, R.J.G. van Weelden 84,, P. Vande Vyvre 32,, D. Varga 46,, Z. Varga 46,, M. Vasileiou78,, A. Vasiliev 140,, O. Vázquez Doce 49,, O. Vazquez Rueda 115,, V. Vechernin 140,, Physics Letters B 849 (2024) 138412 20 ALICE Collaboration E. Vercellin24,, S. Vergara Limón 44, L. Vermunt97,, R. Vértesi 46,, M. Verweij 59,, L. Vickovic33, Z. Vilakazi122, O. Villalobos Baillie 100,, A. Villani23,, G. Vino 50,, A. Vinogradov 140,, T. Virgili28,, M.M.O. Virta116,, V. Vislavicius75, A. Vodopyanov141,, B. Volkel 32,, M.A. Völkl 94,, K. Voloshin140, S.A. Voloshin136,, G. Volpe 31,, B. von Haller 32,, I. Vorobyev95,, N. Vozniuk 140,, J. Vrláková 37,, C. Wang39,, D. Wang 39, Y. Wang39,, A. Wegrzynek 32,, F.T. Weiglhofer 38, S.C. Wenzel 32,, J.P. Wessels125,, J. Wiechula 64,, J. Wikne 19,, G. Wilk 79,, J. Wilkinson 97,, G.A. Willems125,, B. Windelband94,, M. Winn129,, J.R. Wright 108,, W. Wu 39, Y. Wu 119,, R. Xu 6,, A. Yadav 42,, A.K. Yadav134,, S. Yalcin 72,, Y. Yamaguchi 92,, S. Yang20, S. Yano 92,, Z. Yin6,, I.-K. Yoo 16,, J.H. Yoon58,, S. Yuan 20, A. Yuncu 94,, V. Zaccolo 23,, C. Zampolli32,, F. Zanone 94,, N. Zardoshti 32,, A. Zarochentsev140,, P. Závada62,, N. Zaviyalov 140, M. Zhalov 140,, B. Zhang 6,, L. Zhang 39,, S. Zhang39,, X. Zhang 6,, Y. Zhang 119, Z. Zhang 6,, M. Zhao 10,, V. Zherebchevskii 140,, Y. Zhi 10, D. Zhou6,, Y. Zhou 83,, J. Zhu 97,6,, Y. Zhu 6, S.C. Zugravel 56,, N. Zurlo133,55, 1A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia 2AGH University of Krakow, Cracow, Poland 3Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, Kiev, Ukraine 4Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS), Kolkata, India 5California Polytechnic State University, San Luis Obispo, CA, United States 6Central China Normal University, Wuhan, China 7Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana, Cuba 8Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City and Mérida, Mexico 9Chicago State University, Chicago, IL, United States 10 China Institute of Atomic Energy, Beijing, China 11 Chungbuk National University, Cheongju, Republic of Korea 12 Comenius University Bratislava, Faculty of Mathematics, Physics and Informatics, Bratislava, Slovak Republic 13 COMSATS University Islamabad, Islamabad, Pakistan 14 Creighton University, Omaha, NE, United States 15 Department of Physics, Aligarh Muslim University, Aligarh, India 16 Department of Physics, Pusan National University, Pusan, Republic of Korea 17 Department of Physics, Sejong University, Seoul, Republic of Korea 18 Department of Physics, University of California, Berkeley, CA, United States 19 Department of Physics, University of Oslo, Oslo, Norway 20 Department of Physics and Technology, University of Bergen, Bergen, Norway 21 Dipartimento di Fisica, Università di Pavia, Pavia, Italy 22 Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy 23 Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy 24 Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy 25 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Bologna, Italy 26 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Catania, Italy 27 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Padova, Italy 28 Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo Collegato INFN, Salerno, Italy 29 Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy 30 Dipartimento di Scienze MIFT, Università di Messina, Messina, Italy 31 Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari, Italy 32 European Organization for Nuclear Research (CERN), Geneva, Switzerland 33 Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia 34 Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway 35 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic 36 Faculty of Physics, Sofia University, Sofia, Bulgaria 37 Faculty of Science, P.J. Šafárik University, Košice, Slovak Republic 38 Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 39 Fudan University, Shanghai, China 40 Gangneung-Wonju National University, Gangneung, Republic of Korea 41 Gauhati University, Department of Physics, Guwahati, India 42 Helmholtz-Institut für Strahlen- und Kernphysik, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany 43 Helsinki Institute of Physics (HIP), Helsinki, Finland 44 High Energy Physics Group, Universidad Autónoma de Puebla, Puebla, Mexico 45 Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania 46 HUN-REN Wigner Research Centre for Physics, Budapest, Hungary 47 Indian Institute of Technology Bombay (IIT), Mumbai, India 48 Indian Institute of Technology Indore, Indore, India 49 INFN, Laboratori Nazionali di Frascati, Frascati, Italy 50 INFN, Sezione di Bari, Bari, Italy 51 INFN, Sezione di Bologna, Bologna, Italy 52 INFN, Sezione di Cagliari, Cagliari, Italy 53 INFN, Sezione di Catania, Catania, Italy 54 INFN, Sezione di Padova, Padova, Italy 55 INFN, Sezione di Pavia, Pavia, Italy 56 INFN, Sezione di Torino, Turin, Italy 57 INFN, Sezione di Trieste, Trieste, Italy Physics Letters B 849 (2024) 138412 21 ALICE Collaboration 58 Inha University, Incheon, Republic of Korea 59 Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University/Nikhef, Utrecht, Netherlands 60 Institute of Experimental Physics, Slovak Academy of Sciences, Košice, Slovak Republic 61 Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India 62 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 63 Institute of Space Science (ISS), Bucharest, Romania 64 Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 65 Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico City, Mexico 66 Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 67 Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, Mexico 68 iThemba LABS, National Research Foundation, Somerset West, South Africa 69 Jeonbuk National University, Jeonju, Republic of Korea 70 Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik, Fachbereich Informatik und Mathematik, Frankfurt, Germany 71 Korea Institute of Science and Technology Information, Daejeon, Republic of Korea 72 KTO Karatay University, Konya, Turkey 73 Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS-IN2P3, Grenoble, France 74 Lawrence Berkeley National Laboratory, Berkeley, CA, United States 75 Lund University Department of Physics, Division of Particle Physics, Lund, Sweden 76 Nagasaki Institute of Applied Science, Nagasaki, Japan 77 Nara Women’s University (NWU), Nara, Japan 78 National and Kapodistrian University of Athens, School of Science, Department of Physics, Athens, Greece 79 National Centre for Nuclear Research, Warsaw, Poland 80 National Institute of Science Education and Research, Homi Bhabha National Institute, Jatni, India 81 National Nuclear Research Center, Baku, Azerbaijan 82 National Research and Innovation Agency -BRIN, Jakarta, Indonesia 83 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 84 Nikhef, National institute for subatomic physics, Amsterdam, Netherlands 85 Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom 86 Nuclear Physics Institute of the Czech Academy of Sciences, Husinec-ˇ Rež, Czech Republic 87 Oak Ridge National Laboratory, Oak Ridge, TN, United States 88 Ohio State University, Columbus, OH, United States 89 Physics department, Faculty of science, University of Zagreb, Zagreb, Croatia 90 Physics Department, Panjab University, Chandigarh, India 91 Physics Department, University of Jammu, Jammu, India 92 Physics Program and International Institute for Sustainability with Knotted Chiral Meta Matter (SKCM2), Hiroshima University, Hiroshima, Japan 93 Physikalisches Institut, Eberhard-Karls-Universität Tübingen, Tübingen, Germany 94 Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 95 Physik Department, Technische Universität München, Munich, Germany 96 Politecnico di Bari and Sezione INFN, Bari, Italy 97 Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany 98 Saga University, Saga, Japan 99 Saha Institute of Nuclear Physics, Homi Bhabha National Institute, Kolkata, India 100 School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom 101 Sección Física, Departamento de Ciencias, Pontificia Universidad Católica del Perú, Lima, Peru 102 Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria 103 SUBATECH, IMT Atlantique, Nantes Université, CNRS-IN2P3, Nantes, France 104 Sungkyunkwan University, Suwon City, Republic of Korea 105 Suranaree University of Technology, Nakhon Ratchasima, Thailand 106 Technical University of Košice, Košice, Slovak Republic 107 The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Cracow, Poland 108 The University of Texas at Austin, Austin, TX, United States 109 Universidad Autónoma de Sinaloa, Culiacán, Mexico 110 Universidade de São Paulo (USP), São Paulo, Brazil 111 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil 112 Universidade Federal do ABC, Santo Andre, Brazil 113 Universitatea Nationala de Stiinta si Tehnologie Politehnica Bucuresti, Bucharest, Romania 114 University of Cape Town, Cape Town, South Africa 115 University of Houston, Houston, TX, United States 116 University of Jyväskylä, Jyväskylä, Finland 117 University of Kansas, Lawrence, KS, United States 118 University of Liverpool, Liverpool, United Kingdom 119 University of Science and Technology of China, Hefei, China 120 University of South-Eastern Norway, Kongsberg, Norway 121 University of Tennessee, Knoxville, TN, United States 122 University of the Witwatersrand, Johannesburg, South Africa 123 University of Tokyo, Tokyo, Japan 124 University of Tsukuba, Tsukuba, Japan 125 Universität Münster, Institut für Kernphysik, Münster, Germany 126 Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France 127 Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de Lyon, Lyon, France 128 Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France, Strasbourg, France 129 Université Paris-Saclay, Centre d’Etudes de Saclay (CEA), IRFU, Départment de Physique Nucléaire (DPhN), Saclay, France 130 Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France 131 Università degli Studi di Foggia, Foggia, Italy 132 Università del Piemonte Orientale, Vercelli, Italy 133 Università di Brescia, Brescia, Italy 134 Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata, India 135 Warsaw University of Technology, Warsaw, Poland 136 Wayne State University, Detroit, MI, United States 137 Yale University, New Haven, CT, United States Physics Letters B 849 (2024) 138412 22 ALICE Collaboration 138 Yonsei University, Seoul, Republic of Korea 139 Zentrum für Technologie und Transfer (ZTT), Worms, Germany 140 Affiliated with an institute covered by a cooperation agreement with CERN 141 Affiliated with an international laboratory covered by a cooperation agreement with CERN IDeceased. II Also at: Max-Planck-Institut fur Physik, Munich, Germany. III Also at: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Bologna, Italy. IV Also at: Dipartimento DET del Politecnico di Torino, Turin, Italy. VAlso at: Department of Applied Physics, Aligarh Muslim University, Aligarh, India. VI Also at: Institute of Theoretical Physics, University of Wroclaw, Poland. VII Also at: An institution covered by a cooperation agreement with CERN.