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

Shreyasi Acharya, Dagmar Adamova, Alexander Adler, Gianluca Aglieri

Rinella, Michelangelo Agnello, Neelima Agrawal, Zubayer Ahammed, Shakeel

Ahmad, Sang Un Ahn, Ishaan Ahuja, et al.

► To cite this version:

Shreyasi Acharya, Dagmar Adamova, Alexander Adler, Gianluca Aglieri, et al.. Measurement of the radius dependence of charged-particle jet suppression in Pb-Pb collisions at sNN=5.02TeV. Phys.Lett.B, 2024, 849, pp.138412. 10.1016/j.physletb.2023.138412. hal-04427908

HAL Id: hal-04427908

https://hal.science/hal-04427908v1

Submitted on 6 Jun 2025

**HAL** is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

Letter

# Measurement of the radius dependence of charged-particle jet suppression in Pb–Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

ALICE Collaboration *

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

Editor: M. Doser

Dataset link: https://www.hepdata.net/record/ins2637686

## ABSTRACT

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 $\sqrt{s_{NN}}$ = 5.02 TeV. Charged-particle jets are reconstructed using the anti-$k_T$ algorithm 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. A novel 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 $p_T$ ≥ 40 GeV/c at $R = 0.6$ in 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. A mild 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 ($p_T$) 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-$p_T$ partons 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-$p_T$ hadrons

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 $p_T$. Jet suppression is quantified using the nuclear modification factor,

$$R_{AA} = \frac{1}{\langle T_{AA} \rangle} \frac{d^2 N}{d p_T d \eta} \frac{d^2 N}{d p_T d \eta}, \quad (1)$$

which is the ratio of the measured per-event inclusive jet yield in heavy-ion (AA) collisions and the inclusive cross sections in pp collisions scaled by the nuclear overlap in a given centrality class $T_{AA}$ [57]. The value of $R_{AA}$ 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 $p_T$ and 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 $R$ is 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 trans-

<footnote>* E-mail address: alic Publications@cern.ch.</footnote>
port properties of the QGP. While jet suppression has been measured over a large range in jet $p_T$ at both the LHC and RHIC [62–66], of particular interest are the low-$p_T$ and large-$R$ regions. A recent measurement by the CMS collaboration [67] studied jet suppression up to $R = 1.0$ for jets with high $p_T > 200$ GeV/c. Measurements of jet suppression as a function of $R$ were 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 ($R_{CP}$) at lower $p_T$, $40 < p_T < 200$ GeV/c, and found a dependence on $R$ where jets with larger $R$ up to $R = 0.5$ exhibit less suppression. Measuring the $R$-dependence of energy loss [58–61] at low $p_T$ will 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 $p_T$ at the LHC due to high-precision tracking in the Time Projection Chamber (TPC) [69] and Inner Tracking System (ITS) [70]. However, reconstructing the jet $p_T$ in 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 $p_T$ itself. Jet measurements in heavy-ion collisions require a procedure to account for this background which involves both a correction of the jet $p_T$ and a suppression of combinatorial (or fake) jets.

One common procedure applied for the jet $p_T$-smearing is to correct for the average background via a pedestal subtraction of the event-wise 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 $p_T$ requirement. 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 $p_T$ to 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 $p_T$ due to the improved jet $p_T$ resolution. 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 $p_T$, which may also provide the opportunity to measure jets in Pb-Pb collisions with lower jet $p_T$ and 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 charged-particle jet production at a center-of-mass energy per nucleon-nucleon collision of $\sqrt{s_{NN}} = 5.02$ 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 $R_{AA}$ 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 $p_T$ and larger $R$, measuring jets down to $p_T = 30$ GeV/c for $R = 0.4$ and to $p_T = 40$ GeV/c for $R = 0.6$.

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 B describes 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 $\sqrt{s_{NN}} = 5.02$ 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 $2.8 < \eta < 5.1$ (V0A) and $-3.7 < \eta < -1.7$ (V0C). The accepted events, reconstructed as described in Ref. [76], were required to have a reconstructed primary vertex within $\pm 10$ cm from the nominal interaction point along the beam axis and the obtained sample corresponds to an integrated luminosity of about $6.5 \ \mu b^{-1}$. 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 $\sqrt{s} = 5.02$ TeV was collected in 2017 during Run 2 of the LHC with an integrated luminosity of about $19 \ \text{nb}^{-1}$ [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 $\pm 10$ 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 0.5 T aligned with the beam axis. This system consists of the ITS, a high-precision six-layer cylindrical silicon detector with the innermost layer at 3.9 cm 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 $p_T > 0.15$ GeV/c and $|\eta| < 0.9$, 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 $p_T = 0.15$ GeV/c, rising to approximately 84% at track $p_T = 1$ GeV/c and remaining above 75% at higher track $p_T$ [64]. The tracking efficiency in 0–10% Pb-Pb collisions as compared to that in
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 $p_T$. 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 $p_T$ of 1 GeV/c and about 4% at 50 GeV/c. In heavy-ion collisions, the momentum resolution at high track $p_T$ is 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 $\sqrt{s} = 5.02$ TeV. This dataset is 10 times larger than the dataset used in Ref. [84], extending the charged-particle jet spectra for all considered $R$ values up to jet $p_T = 140$ GeV/c.

The values for the $\langle T_{AA} \rangle$ in the nuclear modification factor Eq. (1) were computed in a Glauber model [85] to be $23.26 \pm 0.168$, $3.917 \pm 0.0645$, and $0.4188 \pm 0.0106$ mb$^{-1}$ in central (0–10%), semi-central (30–50%), and peripheral (60–80%) collisions, respectively.

### 3. Jet reconstruction and unfolding

Charged-particle jets are reconstructed using the anti-$k_T$ algorithm 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 $0.6$ in 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 $|\eta_{jet}| < 0.9 - R$ to assure that the nominal jet cone is fully contained within the track acceptance of $|\eta| < 0.9$. A jet area cut of $A_{jet} > 0.56\pi R^2$ is applied to suppress contamination by non-physical jets [36,49,63]. Jets containing a track with $p_T > 100$ GeV/c are 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 $p_T$ resolution). 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 $p_T$.

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 $0.75 \times R$ are accepted. The hybrid and detector-level jets are additionally required to share particles responsible for at least 50% of the jet $p_T$. Then the detector-level jet is matched geometrically with a maximum distance of $0.75 \times R$ to a particle-level jet. These matched jets form a correspondence between the true and reconstructed-level (hybrid-level) jet $p_T$, 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 $p_T^{\text{true}}$ interval,

$$ \epsilon_{\text{rec}}(p_{T, \text{ch jet}}^{\text{true}}) = N_{\text{matched}}(p_{T, \text{ch jet}}^{\text{true}})/N_{\text{generated}}(p_{T, \text{ch jet}}^{\text{true}}), \quad (2) $$

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). A value 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. A requirement of a minimum kinematic efficiency of 60% is also imposed on the considered jet $p_T$ intervals, while lower efficiency regions are rejected.

### 4. Machine learning-based background correction

To expand the jet $p_T$ and $R$ reach of the measurement, a novel estimator based on machine learning is used to correct the $p_T$-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 $p_T$ of the jet. While the background is dominated by low-$p_T$ particles, the particles in the jet signal are distributed towards higher $p_T$ constituents [71,92]. However, the relationship between the relevant input features of the jet candidate and the true jet $p_T$ is 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 $p_T$ value 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. 1 shows the distribution of the residual difference ($\delta p_T$) between background-corrected $p_T$ and target detector-level probe $p_T$, which measures how precisely the background is approximated. The plot compares $\delta p_T$ distributions of the area-based and ML-based background estimators for $R = 0.4$. The right panel of Fig. 1 provides the standard deviation of the residual distributions of both background estimators versus $R$ for different centralities. The ML-based estimator has an approximately two times narrower $\delta p_T$ 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.
<figure><img src="image_4.png" /><figcaption>Fig. 1. Residual $p_T$-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.</figcaption></figure>

### 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 $p_T$ of 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 physics-motivated 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, $z = \frac{p_T,track}{p_T,jet}$. 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 $p_{loss}$, the probability of branching off a particle; $f_{loss}$, the fraction of the jet constituent $p_T$ to radiate; and $\Delta R$, the maximum angle of the emission relative to the jet constituent.

For each jet constituent, a particle is radiated with probability $p_{loss}$, carrying $p_T = f_{loss}p_T^{const.}$, at an angle randomly sampled from a uniform distribution between 0 and $\Delta R$. 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 $\Delta R$ to 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 $\Delta R$ to 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 $p_T$ is 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 guide the values of the tunable parameters used in the phenomenological modifications. Specifically, the $p_{loss}$ 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 $R=0.4$ inclusive jets above 100 GeV/c [96]. Each modification attributes the $p_{loss}$ 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
<figure><img src="image_5.png" /><figcaption>Fig. 2. Left: Comparison of toy model modifications for R = 0.4 jets using the R<sub>mod</sub> as defined in Eq. (3) Right: The ratio of the modified to unmodified fragmentation functions at low jet p<sub>T</sub> (40 < p<sub>T>true</sub> < 100 GeV/c, lower right panel) and high jet p<sub>T</sub> (100 < p<sub>T>true</sub> < 200 GeV/c, upper right panel) for 0–10% central Pb–Pb collisions. In the fractional collinear and fractional large angle case, f<sub>loss</sub> = 25% and p<sub>loss</sub> = 100%. In the medium response case, f<sub>loss</sub> = 10% and p<sub>loss</sub> = 50%. The ratio of the fragmentation functions measured in Pb–Pb and pp collisions are shown for jets with R = 0.4 and p<sub>T</sub> > 100 GeV/c [53] (ATLAS), and for jets with R = 0.3 and p<sub>T</sub> > 30 GeV/c recoiling from a photon with E<sub>T</sub> > 60 GeV/c [94] (CMS).</figcaption></figure>

fact that the p<sub>loss</sub> 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 p<sub>loss</sub> for the Fractional Large Angle model, and the excess inside of the jet cone was used to fix the value of p<sub>loss</sub> for the Fractional Collinear and Medium Response models. The modifications were then compared to existing fragmentation measurements, as discussed below. The values of f<sub>loss</sub> were set to 25% and 10%, which provide a charged hadron R<sub>AA</sub> 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, R<sub>mod</sub>,

$$R_{\text{mod}} = \frac{Y_{\text{modified}}}{Y_{\text{unmodified}}}, \qquad (3)$$

shown for R = 0.4 jets as a function of p<sub>T</sub> in the left panel of Fig. 2. The medium response adds energy to the jet cone, resulting in R<sub>mod</sub> > 1. For the fractional collinear model, the jet does not lose energy most of the time, which results in R<sub>mod</sub> ≈ 1. In the case of fractional large-angle radiation, the jet will lose energy, resulting in R<sub>mod</sub> < 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 z is 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 = 0.4 inclusive jets with p<sub>T</sub> > 100 GeV/c from ATLAS [53] and R = 0.3 photon-tagged jets with p<sub>T</sub> > 30 GeV/c from 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 = 0.4 jets in the 0–10% centrality class with 100 < p<sub>T</sub> < 200 GeV/c. The Medium Response and Fractional Collinear

models describe the measured low-z enhancement of soft particles. The quark-only case describes the intermediate-z suppression and high-z enhancement. Additionally, the ratio between modified and unmodified jet fragmentation functions is shown in the bottom right panel of Fig. 2 for R = 0.4 jets with 40 < p<sub>T</sub> < 100 GeV/c in 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 = 0.4 jets at high p<sub>T</sub>, 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
<caption>**Table 1**</caption>

Relative systematic uncertainties (%) for jet spectra for 0–10% central Pb–Pb collisions and all resolution parameters. The maximum uncertainties for low $p_T$ ($p_{T,jet} < 50$ GeV/c) and high $p_T$ ($p_{T,jet} > 50$ 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.

<table><caption>**Table 2**</caption><thead><tr><th rowspan="2">Resolution parameter (R)<br>p<sub>T</sub></th><th colspan="2">0.2</th><th colspan="2">0.4</th><th colspan="2">0.6</th></tr><tr><th>Low p<sub>T</sub></th><th>High p<sub>T</sub></th><th>Low p<sub>T</sub></th><th>High p<sub>T</sub></th><th>Low p<sub>T</sub></th><th>High p<sub>T</sub></th></tr></thead><tbody><tr><td>Tracking eff.</td><td>21</td><td>18</td><td>24</td><td>12</td><td>12</td><td>34</td></tr><tr><td>Regularization param.</td><td>(&lt; 2)</td><td>(&lt; 2)</td><td>(&lt; 2)</td><td>2</td><td>2</td><td>(&lt; 2)</td></tr><tr><td>Unfolding prior</td><td>6</td><td>16</td><td>8</td><td>(&lt; 2)</td><td>4</td><td>(&lt; 2)</td></tr><tr><td>Measured p<sub>T</sub> range</td><td>46</td><td>4</td><td>8</td><td>(&lt; 2)</td><td>6</td><td>8</td></tr><tr><td>Fractional Collinear</td><td>+30</td><td>+12</td><td>+12</td><td>+16</td><td>+8</td><td>+20</td></tr><tr><td>Fractional Large Angle</td><td>+10</td><td>+10</td><td>+6</td><td>+10</td><td>+8</td><td>+14</td></tr><tr><td>Fractional Medium Response</td><td>+28</td><td>+14</td><td>+20</td><td>+14</td><td>+22</td><td>+14</td></tr><tr><td>Quarks/Gluon</td><td>-8</td><td>-6</td><td>-12</td><td>-12</td><td>-14</td><td>-12</td></tr><tr><td>Combined</td><td>58</td><td>32</td><td>32</td><td>26</td><td>28</td><td>44</td></tr></tbody></table>

Relative systematic uncertainties (%) for jet spectra for 30–50% central Pb–Pb collisions and all resolution parameters. The maximum uncertainties for low $p_T$ ($p_{T,jet} < 50$ GeV/c) and high $p_T$ ($p_{T,jet} > 50$ 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.

<table><caption>**Table 3**</caption><thead><tr><th rowspan="2">Resolution parameter (R)<br>p<sub>T</sub></th><th colspan="2">0.2</th><th colspan="2">0.4</th><th colspan="2">0.6</th></tr><tr><th>Low p<sub>T</sub></th><th>High p<sub>T</sub></th><th>Low p<sub>T</sub></th><th>High p<sub>T</sub></th><th>Low p<sub>T</sub></th><th>High p<sub>T</sub></th></tr></thead><tbody><tr><td>Tracking eff.</td><td>10</td><td>12</td><td>12</td><td>16</td><td>12</td><td>14</td></tr><tr><td>Regularization param.</td><td>(&lt; 2)</td><td>(&lt; 2)</td><td>(&lt; 2)</td><td>(&lt; 2)</td><td>(&lt; 2)</td><td>(&lt; 2)</td></tr><tr><td>Unfolding prior</td><td>(&lt; 2)</td><td>4</td><td>6</td><td>2</td><td>(&lt; 2)</td><td>6</td></tr><tr><td>Measured p<sub>T</sub> range</td><td>28</td><td>(&lt; 2)</td><td>10</td><td>(&lt; 2)</td><td>20</td><td>(&lt; 2)</td></tr><tr><td>Fractional Collinear</td><td>+12</td><td>+6</td><td>+22</td><td>+14</td><td>+26</td><td>+24</td></tr><tr><td>Fractional Large Angle</td><td>+8</td><td>+8</td><td>+8</td><td>+10</td><td>+26</td><td>+24</td></tr><tr><td>Fractional Medium Response</td><td>+8</td><td>+8</td><td>+14</td><td>+10</td><td>+22</td><td>+20</td></tr><tr><td>Quarks/Gluon</td><td>-8</td><td>-6</td><td>-8</td><td>-6</td><td>-12</td><td>-6</td></tr><tr><td>Combined</td><td>34</td><td>22</td><td>32</td><td>26</td><td>42</td><td>40</td></tr></tbody></table>

Relative systematic uncertainties (%) for jet spectra for 60–80% central Pb–Pb collisions and all used resolution parameters. The maximum uncertainties for low $p_T$ ($p_{T,jet} < 50$ GeV/c) and high $p_T$ ($p_{T,jet} > 50$ 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.

<table><thead><tr><th rowspan="2">Resolution parameter (R)<br>p<sub>T</sub></th><th colspan="2">0.2</th><th colspan="2">0.4</th><th colspan="2">0.6</th></tr><tr><th>Low p<sub>T</sub></th><th>High p<sub>T</sub></th><th>Low p<sub>T</sub></th><th>High p<sub>T</sub></th><th>Low p<sub>T</sub></th><th>High p<sub>T</sub></th></tr></thead><tbody><tr><td>Tracking eff.</td><td>4</td><td>10</td><td>2</td><td>10</td><td>10</td><td>14</td></tr><tr><td>Regularization param.</td><td>(&lt; 2)</td><td>(&lt; 2)</td><td>(&lt; 2)</td><td>(&lt; 2)</td><td>(&lt; 2)</td><td>(&lt; 2)</td></tr><tr><td>Unfolding prior</td><td>6</td><td>(&lt; 2)</td><td>10</td><td>6</td><td>(&lt; 2)</td><td>8</td></tr><tr><td>Measured p<sub>T</sub> range</td><td>32</td><td>4</td><td>6</td><td>4</td><td>36</td><td>14</td></tr><tr><td>Quarks/Gluon</td><td>-2</td><td>-4</td><td>-4</td><td>-4</td><td>(&lt; -2)</td><td>-4</td></tr><tr><td>Combined</td><td>46</td><td>14</td><td>14</td><td>10</td><td>50</td><td>16</td></tr></tbody></table>

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 $p_T$, which corresponds to a significant reduction in measured yield in a given $p_T$ 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, a number 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, a prior distribution is needed. The PYTHIA 8 jet $p_T$ spectrum 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 $p_T$-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 $\sigma$, which suppresses the fake jet yield (see Sec 3). The low-$p_T$ cut-off of the data that serves
<figure><img src="image_6.png" /><figcaption>Fig. 3. The $p_T$-differential inclusive charged-particle jet yield distributions as a function of $p_T$ for different values of $R$ in 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.</figcaption></figure>

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 $p_{T, jet}$ 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 3 for 0–10%, 30–50%, and 60–80% Pb–Pb collisions, respectively. The uncertainties of the $R_{AA}$ 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 $R_{AA}$ uncertainty is an additional uncertainty associated with the calculation of the $\langle T_{AA} \rangle$, given in Ref. [85]. The systematic uncertainties on the $R_{AA}$ double ratios and the cross section ratios for different $R$ values 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 $R_{AA}$ double ratios and the cross section ratios.

## 7. Results

In this section, the $p_T$-differential inclusive charged-particle jet production yields (Fig. 3), the nuclear modification factors $R_{AA}$ (Figs. 4, 5, 6), the ratios of the jet cross sections for different $R$ (Fig. 7),
<figure><img src="image_7.png" /><figcaption>Fig. 4. Nuclear modification factors of inclusive charged-particle jets as a function of $p_T$ for $R=0.2$, $R=0.4$, 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.</figcaption></figure>

and the $R_{AA}$ double ratios representing the change of nuclear modification with respect to resolution parameter of $R=0.2$ (Fig. 8) are reported.

The $p_T$-differential charged-particle jet cross section in pp collisions is shown in the bottom right panel of Fig. 3 for a broad range of $R$ values from $R=0.2$ to $R=0.6$. Jets with larger $R$ capture more of the jet’s energy, shifting the spectra to the right and resulting in an increased yield at fixed jet $p_T$. 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 $\langle T_{AA} \rangle$ [85] of the given centrality class

$$ \frac{1}{\langle T_{AA} \rangle} \frac{d^2 N_{ch jet}}{dp_T, ch jet} d\eta_{jet} [\text{mb (GeV/c)}^{-1}]. \quad (4) $$

These spectra are shown in the first three panels of Fig. 3 for the three considered centrality classes and the three values of $R$. The uncertainty from the Glauber calculation to derive $\langle T_{AA} \rangle$ 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. 4 shows the nuclear modification factors using both the new ML-based estimator and the established area-based estimator for the 0–10%,
<figure><img src="image_8.png" /><figcaption>Fig. 5. Nuclear modification factors for jets with R = 0.6 in 0–10% (left) and 30–50% (right) central Pb–Pb collisions outlining the impact of the various fragmentation models on the final result. Note that the systematic uncertainties, described in Sec. 6, are drawn both with and without the fragmentation uncertainties in the empty and filled boxes, respectively.</figcaption></figure>

30–50%, and 60–80% centrality classes. The inclusive yield suppression measured using these two approaches is consistent within uncertainties in their region of overlap. The ML-based method enables measurements at lower transverse momenta and for large R (R = 0.6) in most central Pb–Pb collisions.

Fig. 5 shows the variation in the central value of the inclusive yield suppression for R = 0.6 and the different fragmentation models described in Sec. 5. The systematic uncertainties with and without the fragmentation systematic uncertainty are shown to illustrate its relative contributions to the total. As discussed in Sec. 6, the fragmentation systematic uncertainties were treated as asymmetric. As demonstrated in Fig. 5, this typically results in a positive contribution to the yield suppression with the exception of the Quarks Only variation. Typically the Fractional Collinear variation gives the largest contribution to the systematic uncertainty.

In Fig. 6, the nuclear modification factors for R = 0.2, R = 0.4, and R = 0.6 are compared to theoretical models incorporating jet quenching. Results are compared with: the Hybrid Model [100], which implements an energy loss with an AdS-CFT-inspired dependence on the path length, as well as a response of the medium to the lost energy; the Linear Boltzmann Transport (LBT) model [61,101] and LIDO [102], which use linear Boltzmann equations to describe the transport of partons in the QGP; JETSCAPE [103], which includes a medium-modified parton shower at high parton virtuality via MATTER [104], switching to the LBT model at low virtuality [101] (JETSCAPEv3.5 AA22 tune); Mehtar-Tani et al. [105], which is a first-principles analytical calculation of the single-inclusive jet spectrum using quenching factors; MARTINI [106], which embeds partons into a hydrodynamic medium with a modified parton shower; and JEWEL [107,108], which consists of a Monte Carlo implementation of BDMPS-based medium-induced gluon radiation in a medium modeled with a Bjorken expansion, including calculations both with and without enabling recoils [59]. Some model calculations do not cover the full phase space of the measurements.

The calculations generally describe the data in central collisions for the smaller resolution parameters (R = 0.2 and 0.4), except for JEWEL with recoils which overestimates the R = 0.4 result at low jet p<sub>T</sub>. Additionally, the JEWEL with recoils predicts significantly higher values for the result at R = 0.6 than the measurement. The calculations span a larger range for the semi-central collisions but still mostly describe the data within uncertainties, although JEWEL shows some slight tension. Overall, these comparisons demonstrate the importance of comparing with models over a wide p<sub>T</sub> interval and for different values of the R parameter, particularly at large R.

The jet cross section ratio is defined as the ratio of the per-event jet yields measured in the same collision system for different resolution parameters:

$$ \sigma(R = R_1)/\sigma(R = R_2) = \frac{dN_{R_1}}{dp_{T, \text{ch jet}}} / \frac{dN_{R_2}}{dp_{T, \text{ch jet}}}. \qquad (5) $$

In the ratio, the tracking and fragmentation uncertainties are highly correlated, while all other uncertainties are considered as uncorrelated. Sec. 6 describes the calculation of the systematic uncertainties in the ratio. The inclusive jet cross section ratios are a key observable for jet shapes and have been measured both at RHIC and the LHC [19,62–64,66,109]. The jet cross section ratios are shown in Fig. 7 for pp collisions and for Pb–Pb collisions in the 0–10% and 30–50% centrality intervals. The left panel presents the ratios for R = 0.2 and R = 0.4, and the right panel for R = 0.2 and R = 0.6. The ratio for σ(R = 0.2)/σ(R = 0.6) in Pb–Pb collisions is slightly larger than for pp collisions, taking into account the uncertainties. This suggests a narrowing of the intra-jet energy distribution in Pb–Pb collisions. No significant centrality dependence is observed in the jet cross section ratios within measurement uncertainties. There is a small p<sub>T</sub>-dependence in the jet cross section ratios in pp collisions, which becomes stronger at lower jet p<sub>T</sub>, resulting from the p<sub>T</sub> spectrum being steeper for larger R than from smaller R in pp collisions. However, the jet cross section ratios in Pb–Pb collisions are consistent with no dependence on the jet p<sub>T</sub>. This indicates that there is an R-dependence to the evolution of the jet cross section in pp collisions with p<sub>T</sub> which may impact the dependence of the R<sub>AA</sub> on R and p<sub>T</sub>, as discussed below.

The double ratio of the nuclear modification factor, which compares R<sub>AA</sub><sup>R</sup> for different R to R<sub>AA</sub><sup>R=0.2</sup>, is used to quantify the variation of the nuclear modification factor with respect to the jet resolution parameter. It is defined as

$$ R_{AA}^{R/0.2} = \frac{R_{AA}^{R}}{R_{AA}^{0.2}} = \frac{\sigma_{AA}(R)}{\sigma_{AA}(0.2)} / \frac{\sigma_{pp}(R)}{\sigma_{pp}(0.2)}. \qquad (6) $$

Thus, the observable is not only a double ratio of nuclear modification factors but also of jet cross section ratios as defined in Eq. (5). The R<sub>AA</sub> double ratio is a key observable to quantify the R-dependence of energy loss: when this ratio is less than unity, jets with larger R are more suppressed; when it is consistent with unity, there is no R-dependence (or a cancellation of effects); and when it is greater than unity, larger R jets are less suppressed.

The measured R<sub>AA</sub> double ratios are shown in Fig. 8 for 0–10% and 30–50% central Pb–Pb collisions. The R<sub>AA</sub><sup>0.4/0.2</sup> ratio in 0–10% and
<figure><img src="image_9.png" /><figcaption>Fig. 6. Nuclear modification factors for $R=0.2$, $R=0.4$, and $R=0.6$, shown for 0–10% and 30–50% central Pb–Pb collisions compared to theoretical calculations incorporating jet quenching (see text for details).</figcaption></figure>

30–50% central collisions and the $R_{AA}^{0.6/0.2}$ ratio in 30–50% central collisions are consistent with unity, indicating no significant $R$-dependence or $p_T$-dependence. In contrast, the $R_{AA}^{0.6/0.2}$ ratio in 0–10% central collisions is below unity at lower jet $p_T$ values, indicating a hint of an $R$-dependence within uncertainties. Assuming a 50% (100%) correlation of the systematic uncertainties, the deviation from unity in 0–10% central collisions is approximately 2.8 (1.6) sigma for the $R_{AA}^{0.6/0.2}$ ratio, and approximately 1.6 (1.0) sigma for $R_{AA}^{0.4/0.2}$ ratio.

Many competing effects can be considered when interpreting this ratio. As observed in Ref. [96], the jet energy is transferred mostly to soft particles, and a significant fraction of these particles are found at large angles relative to the jet axis. Thus, as the jet $R$ increases, the energy lost outside of a smaller $R$ jet cone should be recovered in a larger $R$ jet cone. Additionally, the medium responds to the jet as the jet propagates through it, which is hypothesized to cause a wake that pushes particles back inside the jet cone [100]. This could cause an increase in
<figure><img src="image_10.png" /><figcaption>Fig. 7. Jet cross section ratios for $\sigma(R=0.2)/\sigma(R=0.4)$ (left) and $\sigma(R=0.2)/\sigma(R=0.6)$ (right).</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 8. Double ratio of jet nuclear modification factors using $R_{AA}^{R=0.2}$ as the denominator and using $R=0.4$ (left) and $R=0.6$ (right) as the numerator compared to model predictions for central (top row) and semi-central (bottom row) collisions. Note that a comparison to JEWEL with recoils was omitted from the top right plot as its prediction is out of scale.</figcaption></figure>

the $R_{AA}$ with increasing $R$. Recent jet substructure measurements, such as those in Ref. [110,111], show that the yield of jets with a more complex substructure, for example, wider jets, is suppressed at a fixed jet $p_T$ compared to narrower jets. The larger $R$ jets at a given $p_T$ could be a population whose transverse distribution is broader in pp collisions but which, consequently, are more strongly quenched in the medium. Such an effect would cause the $R_{AA}$ to decrease with increasing $R$. Finally,

in vacuum, the jet spectrum is slightly steeper for large $R$ jets as seen in Fig. 7 and Ref. [21]. Thus, even for the same energy loss, some small decrease in the $R_{AA}$ with increasing $R$ would be observed.

The double ratio is also compared in Fig. 8 to calculations that can exhibit different dependencies with the jet $R$ depending on the relative contributions of various energy loss mechanisms, making this a potentially discriminating observable. In addition to the models pre-
viously compared to the jet $R_{AA}$, further comparisons are made with Qiu et al. [112] calculations, which are based on a factorization approach inspired by phenomenological considerations. JEWEL with recoils shows an increasing $R_{AA}$ with increasing $R$ due to the medium response, which contrasts with the data, especially for large $R$. LBT also shows an increasing trend with increasing $R$, as well as a jet $p_T$ dependence at large $R$, which is not supported by the data. The Hybrid Model and LIDO predict values of the double ratio close to unity, indicating a very mild dependence on $R$ in these models. JETSCAPE, JEWEL without recoils, MARTINI, all variations of Mehtar-Tani et al., and the factorization (Qiu et al.) model show the $R_{AA}$ decreasing with increasing $R$, describing the trend in the data. This could indicate that wider jets with a more complex substructure experience more suppression. This explanation may be compatible with the CMS measurement [67], which did not show an $R$-dependence, as there may be a $p_T$-dependence to the substructure of the jet [21]. Additionally, quenching effects are expected to be larger at lower jet $p_T$. The double ratio in Fig. 8 is in contrast with the $R_{cp}$ results from ATLAS [68], though many differences in the jet populations may contribute to these differences. Assuming a purely fractional energy loss scheme where each jet loses a specified fraction of its energy, only an ~3% difference in energy loss between $R=0.6$ and $R=0.2$ is needed to create a difference in the $R_{AA}$ values compatible with what is observed in Fig. 8.

## 8. Summary

A new ML-based background estimator was applied to measure inclusive charged-particle jets with resolution parameters up to $R=0.6$ in Pb-Pb collisions at the LHC. The ML-based approach leads to significantly reduced residual background fluctuations compared to the area-based method (Fig. 1). This improvement is achieved at the expense of an additional systematic uncertainty from the fragmentation dependence of the background estimator (Figs. 2 and 5, Tables 1, 2, and 3). Still, this method allowed for the measurement of $R=0.6$ jets down to a low jet $p_T$ of 40 GeV/c in central (0–10%) Pb-Pb collisions for the first time. Using the new estimator, the transverse momentum spectra (Fig. 3), nuclear modification factors (Figs. 4 and 6), cross section ratios (Fig. 7), and nuclear modification double ratios (Fig. 8) of charged-particle jets in Pb-Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV could be measured. Comparing the nuclear modification factors for different resolution parameters indicates increased jet suppression with increasing $R$, most significantly for the $R=0.6$ jets. This is also reflected in the ratios of jet cross sections reconstructed with different $R$ values measured in Pb-Pb collisions, which also deviate from the pp reference for most central collisions. The data are consistent with a variety of theoretical descriptions, including JETSCAPE, the analytical calculations of Mehtar-Tani and Qiu et al., and JEWEL without recoiling thermal medium particles.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

This manuscript has associated data in a HEPData repository at: https://www.hepdata.net/record/ins2637686.

## Acknowledgements

The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and

the Worldwide LHC Computing Grid (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL), State Committee of Science and World Federation of Scientists (WFS), Armenia; Austrian Academy of Sciences, Austrian Science Fund (FWF): [M 2467-N36] and Nationalstiftung für Forschung, Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, National Nuclear Research Center, Azerbaijan; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Financiadora de Estudos e Projetos (Finep), Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Universidade Federal do Rio Grande do Sul (UFRGS), Brazil; Bulgarian Ministry of Education and Science, within the National Roadmap for Research Infrastructures 2020-2027 (object CERN), Bulgaria; Ministry of Education of China (MOEC), Ministry of Science & Technology of China (MSTC) and National Natural Science Foundation of China (NSFC), China; Ministry of Science and Education and Croatian Science Foundation, Croatia; Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Cuba; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic; The Danish Council for Independent Research | Natural Sciences, the Villum Fonden and Danish National Research Foundation (DNRF), Denmark; Helsinki Institute of Physics (HIP), Finland; Commissariat à l'Énergie Atomique (CEA) and Institut National de Physique Nucléaire et de Physique des Particules (IN2P3) and Centre National de la Recherche Scientifique (CNRS), France; Bundesministerium für Bildung und Forschung (BMBF) and GSI Helmholtzzentrum für Schweizerienforschung GmbH, Germany; General Secretariat for Research and Technology, Ministry of Education, Research and Religions, Greece; National Research, Development and Innovation Office, Hungary; Department of Atomic Energy, Government of India (DAE), Department of Science and Technology, Government of India (DST), University Grants Commission, Government of India (UGC) and Council of Scientific and Industrial Research (CSIR), India; National Research and Innovation Agency - BRIN, Indonesia; Istituto Nazionale di Fisica Nucleare (INFN), Italy; Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japan Society for the Promotion of Science (JSPS) KAKENHI, Japan; Consejo Nacional de Ciencia (CONACYT) y Tecnología, through Fondo de Cooperación Internacional en Ciencia y Tecnología (FONCICYT) y Dirección General de Asuntos del Personal Académico (DGAPA), Mexico; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), Netherlands; The Research Council of Norway, Norway; Commission on Science and Technology for Sustainable Development in the South (COMSAT'S), Pakistan; Pontificia Universidad Católica del Perú, Peru; Ministry of Education and Science, National Science Centre and WUT ID-UB, Poland; Korea Institute of Science and Technology Information and National Research Foundation of Korea (NRF), Republic of Korea; Ministry of Education and Scientific Research, Institute of Atomic Physics, Ministry of Research and Innovation and Institute of Atomic Physics and Universitatea Nationala de Stiința și Tehnologie Politehnică București, Romania; Ministry of Education, Science, Research and Sport of the Slovak Republic, Slovakia; National Research Foundation of South Africa, South Africa; Swedish Research Council (VR) and Knut & Alice Wallenberg Foundation (KAW), Sweden; European Organization for Nuclear Research, Switzerland; Suranaree University of Technology (SUT), National Science and Technology Development Agency (NSTDA) and National Science, Research and Innovation Fund (NSRF via PMU-B B05F650021), Thailand; Turkish Energy, Nuclear and Mineral Research Agency (TENMAK), Turkey; National Academy of Sciences of Ukraine, Ukraine; Science and Technology Facilities Council (STFC), United Kingdom; National Science Foundation of the United States of America (NSF) and United States Department of Energy, Office of Nuclear Physics (DOE NP), United States of America. In addition, individual groups or members have received support from: European Research Council, Strong 2020 - Horizon 2020, Marie Skłodowska Curie (grant nos. 950692, 824093, 896850), Euro-
<figure><img src="image_12.png" /><figcaption>Fig. B.1. The $\delta p_T$ distributions for $R = 0.4$ (left) and $R = 0.6$ (right) jets in central (0–10%) collisions using the area-based (dashed lines) and ML-based (solid markers) corrections.</figcaption></figure>

pean Union; Academy of Finland (Center of Excellence in Quark Matter) (grant nos. 346327, 346328), Finland; Programa de Apoyos para la Su- peración del Personal Académico, UNAM, Mexico.

## Appendix A. Machine learning methods

A novel background estimator based on machine learning is used to correct the $p_T$-smearing effects caused by the background, utilizing the approach described in Ref. [73]. In this treatment, correcting the background effects is framed as a regression task which aims to predict a reconstructed jet $p_T$ value for each jet candidate. Following this ap- proach, several ML algorithms have been evaluated and compared, such as neural networks [113], random forests [114], and linear regression. While these algorithms differ in performance, they all lead to similar, fully corrected results. For this analysis, we chose a shallow neural net- work model, which (as in Ref. [73]) demonstrates a slightly improved performance compared to other explored algorithms. This shallow neu- ral network is implemented as a three-layer perceptron with 100 nodes in the first two layers and 50 in the last. The activation function cho- sen for the nodes is the ReLU [115], while the ADAM optimizer [116] is employed in the neural network training.

The quality of the training dataset plays a crucial role in the ap- plicability of corrections based on a machine-learning technique. To simulate events with jets in a heavy-ion background for the training of the ML estimator, reconstructed PYTHIA 8 events simulated to the detector level are embedded into a thermally-distributed background. The thermal background is created by randomly distributing charged particles according to a uniform particle multiplicity distribution rang- ing from 0 to 3000 tracks with a uniform $\eta$ distribution and a realistic (quasi-thermal) transverse momentum distribution based on a Tsallis fit to data. The transverse momentum distribution is tuned to describe the reconstructed track momentum distribution of Pb–Pb minimum bias data at low $p_T$. For particles with $p_T \ge 4$ GeV/c, the transverse mo- mentum distribution in the thermal background is steeper than in data since, by construction, the thermal background does not include jets. As a cross-check, real minimum bias Pb–Pb events were also used as background heavy-ion events. The difference in the distribution of final jet observables was negligible for different choices of the background training distribution. This indicates that the model is robust to changes in the background used in training.

In order to find a suitable combination of jet and event proper- ties as input features to the neural network, the analysis was repeated for a large variety of configurations of the input parameters/features. The number of features used was kept small to ensure a generalizable

model. These features were chosen by iteratively removing unimpor- tant or highly correlated features as long as the performance was not significantly reduced, ensuring a minimal list with good performance was reached. Note that the metric for considering a parameter unim- portant is a relatively low Gini [117] importance in the random forest estimator, which is used as a proxy for important features in the neural network. The degree of correlation between variables was calculated using the Pearson coefficient. For example, the uncorrected jet trans- verse momentum was excluded from this list due to its correlation with the area-based corrected transverse momentum. Based on these consid- erations, the following input features were selected: the jet transverse momentum corrected by the standard area-based method, the first ra- dial moment of constituent momenta (jet angularity), the number of constituents within the jet, and the transverse momenta of the eight leading (highest $p_T$) particles within the jet. For the training, the ap- plied supervised learning techniques need a target value assigned to each sample, i.e. to each jet. The regression target that is approximated by the correction method is the jet $p_T$ at detector level, $p_{T,jet}^{\text{target}}$. This is defined as the reconstructed jet transverse momentum multiplied by the jet momentum fraction that is carried by the jet constituents originating from the PYTHIA simulated event,

$$p_{T,jet}^{\text{target}} = p_{T,jet}^{\text{raw}} \sum_{i} p_{T, \text{const } i}^{\text{PYTHIA}} / \sum_{i} p_{T, \text{const } i} \quad (A.1)$$

where $p_{T,jet}^{\text{raw}}$ is the reconstructed jet transverse momentum before any background correction. With this definition of the target jet $p_T$, the background is also defined implicitly; it consists of all the particles from the thermal model. As an alternative definition, the target jet $p_T$ could also be defined as the detector-level jet $p_T$ described in Sec. 3. Since the background influences the jet finding algorithm, these matched jets are conceptually closer to the perfectly corrected jets but have other disad- vantages, such as potential mismatches to jets present in the minimum bias data sample, in particular at low transverse momentum. However, models trained on the two target definitions were shown to have a sim- ilar performance within the uncertainty of the measurement.

## Appendix B. Supplementary figures

One source of fluctuations in the heavy-ion background is correlated fluctuations due to flow-like effects. These flow-like effects generate a momentum-space anisotropy of particles, which are oriented with re- spect to the event plane. The magnitude of this correlated background grows with the radius of the jet and depends on the angle between the jet axis and the event plane, given by $\Delta\phi = \phi_{jet} - \psi_{EP}$, where $\psi_{EP}$
represents the event plane orientation. Jets are classified as in-plane $\Delta\phi < 30^{\circ}$, mid-plane ($60^{\circ} > \Delta\phi > 30^{\circ}$), or out-of-plane ($\Delta\phi > 60^{\circ}$). The AB method does not correct explicitly for flow-like effects, which manifests itself as a dependence of the $\delta p_T$ distributions on $\Delta\phi$. This can be seen in the dashed lines in Fig. B.1, where the mean of the $\delta p_T$ distributions is larger for the in-plane case. Such an observation is consistent with not fully accounting for correlated fluctuations. The performance of the ML-based method is shown as solid markers in Fig. B.1, where the $\delta p_T$ distribution exhibits minimal event plane dependence. This indicates that the ML is able to properly correct for these effects despite omitting the event plane from training.

## References

[1] BRAHMS Collaboration, I. Arsene, et al., Quark-gluon plasma and color glass condensate at RHIC? The perspective from the BRAHMS experiment, Nucl. Phys. A 757 (2005) 1-27, arXiv:nucl-ex/0410020.

[2] PHOBOS Collaboration, B. Back, et al., The PHOBOS perspective on discoveries at RHIC, Nucl. Phys. A 757 (2005) 28-101, arXiv:nucl-ex/0410003.

[3] PHENIX Collaboration, K. Adcox, et al., Formation of dense partonic matter in relativistic nucleus-nucleus collisions at RHIC: experimental evaluation by the PHENIX collaboration, Nucl. Phys. A 757 (2005) 184-283, arXiv:nucl-ex/0410022.

[4] STAR Collaboration, J. Adams, et al., Experimental and theoretical challenges in the search for the quark-gluon plasma: the STAR Collaboration's critical assessment of the evidence from RHIC collisions, Nucl. Phys. A 757 (2005) 102-183, arXiv:nucl-ex/0501009.

[5] ALICE Collaboration, K. Aamodt, et al., Charged-particle multiplicity density at mid-rapidity in central Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Rev. Lett. 105 (2010) 252301, arXiv:1011.3916 [nucl-ex].

[6] ALICE Collaboration, K. Aamodt, et al., Centrality dependence of the charged-particle multiplicity density at mid-rapidity in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Rev. Lett. 106 (2011) 032301, arXiv:1012.1657 [nucl-ex].

[7] CMS Collaboration, S. Chatrchyan, et al., Dependence on pseudorapidity and centrality of charged hadron production in Pb-Pb collisions at a nucleon-nucleon centre-of-mass energy of 2.76 TeV, J. High Energy Phys. 1108 (2011) 141, arXiv:1107.4800 [nucl-ex].

[8] ALICE Collaboration, K. Aamodt, et al., Two-pion Bose-Einstein correlations in central Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Lett. B 696 (2011) 328-337, arXiv:1012.4035 [nucl-ex].

[9] ALICE Collaboration, K. Aamodt, et al., Elliptic flow of charged particles in Pb-Pb collisions at 2.76 TeV, Phys. Rev. Lett. 105 (2010) 252302, arXiv:1011.3914 [nucl-ex].

[10] ATLAS Collaboration, G. Aad, et al., Measurement of the pseudorapidity and transverse momentum dependence of the elliptic flow of charged particles in lead-lead collisions at $\sqrt{s_{NN}} = 2.76$ TeV with the ATLAS detector, Phys. Lett. B 707 (2012) 330-348, arXiv:1108.6018 [hep-ex].

[11] CMS Collaboration, S. Chatrchyan, et al., Centrality dependence of dihadron correlations and azimuthal anisotropy harmonics in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Eur. Phys. J. C 72 (2012) 12, arXiv:1201.3158 [nucl-ex].

[12] ALICE Collaboration, K. Aamodt, et al., Higher harmonic anisotropic flow measurements of charged particles in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Rev. Lett. 107 (2011) 032301, arXiv:1105.3865 [nucl-ex].

[13] ATLAS Collaboration, G. Aad, et al., Measurement of the distributions of event-by-event flow harmonics in lead-lead collisions at 2.76 TeV with the ATLAS detector at the LHC, J. High Energy Phys. 1311 (2013) 183, arXiv:1305.2942 [hep-ex].

[14] CMS Collaboration, S. Chatrchyan, et al., Measurement of higher-order harmonic azimuthal anisotropy in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Rev. C 89 (2014) 044906, arXiv:1310.8651 [nucl-ex].

[15] ALICE Collaboration, K. Aamodt, et al., Suppression of charged particle production at large transverse momentum in central Pb-Pb Collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Lett. B 696 (2011) 30-39, arXiv:1012.1004 [nucl-ex].

[16] CMS Collaboration, S. Chatrchyan, et al., Observation and studies of jet quenching in Pb-Pb collisions at nucleon-nucleon center-of-mass energy of 2.76 TeV, Phys. Rev. C 84 (2011) 024906, arXiv:1102.1957 [nucl-ex].

[17] ALICE Collaboration, The ALICE experiment - a journey through QCD, arXiv:2211.04384 [nucl-ex].

[18] ATLAS Collaboration, M. Aaboud, et al., Measurement of inclusive jet and dijet cross-sections in proton-proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector, J. High Energy Phys. 05 (2018) 195, arXiv:1711.02692 [hep-ex].

[19] CMS Collaboration, V. Khachatryan, et al., Measurement of the double-differential inclusive jet cross section in proton-proton collisions at $\sqrt{s} = 13$ TeV, Eur. Phys. J. C 76 (2016) 451, arXiv:1605.04436 [hep-ex].

[20] S. Marzani, G. Soyez, M. Spannowsky, Looking Inside Jets: An Introduction to Jet Substructure and Boosted-Object Phenomenology, Lect. Notes Phys., vol. 958, Springer, 2019, arXiv:1901.10342 [hep-ph].

[21] CMS Collaboration, A.M. Sirunyan, et al., Dependence of inclusive jet production on the anti-$k_T$ distance parameter in pp collisions at $\sqrt{s} = 13$ TeV, J. High Energy Phys. 12 (2020) 082, arXiv:2005.05159 [hep-ex].

[22] L. Cunqueiro, A.M. Sickles, Studying the QGP with Jets at the LHC and RHIC, Prog. Part. Nucl. Phys. 124 (2022) 103940, arXiv:2110.14490 [nucl-ex].

[23] PHENIX Collaboration, K. Adcox, et al., Suppression of hadrons with large transverse momentum in central Au-Au collisions at $\sqrt{s_{NN}} = 130$ GeV, Phys. Rev. Lett. 88 (2002) 022301, arXiv:nucl-ex/0109003 [nucl-ex].

[24] STAR Collaboration, C. Adler, et al., Disappearance of back-to-back high $p_T$ hadron correlations in central Au-Au collisions at $\sqrt{s_{NN}} = 200$ GeV, Phys. Rev. Lett. 90 (2003) 082302, arXiv:nucl-ex/0210033 [nucl-ex].

[25] STAR Collaboration, C. Adler, et al., Centrality dependence of high $p_T$ hadron suppression in Au-Au collisions at $\sqrt{s_{NN}} = 130$ GeV, Phys. Rev. Lett. 89 (2002) 202301, arXiv:nucl-ex/0206011 [nucl-ex].

[26] PHENIX Collaboration, K. Adcox, et al., Centrality dependence of the high-$p_T$ charged hadron suppression in Au-Au collisions at $\sqrt{s_{NN}} = 130$ GeV, Phys. Lett. B 561 (2003) 82-92, arXiv:nucl-ex/0207009 [nucl-ex].

[27] PHENIX Collaboration, S.S. Adler, et al., Suppressed $\pi^0$ production at large transverse momentum in central Au-Au collisions at $\sqrt{s_{NN}} = 200$ GeV, Phys. Rev. Lett. 91 (2003) 072301, arXiv:nucl-ex/0304022.

[28] STAR Collaboration, J. Adams, et al., Transverse momentum and collision-energy dependence of high-$p_T$ hadron suppression in Au-Au collisions at ultrarelativistic energies, Phys. Rev. Lett. 91 (Oct 2003) 172302, arXiv:nucl-ex/0305015 [nucl-ex].

[29] STAR Collaboration, J. Adams, et al., Evidence from d-Au measurements for final state suppression of high-$p_T$ hadrons in Au-Au collisions at RHIC, Phys. Rev. Lett. 91 (2003) 072304, arXiv:nucl-ex/0306024 [nucl-ex].

[30] PHOBOS Collaboration, B. Back, et al., Charged hadron transverse momentum distributions in Au-Au collisions at $\sqrt{s_{NN}} = 200$ GeV, Phys. Lett. B 578 (2004) 297-303, arXiv:nucl-ex/0302015 [nucl-ex].

[31] BRAHMS Collaboration, I. Arsene, et al., Transverse momentum spectra in Au-Au and d-Au collisions at $\sqrt{s_{NN}} = 200$ GeV and the pseudorapidity dependence of high-$p_T$ suppression, Phys. Rev. Lett. 91 (2003) 072305, arXiv:nucl-ex/0307003 [nucl-ex].

[32] STAR Collaboration, J. Adams, et al., Direct observation of dijets in central Au-Au collisions at $\sqrt{s_{NN}} = 200$ GeV, Phys. Rev. Lett. 97 (2006) 162301, arXiv:nucl-ex/0604018 [nucl-ex].

[33] PHENIX Collaboration, A. Adare, et al., System size and energy dependence of jet-induced hadron pair correlation shapes in Cu-Cu and Au-Au collisions at $\sqrt{s_{NN}} = 200$ and 62.4 GeV, Phys. Rev. Lett. 98 (2007) 232302, arXiv:nucl-ex/0611019 [nucl-ex].

[34] PHENIX Collaboration, A. Adare, et al., Quantitative constraints on the opacity of hot partonic matter from semi-inclusive single high transverse momentum pion suppression in Au-Au collisions at $\sqrt{s_{NN}} = 200$ GeV, Phys. Rev. C 77 (2008) 064907, arXiv:0801.1665 [nucl-ex].

[35] STAR Collaboration, L. Adamczyk, et al., Jet-hadron correlations in $\sqrt{s_{NN}} = 200$ GeV pp and central Au-Au collisions, Phys. Rev. Lett. 112 (2014) 122301, arXiv:1302.6184 [nucl-ex].

[36] STAR Collaboration, L. Adamczyk, et al., Measurements of jet quenching with semi-inclusive hadron-jet distributions in Au-Au collisions at $\sqrt{s_{NN}} = 200$ GeV, Phys. Rev. C 96 (2017) 024905, arXiv:1702.01108 [nucl-ex].

[37] ATLAS Collaboration, G. Aad, et al., Observation of a centrality-dependent dijet asymmetry in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV with the ATLAS Detector at the LHC, Phys. Rev. Lett. 105 (2010) 252303, arXiv:1011.6182 [hep-ex].

[38] ALICE Collaboration, K. Aamodt, et al., Particle-yield modification in jet-like azimuthal di-hadron correlations in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Rev. Lett. 108 (2012) 092301, arXiv:1110.0121 [nucl-ex].

[39] CMS Collaboration, S. Chatrchyan, et al., Study of high-$p_T$ charged particle suppression in Pb-Pb compared to pp collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Eur. Phys. J. C 72 (2012) 1945, arXiv:1202.2554 [nucl-ex].

[40] CMS Collaboration, S. Chatrchyan, et al., Jet momentum dependence of jet quenching in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Lett. B 712 (2012) 176-197, arXiv:1202.5022 [nucl-ex].

[41] CMS Collaboration, S. Chatrchyan, et al., Measurement of jet fragmentation into charged particles in pp and Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV, J. High Energy Phys. 1210 (2012) 087, arXiv:1205.5872 [nucl-ex].

[42] CMS Collaboration, S. Chatrchyan, et al., Studies of jet quenching using isolated-photon+jet correlations in Pb-Pb and pp collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Lett. B 718 (2013) 773-794, arXiv:1205.0206 [nucl-ex].

[43] ATLAS Collaboration, G. Aad, et al., Measurement of the jet radius and transverse momentum dependence of inclusive jet suppression in lead-lead collisions at $\sqrt{s_{NN}} = 2.76$ TeV with the ATLAS detector, Phys. Lett. B 719 (2013) 220-241, arXiv:1208.1967 [hep-ex].

[44] CMS Collaboration, S. Chatrchyan, et al., Evidence of b-jet quenching in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Rev. Lett. 113 (2014) 132301, arXiv:1312.4198 [nucl-ex].

[45] CMS Collaboration, S. Chatrchyan, et al., Modification of jet shapes in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Lett. B 730 (2014) 243-263, arXiv:1310.0878 [nucl-ex].

[46] CMS Collaboration, S. Chatrchyan, et al., Measurement of jet fragmentation in Pb-Pb and pp collisions at $\sqrt{s_{NN}} = 2.76$ TeV, Phys. Rev. C 90 (2014) 024908, arXiv:1406.0932 [nucl-ex].

[47] ATLAS Collaboration, G. Aad, et al., Measurement of inclusive jet charged-particle fragmentation functions in Pb-Pb collisions at $\sqrt{s_{NN}} = 2.76$ TeV with the ATLAS detector, Phys. Lett. B 739 (2014) 320-342, arXiv:1406.2979 [hep-ex].
[48] ATLAS Collaboration, G. Aad, et al., Measurements of the nuclear modification factor for jets in Pb-Pb collisions at $\sqrt{s_{NN}}$ = 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 semi-inclusive hadron-jet distributions in central Pb-Pb collisions at $\sqrt{s_{NN}}$ = 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 small-radius jets in central Pb-Pb collisions at $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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. Vitek, 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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, a large 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s}$ = 5 TeV, 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. Safarik, 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 $\sqrt{s}$ = 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 $\sqrt{s}$ = 7 TeV, 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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].
[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 $\sqrt{s} = 2.76$ 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 $\sqrt{s_{NN}} = 5.02$ 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 heavy-ion 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. Adamová⁸⁶, A. Adler⁷⁰, G. Aglieri Rinella³², M. Agnello²⁹, N. Agrawal⁵¹, Z. Ahammed¹³⁴, S. Ahmad¹⁵, S.U. Ahn⁷¹, I. Ahuja³⁷, A. Akindinov¹⁴⁰, M. Al-Turany⁹⁷, D. Aleksandrov¹⁴⁰, B. Alessandro⁵⁶, H.M. Alfanda⁶, R. Alfaro Molina⁶⁷, B. Ali¹⁵, A. Alici²⁵, N. Alizadehvandchali¹¹⁵, A. Alkin³², J. Alme²⁰, G. Alocco⁵², T. Alt⁶⁴, I. Altsybeev¹⁴⁰, J.R. Alvarado⁴⁴, M.N. Anaam⁶, C. Andrei⁴⁵, A. Andronic¹²⁵, V. Anguelov⁹⁴, F. Antinori⁵⁴, P. Antonioli⁵¹, N. Apadula⁷⁴, L. Aphecetche¹⁰³, H. Appelshäuser⁶⁴, C. Arata⁷³, S. Arcelli²⁵, M. Aresti⁵², R. Arnaldi⁵⁶, J.G.M.C.A. Arneiro¹¹⁰, I.C. Arsene¹⁹, M. Arslandok¹³⁷, A. Augustinus³², R. Averbeck⁹⁷, M.D. Azmi¹⁵, A. Badalà⁵³, J. Bae¹⁰⁴, Y.W. Baek⁴⁰, X. Bai¹¹⁹, R. Bailhache⁶⁴, Y. Bailung⁴⁸, A. Balbino²⁹, A. Baldisseri¹²⁹, B. Balis², D. Banerjee⁴, Z. Banoo⁹¹, R. Barbera²⁶, F. Barile³¹, L. Barioglio⁹⁵, M. Barlou⁷⁸, G.G. Barnaföldi⁴⁶, L.S. Barnby⁸⁵, V. Barret¹²⁶, L. Barreto¹¹⁰, C. Bartels¹¹⁸, K. Barth³², E. Bartsch⁶⁴, N. Bastid¹²⁶, S. Basu⁷⁵, G. Batigne¹⁰³, D. Battistini⁹⁵, B. Batyunya¹⁴¹, D. Bauri⁴⁷, J.L. Bazo Alba¹⁰¹, I.G. Bearden⁸³, C. Beattie¹³⁷, P. Becht⁹⁷, D. Behera⁴⁸, I. Belikov¹²⁸, A.D.C. Bell Hechavarria¹²⁵, F. Bellini²⁵, R. Bellwied¹¹⁵, S. Belokurova¹⁴⁰, V. Belyaev¹⁴⁰, G. Bencedi⁴⁶, S. Beole²⁴, Y. Berdnikov¹⁴⁰, A. Berdnikova⁹⁴, L. Bergmann⁹⁴, M.G. Besoiu⁶³, L. Betev³², P.P. Bhaduri¹³⁴, A. Bhasin⁹¹, M.A. Bhat⁴, B. Bhattacharjee⁴¹, L. Bianchi²⁴, N. Bianchi⁴⁹, J. Bielčík³⁵, J. Bielčíková⁸⁶, J. Biernat¹⁰⁷, A.P. Bigot¹²⁸, A. Bilandzic⁹⁵, G. Biro⁴⁶, S. Biswas⁴, N. Bize¹⁰³, J.T. Blair¹⁰⁸, D. Blau¹⁴⁰, M.B. Blidaru⁹⁷, N. Bluhme³⁸, C. Blume⁶⁴, G. Boca²¹,⁵⁵, F. Bock⁸⁷, T. Bodova²⁰, A. Bogdanov¹⁴⁰, S. Boi²², J. Bok⁵⁸, L. Boldizsár⁴⁶, M. Bombara³⁷, P.M. Bond³², G. Bonomi¹³³,⁵⁵, H. Borel¹²⁹, A. Borissov¹⁴⁰, A.G. Borquez Carcamo⁹⁴, H. Bossi¹³⁷, E. Botta²⁴, Y.E.M. Bouziani⁶⁴, L. Bratrud⁶⁴, P. Braun-Munzinger⁹⁷, M. Bregant¹¹⁰, M. Broz³⁵, G.E. Bruno⁹⁶,³¹, M.D. Buckland²³, D. Budnikov¹⁴⁰, H. Buesching⁶⁴, S. Bufalino²⁹, P. Buhler¹⁰², Z. Buthelezi⁶⁸,¹²², A. Bylinkin²⁰, S.A. Bysiak¹⁰⁷, M. Cai⁶, H. Caines¹³⁷, A. Caliva⁹⁷, E. Calvo Villar¹⁰¹, J.M.M. Camacho¹⁰⁹, P. Camerini²³, F.D.M. Canedo¹¹⁰, S.L. Cantway¹³⁷, M. Carabas¹¹³, A.A. Carballo³², F. Carnesecchi³², R. Caron¹²⁷, L.A.D. Carvalho¹¹⁰, J. Castillo Castellanos¹²⁹, F. Catalano³²,⁴², C. Ceballos Sanchez¹⁴¹, I. Chakaberia⁷⁴, P. Chakraborty⁴⁷, S. Chandra¹³⁴, S. Chapeland³², M. Chartier¹¹⁸, S. Chattopadhyay¹³⁴, S. Chattopadhyay⁹⁹, T. Cheng⁹⁷,⁶, C. Cheshkov¹²⁷, B. Cheynis¹²⁷, V. Chibante Barroso³², D.D. Chinellato¹¹¹, E.S. Chizzali⁹⁵, II, J. Cho⁵⁸, S. Cho⁵⁸, P. Chochula³², P. Christakoglou⁸⁴, C.H. Christensen⁸³, P. Christiansen⁷⁵, T. Chujo¹²⁴, M. Ciacco²⁹, C. Cicalo⁵², F. Cindolo⁵¹, M.R. Ciupek⁹⁷, G. Clai⁵¹,III, F. Colamaria⁵⁰, J.S. Colburn¹⁰⁰, D. Colella⁹⁶,³¹, M. Colocci²⁵, M. Concas⁵⁶, IV, G. Conesa Balbastre⁷³, Z. Conesa del Valle¹³⁰, G. Contin²³, J.G. Contreras³⁵, M.L. Coquet¹²⁹, T.M. Cormier⁸⁷, I, P. Cortese¹³²,⁵⁶, M.R. Cosentino¹¹², F. Costa³², S. Costanza²¹,⁵⁵, C. Cot¹³⁰, J. Crkovská⁹⁴, P. Crochet¹²⁶, R. Cruz-Torres⁷⁴, P. Cui⁶, A. Dainese⁵⁴, M.C. Danisch⁹⁴, A. Danu⁶³, P. Das⁸⁰, P. Das⁴,¹⁰
S. Das 4, ID, A.R. Dash 125, ID, S. Dash 47, ID, A. De Caro 28, ID, G. de Cataldo 50, ID, J. de Cuveland 38, A. De Falco 22, ID, D. De Gruttola 28, ID, N. De Marco 56, ID, C. De Martin 23, ID, S. De Pasquale 28, ID, R. Deb 133, ID, S. Deb 48, ID, R.J. Debski 2, ID, K.R. Deja 135, R. Del Grande 95, ID, L. Dello Stritto 28, ID, W. Deng 6, ID, P. Dhankher 18, ID, D. Di Bari 31, ID, A. Di Mauro 32, ID, B. Diab 129, ID, R.A. Diaz 141,7, ID, T. Dietel 114, ID, Y. Ding 6, ID, R. Divià 32, ID, D.U. Dixit 18, ID, Ø. Djuvsland 20, U. Dmitrieva 140, ID, A. Dobrin 63, ID, B. Dönigus 64, ID, J.M. Dubinski 135, ID, A. Dubla 97, ID, S. Dudi 90, ID, P. Dupieux 126, ID, M. Durkac 106, N. Dzalaiova 12, T.M. Eder 125, ID, R.J. Ehlers 74, ID, F. Eisenhut 64, ID, D. Elia 50, ID, B. Erazmus 103, ID, F. Ercolessi 25, ID, F. Erhardt 89, ID, M.R. Ersdal 20, B. Espagnon 130, ID, G. Eulisse 32, ID, D. Evans 100, ID, S. Evdokimov 140, ID, L. Fabbietti 95, ID, M. Faggin 27, ID, J. Faivre 73, ID, F. Fan 6, ID, W. Fan 74, ID, A. Fantoni 49, ID, M. Fasel 87, ID, P. Fecchio 29, A. Feliciello 56, ID, G. Feofilov 140, ID, A. Fernández Téllez 44, ID, L. Ferrandi 110, ID, M.B. Ferrer 32, ID, A. Ferrero 129, ID, C. Ferrero 56, ID, A. Ferretti 24, ID, V.J.G. Feuillard 94, ID, V. Filova 35, ID, D. Finogeev 140, ID, F.M. Fionda 52, ID, F. Flor 115, ID, A.N. Flores 108, ID, S. Foertsch 68, ID, I. Fokin 94, ID, S. Fokin 140, ID, E. Fragiacomo 57, ID, E. Frajna 46, ID, U. Fuchs 32, ID, N. Funicello 28, ID, C. Furget 73, ID, A. Furs 140, ID, T. Fusayasu 98, ID, J.J. Gaardhøje 83, ID, M. Gagliardi 24, ID, A.M. Gago 101, ID, C.D. Galvan 109, ID, D.R. Gangadharan 115, ID, P. Ganoti 78, ID, C. Garabatos 97, ID, T. García Chávez 44, ID, E. Garcia-Solis 9, ID, C. Gargiulo 32, ID, K. Garner 125, P. Gasik 97, ID, A. Gautam 117, ID, M.B. Gay Ducati 66, ID, M. Germain 103, ID, A. Ghimouz 124, C. Ghosh 134, M. Giacalone 51,25, ID, P. Giubellino 97,56, ID, P. Giubilato 27, ID, A.M.C. Glaenzer 129, ID, P. Glässel 94, ID, E. Glimos 121, ID, D.J.Q. Goh 76, V. Gonzalez 136, ID, M. Gorgon 2, ID, S. Gotovac 33, V. Grabski 67, ID, L.K. Graczykowski 135, ID, E. Grecka 86, ID, A. Grelli 59, ID, C. Grigoras 32, ID, V. Grigoriev 140, ID, S. Grigoryan 141,1, ID, F. Grosa 32, ID, J.F. Grosse-Oetringhaus 32, ID, R. Grosso 97, ID, D. Grund 35, ID, G.G. Guardiano 111, ID, R. Guernane 73, ID, M. Guilbaud 103, ID, K. Gulbrandsen 83, ID, T. Gündem 64, ID, T. Gunji 123, ID, W. Guo 6, ID, A. Gupta 91, ID, R. Gupta 91, ID, R. Gupta 48, ID, K. Gwizdziel 135, ID, L. Gyulai 46, ID, R. Haake 137, ID, M.K. Habib 97, C. Hadjidakis 130, ID, F.U. Haider 91, ID, H. Hamagaki 76, ID, A. Hamdi 74, ID, M. Hamid 6, Y. Han 138, ID, R. Hannigan 108, ID, J. Hansen 75, ID, M.R. Haque 135, ID, J.W. Harris 137, ID, A. Harton 9, ID, H. Hassan 87, ID, D. Hatzifotiadou 51, ID, P. Hauer 42, ID, L.B. Havener 137, ID, S.T. Heckel 95, ID, E. Hellbär 97, ID, H. Helstrup 34, ID, M. Hemmer 64, ID, T. Herman 35, ID, G. Herrera Corral 8, ID, F. Herrmann 125, S. Herrmann 127, ID, K.F. Hetland 34, ID, B. Heybeck 64, ID, H. Hillemanns 32, ID, B. Hippolyte 128, ID, F.W. Hoffmann 70, ID, B. Hofman 59, ID, B. Hohlweger 84, ID, G.H. Hong 138, ID, M. Horst 95, ID, A. Horzyk 2, ID, Y. Hou 6, ID, P. Hristov 32, ID, C. Hughes 121, ID, P. Huhn 64, L.M. Huhta 116, ID, T.J. Humanic 88, ID, A. Hutson 115, ID, D. Hutter 38, ID, R. Ilkaev 140, H. Ilyas 13, ID, M. Inaba 124, ID, G.M. Innocenti 32, ID, M. Ippolitov 140, ID, A. Isakov 86, ID, T. Isidori 117, ID, M.S. Islam 99, ID, M. Ivanov 12, M. Ivanov 97, ID, V. Ivanov 140, ID, M. Jablonski 2, ID, B. Jacak 74, ID, N. Jacazio 32, ID, P.M. Jacobs 74, ID, S. Jadlovska 106, J. Jadlovsky 106, S. Jaelani 82, ID, C. Jahnke 111, ID, M.J. Jakubowska 135, ID, M.A. Janik 135, ID, T. Janson 70, M. Jercic 89, S. Jia 10, ID, A.A.P. Jimenez 65, ID, F. Jonas 87,125, ID, J.M. Jowett 32,97, ID, J. Jung 64, ID, M. Jung 64, ID, A. Junique 32, ID, A. Jusko 100, ID, M.J. Kabus 32,135, ID, J. Kaewjai 105, P. Kalinak 60, ID, A.S. Kalteyer 97, ID, A. Kalweit 32, ID, V. Kaplin 140, ID, A. Karasu Uysal 72, ID, D. Karatovic 89, ID, O. Karavichev 140, ID, T. Karavicheva 140, ID, P. Karczmarczyk 135, ID, E. Karpechev 140, ID, U. Kebschull 70, ID, R. Keidel 139, ID, D.L.D. Keijdener 59, M. Keil 32, ID, B. Ketzer 42, ID, S.S. Khade 48, ID, A.M. Khan 119,6, ID, S. Khan 15, ID, A. Khanzadeev 140, ID, Y. Kharlov 140, ID, A. Khatun 117,15, ID, A. Khuntia 107, ID, M.B. Kidson 114, B. Kileng 34, ID, B. Kim 104, ID, C. Kim 16, ID, D.J. Kim 116, ID, E.J. Kim 69, ID, J. Kim 138, ID, J.S. Kim 40, ID, J. Kim 69, ID, M. Kim 18, ID, S. Kim 17, ID, T. Kim 138, ID, K. Kimura 92, ID, S. Kirsch 64, ID, I. Kisel 38, ID, S. Kiselev 140, ID, A. Kisiel 135, ID, J.P. Kitowski 2, ID, J.L. Klay 5, ID, J. Klein 32, ID, S. Klein 74, ID, C. Klein-Bösing 125, ID, M. Kleiner 64, ID, T. Klemenz 95, ID, A. Kluge 32, ID, A.G. Knospe 115, ID, C. Kobdaj 105, ID, T. Kollegger 97, A. Kondratyev 141, ID, N. Kondratyeva 140, ID, E. Kondratyuk 140, ID, J. König 64, ID,
S.A. Konigstorfer 95, P.J. Konopka 32, G. Kornakov 135, M. Korwieser 95, S.D. Koryciak 2,
A. Kotliarov 86, V. Kovalenko 140, M. Kowalski 107, V. Kozhuharov 36, I. Králik 60, A. Kravčáková 37,
L. Krcal 32,38, M. Krivda 100,60, F. Krizek 86, K. Krizkova Gajdosova 32, M. Kroesen 94, M. Krüger 64,
D.M. Krupova 35, E. Kryshen 140, V. Kučera 58, C. Kuhn 128, P.G. Kuijer 84, T. Kumaoka 124,
D. Kumar 134, L. Kumar 90, N. Kumar 90, S. Kumar 31, S. Kundu 32, P. Kurashvili 79, A. Kurepin 140,
A.B. Kurepin 140, A. Kuryakin 140, S. Kushpil 86, J. Kvapil 100, M.J. Kweon 58, J.Y. Kwon 58,
Y. Kwon 138, S.L. La Pointe 38, P. La Rocca 26, A. Lakrathok 105, M. Lamanna 32, R. Langoy 120,
P. Larionov 32, E. Laudi 32, L. Lautner 32,95, R. Lavicka 102, T. Lazareva 140, R. Lea 133,55,
H. Lee 104, I. Legrand 45, G. Legras 125, J. Lehrbach 38, T.M. Lelek 2, R.C. Lemmon 85, I. León
Monzón 109, M.M. Lesch 95, E.D. Lesser 18, P. Lévai 46, X. Li 10, X.L. Li 6, J. Lien 120, R. Lietava 100,
I. Likmeta 115, B. Lim 24, S.H. Lim 16, V. Lindenstruth 38, A. Lindner 45, C. Lippmann 97, A. Liu 18,
D.H. Liu 6, J. Liu 118, I.M. Lofnes 20, C. Loizides 87, S. Lokos 107, J. Lömker 59, P. Loncar 33,
J.A. Lopez 94, X. Lopez 126, E. López Torres 7, P. Lu 97,119, J.R. Luhder 125, M. Lunardon 27,
G. Luparello 57, Y.G. Ma 39, A. Maevskaya 140, M. Mager 32, A. Maire 128, M.V. Makariev 36,
M. Malaev 140, G. Malfattore 25, N.M. Malik 91, Q.W. Malik 19, S.K. Malik 91, L. Malinina 141, I.VII,
D. Mal'Kevich 140, D. Mallick 80, N. Mallick 48, G. Mandaglio 30,53, S.K. Mandal 79, V. Manko 140,
F. Manso 126, V. Manzari 50, Y. Mao 6, G.V. Margagliotti 23, A. Margotti 51, A. Marín 97,
C. Markert 108, P. Martinengo 32, J.L. Martinez 115, M.I. Martínez 44, G. Martínez García 103,
M.P.P. Martins 110, S. Masciocchi 97, M. Masera 24, A. Masoni 52, L. Massacrier 130,
A. Mastroserio 131,50, O. Matonoha 75, P.F.T. Matuoka 110, A. Matyja 107, C. Mayer 107,
A.L. Mazuecos 32, F. Mazzaschi 24, M. Mazzilli 32, J.E. Mdhluli 122, A.F. Mechler 64, Y. Melikyan 43,140,
A. Menchaca-Rocha 67, E. Meninno 102, A.S. Menon 115, M. Meres 12, S. Mhlanga 114,68, Y. MIAKE 124,
L. Micheletti 56, L.C. Migliorin 127, D.L. Mihaylov 95, K. Mikhaylov 141,140, A.N. Mishra 46,
D. Miśkowiec 97, A. Modak 4, A.P. Mohanty 59, B. Mohanty 80, M. Mohisin Khan 15, V,
M.A. Molander 43, Z. Moravcova 83, C. Mordasini 95, D.A. Moreira De Godoy 125, I. Morozov 140,
A. Morsch 32, T. Mrnjavac 32, V. Muccifora 49, S. Muhuri 134, J.D. Mulligan 74, A. Mulliri 22,
M.G. Munhoz 110, R.H. Munzer 64, H. Murakami 123, S. Murray 114, L. Musa 32, J. Musinsky 60,
J.W. Myrcha 135, B. Naik 122, A.I. Nambrath 18, B.K. Nandi 47, R. Nania 51, E. Nappi 50,
A.F. Nassirpour 17,75, A. Nath 94, C. Nattrass 121, M.N. Naydenov 36, A. Neagu 19, A. Negru 113,
L. Nellen 65, S.V. Nesbo 34, G. Neskovic 38, D. Nesterov 140, B.S. Nielsen 83, E.G. Nielsen 83,
S. Nikolaev 140, S. Nikulin 140, V. Nikulin 140, F. Noferini 51, S. Noh 11, P. Nomokonov 141,
J. Norman 118, N. Novitzky 124, P. Nowakowski 135, A. Nyanin 140, J. Nystrand 20, M. Ogino 76,
A. Ohlson 75, V.A. Okorokov 140, J. Oleniacz 135, A.C. Oliveira Da Silva 121, M.H. Oliver 137,
A. Onnerstad 116, C. Oppedisano 56, A. Ortiz Velasquez 65, J. Otwinowski 107, M. Oya 92, K. Oyama 76,
Y. Pachmayer 94, S. Padhan 47, D. Pagano 133,55, G. Paić 65, S. Paisano-Guzmán 44, A. Palasciano 50,
S. Panebianco 129, H. Park 124, H. Park 104, J. Park 58, J.E. Parkkila 32, R.N. Patra 91, B. Paul 22,
H. Pei 6, T. Peitzmann 59, X. Peng 6, M. Pennisi 24, L.G. Pereira 66, D. Peresunko 140,
G.M. Perez 7, S. Perrin 129, Y. Pestov 140, V. Petráček 35, V. Petrov 140, M. Petrovici 45,
R.P. Pezzi 103,66, S. Piano 57, M. Pikna 12, P. Pillot 103, O. Pinazza 51,32, L. Pinsky 115, C. Pinto 95,
S. Pisano 49, M. Płoskoń 74, M. Planinic 89, F. Pliquett 64, M.G. Poghosyan 87, B. Polichtchouk 140,
S. Politano 29, N. Poljak 89, A. Pop 45, S. Porteboeuf-Houssais 126, V. Pozdniakov 141, I.Y. Pozos 44,
K.K. Pradhan 48, S.K. Prasad 4, S. Prasad 48, R. Preghenella 51, F. Prino 56, C.A. Pruneau 136,
I. Pshenichnov 140, ID, M. Puccio 32, ID, S. Pucillo 24, ID, Z. Pugelova 106, S. Qiu 84, ID, L. Quaglia 24, ID,
R.E. Quishpe 115, S. Ragoni 14, ID, A. Rakotozafindrabe 129, ID, L. Ramello 132,56, ID, F. Rami 128, ID, T.A. Rancien 73,
M. Rasa 26, ID, S.S. Räsänen 43, ID, R. Rath 51, ID, M.P. Rauch 20, ID, I. Ravasenga 84, ID, K.F. Read 87,121, ID,
C. Reckziegel 112, ID, A.R. Redelbach 38, ID, K. Redlich 79, ID, VI, C.A. Reetz 97, ID, H.D. Regules-Medel 44,
A. Rehman 20, F. Reidt 32, ID, H.A. Reme-Ness 34, ID, Z. Rescakova 37, K. Reygers 94, ID, A. Riabov 140, ID,
V. Riabov 140, ID, R. Ricci 28, ID, M. Richter 19, ID, A.A. Riedel 95, ID, W. Riegler 32, ID, C. Ristea 63, ID, M. Rodríguez
Cahuantzi 44, ID, S.A. Rodríguez Ramírez 44, ID, K. Røed 19, ID, R. Rogalev 140, ID, E. Rogochaya 141, ID,
T.S. Rogoschinski 64, ID, D. Rohr 32, ID, D. Röhrich 20, ID, P.F. Rojas 44, S. Rojas Torres 35, ID, P.S. Rokita 135, ID,
G. Romanenko 141, ID, F. Ronchetti 49, ID, A. Rosano 30,53, ID, E.D. Rosas 65, K. Roslon 135, ID, A. Rossi 54, ID,
A. Roy 48, ID, S. Roy 47, ID, N. Rubini 25, ID, D. Ruggiano 135, ID, R. Rui 23, ID, P.G. Russek 2, ID, R. Russo 84, ID,
A. Rustamov 81, ID, E. Ryabinkin 140, ID, Y. Ryabov 140, ID, A. Rybicki 107, ID, H. Rytkonen 116, ID, W. Rzesa 135, ID,
O.A.M. Saarimaki 43, ID, R. Sadek 103, ID, S. Sadhu 31, ID, S. Sadovsky 140, ID, J. Saetre 20, ID, K. Šafařík 35, ID,
P. Saha 41, S.K. Saha 4, ID, S. Saha 80, ID, B. Sahoo 47, ID, B. Sahoo 48, ID, R. Sahoo 48, ID, S. Sahoo 61, D. Sahu 48, ID,
P.K. Sahu 61, ID, J. Saini 134, ID, K. Sajdakova 37, S. Sakai 124, ID, M.P. Salvan 97, ID, S. Sambyal 91, ID, I. Sanna 32,95, ID,
T.B. Saramela 110, D. Sarkar 136, ID, N. Sarkar 134, P. Sarma 41, ID, V. Sarritzu 22, ID, V.M. Sarti 95, ID,
M.H.P. Sas 137, ID, J. Schambach 87, ID, H.S. Scheid 64, ID, C. Schiaua 45, ID, R. Schicker 94, ID, A. Schmah 94,
C. Schmidt 97, ID, H.R. Schmidt 93, M.O. Schmidt 32, ID, M. Schmidt 93, N.V. Schmidt 87, ID, A.R. Schmier 121, ID,
R. Schotter 128, ID, A. Schröter 38, ID, J. Schukraft 32, ID, K. Schwarz 97, K. Schweda 97, ID, G. Scioli 25, ID,
E. Scomparin 56, ID, J.E. Seger 14, ID, Y. Sekiguchi 123, D. Sekihata 123, ID, I. Selyuzhenkov 97,140, ID,
S. Senyukov 128, ID, J.J. Seo 58, ID, D. Serebryakov 140, ID, L. Šerkšnytė 95, ID, A. Sevcenco 63, ID, T.J. Shaba 68, ID,
A. Shabetai 103, ID, R. Shahoyan 32, A. Shangaraev 140, ID, A. Sharma 90, B. Sharma 91, ID, D. Sharma 47, ID,
H. Sharma 54,107, ID, M. Sharma 91, ID, S. Sharma 76, ID, S. Sharma 91, ID, U. Sharma 91, ID, A. Shatat 130, ID,
O. Sheibani 115, K. Shigaki 92, ID, M. Shimomura 77, J. Shin 11, S. Shirinkin 140, ID, Q. Shou 39, ID, Y. Sibiriak 140, ID,
S. Siddhanta 52, ID, T. Siemiarczuk 79, ID, T.F. Silva 110, ID, D. Silvermyr 75, ID, T. Simantathammakul 105,
R. Simeonov 36, ID, B. Singh 91, B. Singh 95, ID, R. Singh 80, ID, R. Singh 91, ID, R. Singh 48, ID, S. Singh 15, ID,
V.K. Singh 134, ID, V. Singhal 134, ID, T. Sinha 99, ID, B. Sitar 12, ID, M. Sitta 132,56, ID, T.B. Skaali 19,
G. Skorodumovs 94, ID, M. Slupecki 43, ID, N. Smirnov 137, ID, R.J.M. Snellings 59, ID, E.H. Solheim 19, ID,
J. Song 115, ID, A. Songmoolnak 105, C. Sonnabend 32,97, ID, F. Soramel 27, ID, A.B. Soto-hernandez 88, ID,
R. Spijkers 84, ID, I. Sputowska 107, ID, J. Staa 75, ID, J. Stachel 94, ID, I. Stan 63, ID, P.J. Steffanic 121, ID,
S.F. Stiefelmaier 94, ID, D. Stocco 103, ID, I. Storehaug 19, ID, P. Stratmann 125, ID, S. Strazzi 25, ID, C.P. Stylianidis 84,
A.A.P. Suaide 110, ID, C. Suire 130, ID, M. Sukhanov 140, ID, M. Suljic 32, ID, R. Sultanov 140, ID, V. Sumberia 91, ID,
S. Sumowidagdo 82, ID, S. Swain 61, I. Szarka 12, ID, M. Szymkowski 135, ID, S.F. Taghavi 95, ID, G. Taillepied 97, ID,
J. Takahashi 111, ID, G.J. Tambave 80, ID, S. Tang 126,6, ID, Z. Tang 119, ID, J.D. Tapia Takaki 117, ID, N. Tapus 113,
L.A. Tarasovicova 125, ID, M.G. Tarzila 45, ID, G.F. Tassielli 31, ID, A. Tauro 32, ID, G. Tejeda Muñoz 44, ID,
A. Telesca 32, ID, L. Terlizzi 24, ID, C. Terrevoli 115, ID, S. Thakur 4, ID, D. Thomas 108, ID, A. Tikhonov 140, ID,
A.R. Timmins 115, ID, M. Tkacik 106, T. Tkacik 106, ID, A. Toia 64, ID, R. Tokumoto 92, N. Topilskaya 140, ID,
M. Toppi 49, ID, F. Torales-Acosta 18, T. Tork 130, ID, A.G. Torres Ramos 31, ID, A. Trifiró 30,53, ID, A.S. Triolo 32,30,53, ID,
S. Tripathy 51, ID, T. Tripathy 47, ID, S. Trogolo 32, ID, V. Trubnikov 3, ID, W.H. Trzaska 116, ID, T.P. Trzcinski 135, ID,
A. Tumkin 140, ID, R. Turrisi 54, ID, T.S. Tveter 19, ID, K. Ullaland 20, ID, B. Ulukutlu 95, ID, A. Uras 127, ID,
M. Urioni 55,133, ID, G.L. Usai 22, ID, M. Vala 37, N. Valle 21, ID, L.V.R. van Doremalen 59, M. van Leeuwen 84, ID,
C.A. van Veen 94, ID, R.J.G. van Weelden 84, ID, P. Vande Vyvre 32, ID, D. Varga 46, ID, Z. Varga 46, ID,
M. Vasileiou 78, ID, A. Vasiliev 140, ID, O. Vázquez Doce 49, ID, O. Vazquez Rueda 115, ID, V. Vechernin 140, ID,
E. Vercellin<sup>24,10</sup>, S. Vergara Limón<sup>44</sup>, L. Vermunt<sup>97,11</sup>, R. Vértesi<sup>46,12</sup>, M. Verweij<sup>59,13</sup>, L. Vickovic<sup>33</sup>, Z. Vilakazi<sup>122</sup>, O. Villalobos Baillie<sup>100,14</sup>, A. Villani<sup>23,15</sup>, G. Vino<sup>50,16</sup>, A. Vinogradov<sup>140,17</sup>, T. Virgili<sup>28,18</sup>, M.M.O. Virta<sup>116,19</sup>, V. Vislavicius<sup>75</sup>, A. Vodopyanov<sup>141,20</sup>, B. Volkel<sup>32,21</sup>, M.A. Völkl<sup>94,22</sup>, K. Voloshin<sup>140</sup>, S.A. Voloshin<sup>136,23</sup>, G. Volpe<sup>31,24</sup>, B. von Haller<sup>32,25</sup>, I. Vorobyev<sup>95,26</sup>, N. Vozniuk<sup>140,27</sup>, J. Vrláková<sup>37,28</sup>, C. Wang<sup>39,29</sup>, D. Wang<sup>39,30</sup>, Y. Wang<sup>39,31</sup>, A. Wegrzynek<sup>32,32</sup>, F.T. Weiglhofer<sup>38</sup>, S.C. Wenzel<sup>32,33</sup>, J.P. Wessels<sup>125,34</sup>, J. Wiechula<sup>64,35</sup>, J. Wikne<sup>19,36</sup>, G. Wilk<sup>79,37</sup>, J. Wilkinson<sup>97,38</sup>, G.A. Willems<sup>125,39</sup>, B. Windelband<sup>94,40</sup>, M. Winn<sup>129,41</sup>, J.R. Wright<sup>108,42</sup>, W. Wu<sup>39,43</sup>, Y. Wu<sup>119,44</sup>, R. Xu<sup>6,45</sup>, A. Yadav<sup>42,46</sup>, A.K. Yadav<sup>134,47</sup>, S. Yalcin<sup>72,48</sup>, Y. Yamaguchi<sup>92,49</sup>, S. Yang<sup>20,50</sup>, S. Yano<sup>92,51</sup>, Z. Yin<sup>6,52</sup>, I.-K. Yoo<sup>16,53</sup>, J.H. Yoon<sup>58,54</sup>, S. Yuan<sup>20,55</sup>, A. Yuncu<sup>94,55</sup>, V. Zaccolo<sup>23,56</sup>, C. Zampolli<sup>32,57</sup>, F. Zanone<sup>94,58</sup>, N. Zardoshti<sup>32,59</sup>, A. Zarochentsev<sup>140,60</sup>, P. Závada<sup>62,61</sup>, N. Zaviyalov<sup>140</sup>, M. Zhalov<sup>140,62</sup>, B. Zhang<sup>6,63</sup>, L. Zhang<sup>39,64</sup>, S. Zhang<sup>39,65</sup>, X. Zhang<sup>6,66</sup>, Y. Zhang<sup>119</sup>, Z. Zhang<sup>6,67</sup>, M. Zhao<sup>10,68</sup>, V. Zherebchevskii<sup>140,69</sup>, Y. Zhi<sup>10,70</sup>, D. Zhou<sup>6,71</sup>, Y. Zhou<sup>83,72</sup>, J. Zhu<sup>97,6,73</sup>, Y. Zhu<sup>6,74</sup>, S.C. Zugravel<sup>56,75</sup>, N. Zurlo<sup>133,55,76</sup>

<footnote n="1">¹ A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia</footnote>

<footnote n="2">² AGH University of Krakow, Cracow, Poland</footnote>

<footnote n="3">³ Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, Kiev, Ukraine</footnote>

<footnote n="4">⁴ Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS), Kolkata, India</footnote>

<footnote n="5">⁵ California Polytechnic State University, San Luis Obispo, CA, United States</footnote>

<footnote n="6">⁶ Central China Normal University, Wuhan, China</footnote>

<footnote n="7">⁷ Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana, Cuba</footnote>

<footnote n="8">⁸ Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City and Mérida, Mexico</footnote>

<footnote n="9">⁹ Chicago State University, Chicago, IL, United States</footnote>

<footnote n="10">¹⁰ China Institute of Atomic Energy, Beijing, China</footnote>

<footnote n="11">¹¹ Chungbuk National University, Cheongju, Republic of Korea</footnote>

<footnote n="12">¹² Comenius University Bratislava, Faculty of Mathematics, Physics and Informatics, Bratislava, Slovak Republic</footnote>

<footnote n="13">¹³ COMSATS University Islamabad, Islamabad, Pakistan</footnote>

<footnote n="14">¹⁴ Creighton University, Omaha, NE, United States</footnote>

<footnote n="15">¹⁵ Department of Physics, Aligarh Muslim University, Aligarh, India</footnote>

<footnote n="16">¹⁶ Department of Physics, Pusan National University, Pusan, Republic of Korea</footnote>

<footnote n="17">¹⁷ Department of Physics, Sejong University, Seoul, Republic of Korea</footnote>

<footnote n="18">¹⁸ Department of Physics, University of California, Berkeley, CA, United States</footnote>

<footnote n="19">¹⁹ Department of Physics, University of Oslo, Oslo, Norway</footnote>

<footnote n="20">²⁰ Department of Physics and Technology, University of Bergen, Bergen, Norway</footnote>

<footnote n="21">²¹ Dipartimento di Fisica, Università di Pavia, Pavia, Italy</footnote>

<footnote n="22">²² Dipartimento di Fisica dell'Università and Sezione INFN, Cagliari, Italy</footnote>

<footnote n="23">²³ Dipartimento di Fisica dell'Università and Sezione INFN, Trieste, Italy</footnote>

<footnote n="24">²⁴ Dipartimento di Fisica dell'Università and Sezione INFN, Turin, Italy</footnote>

<footnote n="25">²⁵ Dipartimento di Fisica e Astronomia dell'Università and Sezione INFN, Bologna, Italy</footnote>

<footnote n="26">²⁶ Dipartimento di Fisica e Astronomia dell'Università and Sezione INFN, Catania, Italy</footnote>

<footnote n="27">²⁷ Dipartimento di Fisica e Astronomia dell'Università and Sezione INFN, Padova, Italy</footnote>

<footnote n="28">²⁸ Dipartimento di Fisica 'E.R. Caianiello' dell'Università and Gruppo Collegato INFN, Salerno, Italy</footnote>

<footnote n="29">²⁹ Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy</footnote>

<footnote n="30">³⁰ Dipartimento di Scienze MIFT, Università di Messina, Messina, Italy</footnote>

<footnote n="31">³¹ Dipartimento Interateneo di Fisica 'M. Merlin' and Sezione INFN, Bari, Italy</footnote>

<footnote n="32">³² European Organization for Nuclear Research (CERN), Geneva, Switzerland</footnote>

<footnote n="33">³³ Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia</footnote>

<footnote n="34">³⁴ Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway</footnote>

<footnote n="35">³⁵ Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic</footnote>

<footnote n="36">³⁶ Faculty of Physics, Sofia University, Sofia, Bulgaria</footnote>

<footnote n="37">³⁷ Faculty of Science, P.J. Šafárik University, Košice, Slovak Republic</footnote>

<footnote n="38">³⁸ Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany</footnote>

<footnote n="39">³⁹ Fudan University, Shanghai, China</footnote>

<footnote n="40">⁴⁰ Gangneung-Wonju National University, Gangneung, Republic of Korea</footnote>

<footnote n="41">⁴¹ Gauhati University, Department of Physics, Guwahati, India</footnote>

<footnote n="42">⁴² Helmholtz-Institut für Strahlen- und Kernphysik, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany</footnote>

<footnote n="43">⁴³ Helsinki Institute of Physics (HIP), Helsinki, Finland</footnote>

<footnote n="44">⁴⁴ High Energy Physics Group, Universidad Autónoma de Puebla, Puebla, Mexico</footnote>

<footnote n="45">⁴⁵ Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania</footnote>

<footnote n="46">⁴⁶ HUN-REN Wigner Research Centre for Physics, Budapest, Hungary</footnote>

<footnote n="47">⁴⁷ Indian Institute of Technology Bombay (IIT), Mumbai, India</footnote>

<footnote n="48">⁴⁸ Indian Institute of Technology Indore, Indore, India</footnote>

<footnote n="49">⁴⁹ INFN, Laboratori Nazionali di Frascati, Frascati, Italy</footnote>

<footnote n="50">⁵⁰ INFN, Sezione di Bari, Bari, Italy</footnote>

<footnote n="51">⁵¹ INFN, Sezione di Bologna, Bologna, Italy</footnote>

<footnote n="52">⁵² INFN, Sezione di Cagliari, Cagliari, Italy</footnote>

<footnote n="53">⁵³ INFN, Sezione di Catania, Catania, Italy</footnote>

<footnote n="54">⁵⁴ INFN, Sezione di Padova, Padova, Italy</footnote>

<footnote n="55">⁵⁵ INFN, Sezione di Pavia, Pavia, Italy</footnote>

<footnote n="56">⁵⁶ INFN, Sezione di Torino, Turin, Italy</footnote>

<footnote n="57">⁵⁷ INFN, Sezione di Trieste, Trieste, Italy</footnote>
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-Řež, 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 Naționala de Știința și Tehnologie Politehnică București, 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, Département 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
<footnote n="138">138 Yonsei University, Seoul, Republic of Korea</footnote>

<footnote n="139">139 Zentrum für Technologie und Transfer (ZTT), Worms, Germany</footnote>

<footnote n="140">140 Affiliated with an institute covered by a cooperation agreement with CERN</footnote>

<footnote n="141">141 Affiliated with an international laboratory covered by a cooperation agreement with CERN</footnote>

<footnote n="1">¹ Deceased.</footnote>

<footnote>II Also at: Max-Planck-Institut fur Physik, Munich, Germany.</footnote>

<footnote>III Also at: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Bologna, Italy.</footnote>

<footnote>IV Also at: Dipartimento DET del Politecnico di Torino, Turin, Italy.</footnote>

<footnote>V Also at: Department of Applied Physics, Aligarh Muslim University, Aligarh, India.</footnote>

<footnote>VI Also at: Institute of Theoretical Physics, University of Wroclaw, Poland.</footnote>

<footnote>VII Also at: An institution covered by a cooperation agreement with CERN.</footnote>