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

TL;DR

This paper measures how the suppression of charged-particle jets in Pb-Pb collisions at 5.02 TeV depends on the jet radius, using advanced machine learning techniques to improve background mitigation and explore new phase space regions.

Contribution

It introduces a novel machine learning approach for background mitigation and provides the first detailed measurement of radius-dependent jet suppression at low jet $p_T$ in heavy-ion collisions.

Findings

Jet suppression shows mild dependence on collision centrality.

Nuclear modification factor ratios vary with jet radius.

Results help discriminate between jet quenching models.

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 $\sqrt{s_{\rm NN}} = 5.02$ TeV. Charged-particle jets are reconstructed using the anti-$k_{\rm T}$ 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 $p_{\rm T} \geq 40$ 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.

Tables

Table 1. Table 1: Relative systematic uncertainties (%) for jet spectra for 0–10% central Pb–Pb collisions and all resolution parameters. The maximum uncertainties for low p T subscript 𝑝 T p_{\mathrm{T}} ( p T , jet < 50 subscript 𝑝 T jet 50 p_{\rm T,jet}<50 GeV/ c 𝑐 c ) and high p T subscript 𝑝 T p_{\mathrm{T}} ( p T , jet > 50 subscript 𝑝 T jet 50 p_{\rm T,jet}>50 GeV/ c 𝑐 c ) are shown. The direction of asymmetric uncertainties is indicated with a + + or − - sign. The combined uncertainty is the sum in quadrature of individual uncertainties.

Resolution parameter ($R$)	$0.2$		$0.4$		$0.6$
$p_T$	Low $p_T$	High $p_T$	Low $p_T$	High $p_T$	Low $p_T$	High $p_T$
Tracking eff.	21	18	24	12	12	34
Regularization param.	(< 2)	(< 2)	(< 2)	2	2	(< 2)
Unfolding prior	6	16	8	(< 2)	4	(< 2)
Measured $p_T$ range	46	4	8	(< 2)	6	8
Fractional Collinear	+30	+12	+12	+16	+8	+20
Fractional Large Angle	+10	+10	+6	+10	+8	+14
Fractional Medium Response	+28	+14	+20	+14	+22	+14
Quarks/Gluon	-8	-6	-12	-12	-14	-12
Combined	58	32	32	26	28	44

Table 2. Table 2: Relative systematic uncertainties (%) for jet spectra for 30–50% central Pb–Pb collisions and all resolution parameters. The maximum uncertainties for low p T subscript 𝑝 T p_{\mathrm{T}} ( p T , jet < 50 subscript 𝑝 T jet 50 p_{\rm T,jet}<50 GeV/ c 𝑐 c ) and high p T subscript 𝑝 T p_{\mathrm{T}} ( p T , jet > 50 subscript 𝑝 T jet 50 p_{\rm T,jet}>50 GeV/ c 𝑐 c ) are shown. The direction of asymmetric uncertainties is indicated with a + + or − - sign. The combined uncertainty is the sum in quadrature of individual uncertainties.

Resolution parameter ($R$)	$0.2$		$0.4$		$0.6$
$p_T$	Low $p_T$	High $p_T$	Low $p_T$	High $p_T$	Low $p_T$	High $p_T$
Tracking eff.	10	12	12	16	12	14
Regularization param.
Unfolding prior		4	6	2		6
Measured $p_T$ range	28		10		20
Fractional Collinear	+12	+6	+22	+14	+26	+24
Fractional Large Angle	+8	+8	+8	+10	+26	+24
Fractional Medium Response	+8	+8	+14	+10	+22	+20
Quarks/Gluon	-8	-6	-8	-6	-12	-6
Combined	34	22	32	26	42	40

Table 3. Table 3: Relative systematic uncertainties (%) for jet spectra for 60–80% central Pb–Pb  collisions and all used resolution parameters. The maximum uncertainties for low p T subscript 𝑝 T p_{\mathrm{T}} ( p T , jet < 50 subscript 𝑝 T jet 50 p_{\rm T,jet}<50 GeV/ c 𝑐 c ) and high p T subscript 𝑝 T p_{\mathrm{T}} ( p T , jet > 50 subscript 𝑝 T jet 50 p_{\rm T,jet}>50 GeV/ c 𝑐 c ) are shown. In this centrality interval the spectra are measured with the area-based method, the uncertainties related to the fragmentation functions adopted in the machine learning algorithm are not included for this case. The combined uncertainty is the sum in quadrature of individual uncertainties.

Resolution parameter ($R$)	$0.2$		$0.4$		$0.6$
$p_T$	Low $p_T$	High $p_T$	Low $p_T$	High $p_T$	Low $p_T$	High $p_T$
Tracking eff.	4	10	2	10	10	14
Regularization param.
Unfolding prior	6		10	6		8
Measured $p_T$ range	32	4	6	4	36	14
Quarks/Gluon	-2	-4	-4	-4		-4
Combined	46	14	14	10	50	16

Equations

$$R_{\mathrm{AA}}=\frac{1}{⟨T_{\mathrm{AA}}⟩}\frac{{\mathrm{d}}^{2}N/\mathrm{d}{p}_{\mathrm{T}}\mathrm{d}\eta }{{\mathrm{d}}^2{\sigma }_{\mathrm{pp}}/\mathrm{d}{p}_{\mathrm{T}}\mathrm{d}\eta },$$

$${\epsilon }_{\mathrm{rec}}(p_{\mathrm{T},\mathrm{ch}\mathrm{jet}}^{\mathrm{true}})=N_{\mathrm{matched}}(p_{\mathrm{T},\mathrm{ch}\mathrm{jet}}^{\mathrm{true}})/N_{\mathrm{generated}}(p_{\mathrm{T},\mathrm{ch}\mathrm{jet}}^{\mathrm{true}}),$$

$$R_{\mathrm{mod}}=\frac{{\text{Y}}_{\text{modified}}}{{\text{Y}}_{\text{unmodified}}},$$

$$\frac{1}{⟨T_{\mathrm{AA}}⟩}\frac{{\mathrm{d}}^2{N}_{\mathrm{ch}\mathrm{jet}}}{\mathrm{d}{p}_{\mathrm{T},\mathrm{ch}\mathrm{jet}}\mathrm{d}{\eta }_{\mathrm{jet}}}[\text{mb}(\mathrm{GeV}/c{)}^{-1}].$$

$$\sigma(R=R_{1})/\sigma(R=R_{2})=\frac{\mathrm{d}N_{\mathrm{R_{1}}}}{\mathrm{d}p_{\mathrm{T,\;ch\;jet}}}\Big{/}\frac{\mathrm{d}N_{\mathrm{R_{2}}}}{\mathrm{d}p_{\mathrm{T,\;ch\;jet}}}.$$

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

$$p_{\mathrm{T},\mathrm{jet}}^{\mathrm{target}}=p_{\mathrm{T},\mathrm{jet}}^{\mathrm{raw}}\sum _i{p}_{\mathrm{T},\mathrm{const}i}^{\mathrm{PYTHIA}}/\sum _i{p}_{\mathrm{T},\mathrm{const}i},$$

Full text

\PHyear2023 \PHnumber027 \PHdate28 February \ShortTitle$R$-dependence of jet suppression \CollaborationALICE Collaboration††thanks: See Appendix C for the list of collaboration members \ShortAuthorALICE Collaboration

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 $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV. Charged-particle jets are reconstructed using the anti-$k_{\rm T}$ 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 $p_{\mathrm{T}}\geq 40$ 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, 2, 3, 4] and at the Large Hadron Collider (LHC) [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 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 ($p_{\mathrm{T}}$) 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, 19, 20, 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-$p_{\mathrm{T}}$ 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-$p_{\mathrm{T}}$ hadrons and reconstructed jets, semi-inclusive jet measurements, jet shapes, and recently via jet substructure measurements at RHIC [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36] and at the LHC [15, 37, 16, 7, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56].

Jet energy loss results in a suppression of the jet yield at a fixed value of the jet $p_{\mathrm{T}}$. Jet suppression is quantified using the nuclear modification factor,

[TABLE]

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 $T_{\mathrm{AA}}$ [57]. The value of $R_{\rm AA}$ 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 $p_{\mathrm{T}}$ 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, 59, 60, 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 transport properties of the QGP. While jet suppression has been measured over a large range in jet $p_{\mathrm{T}}{}$ at both the LHC and RHIC [62, 63, 64, 65, 66], of particular interest are the low-$p_{\mathrm{T}}$ and large-$R$ regions. A recent measurement by the CMS collaboration [67] studied jet suppression up to $R=1.0$ for jets with high $p_{\mathrm{T}}>200$ 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 ($R_{\rm CP}$) at lower $p_{\mathrm{T}}$, $40<p_{\mathrm{T}}<200$ 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, 60, 61, 59] at low $p_{\mathrm{T}}$ 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 $p_{\mathrm{T}}$ 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 $p_{\mathrm{T}}$ 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 $p_{\mathrm{T}}$ itself. Jet measurements in heavy-ion collisions require a procedure to account for this background which involves both a correction of the jet $p_{\mathrm{T}}$ and a suppression of combinatorial (or fake) jets.

One common procedure applied for the jet $p_{\mathrm{T}}$-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 $p_{\mathrm{T}}$ 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 $p_{\mathrm{T}}$ 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 $p_{\mathrm{T}}{}$ due to the improved jet $p_{\rm T}$ 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 $p_{\mathrm{T}}$, which may also provide the opportunity to measure jets in Pb–Pb collisions with lower jet $p_{\mathrm{T}}$ 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 $\sqrt{s_{\mathrm{NN}}}$ = 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 $R_{\mathrm{AA}}$ 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 $p_{\mathrm{T}}$ and larger $R$, measuring jets down to $p_{\mathrm{T}}=30$ GeV/$c$ for $R=0.4$ and to $p_{\mathrm{T}}=40$ 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 $\sqrt{s_{\mathrm{NN}}}=5.02$ 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 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 $p_{\mathrm{T}}>0.15$ 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 $p_{\mathrm{T}}=0.15$ GeV/$c$, rising to approximately 84% at track $p_{\mathrm{T}}=1$ GeV/$c$ and remaining above 75% at higher track $p_{\mathrm{T}}$ [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 $p_{\mathrm{T}}$. 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 $p_{\rm T}$ of $1$ GeV/$c$ and about $4$% at $50$ GeV/$c$. In heavy-ion collisions, the momentum resolution at high track $p_{\rm T}$ 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_{\rm T}=$140 GeV/$c$.

The values for the $\langle T_{\mathrm{AA}}\rangle$ 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$\pm 0.0106$ $\text{mb}^{-1}$ 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-$k_{\mathrm{T}}$ algorithm with $E$-scheme recombination [86, 87] in the $\mathrm{FastJet}$ 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 $|\eta_{\rm jet}|<0.9-R$ to assure that the nominal jet cone is fully contained within the track acceptance of $|\eta|<0.9$. A jet area cut of $A_{\mathrm{jet}}>0.56\pi R^{2}$ is applied to suppress contamination by non-physical jets [63, 36, 49]. Jets containing a track with $p_{\rm T}>100$ 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 $p_{\rm T}$ 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 $p_{\rm T}$.

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 $p_{\rm T}$. 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 $p_{\rm T}$, 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 $p_{\rm T}^{\rm true}$ interval,

[TABLE]

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 $p_{\mathrm{T}}$ intervals, while lower efficiency regions are rejected.

4 Machine learning-based background correction

To expand the jet $p_{\mathrm{T}}$ and $R$ reach of the measurement, a novel estimator based on machine learning is used to correct the $p_{\mathrm{T}}$-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 $p_{\mathrm{T}}$ of the jet. While the background is dominated by low-$p_{\mathrm{T}}$ particles, the particles in the jet signal are distributed towards higher $p_{\mathrm{T}}$ constituents [71, 92]. However, the relationship between the relevant input features of the jet candidate and the true jet $p_{\mathrm{T}}$ 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 $p_{\mathrm{T}}$ 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 ($\delta p_{\mathrm{T}}$) between background-corrected $p_{\mathrm{T}}$ and target detector-level probe $p_{\mathrm{T}}$, which measures how precisely the background is approximated. The plot compares $\delta p_{\mathrm{T}}$ 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 $\delta p_{\mathrm{T}}$ 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.

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 [93, 53, 94, 54]. 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 $p_{\rm T}$ 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, $z=\frac{p_{\rm T,track}}{p_{\rm T,jet}}$. 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 $p_{\text{loss}}$, the probability of branching off a particle; $f_{\text{loss}}$, the fraction of the jet constituent $p_{\rm T}$ 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 $p_{\text{loss}}$, carrying $p_{\mathrm{T}}\ =f_{\text{loss}}p_{\mathrm{T}}^{\text{const.}}$ 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:

Quark Only: jets originating from a quark in the PYTHIA 8 simulation are used. 2. 2.

Gluon Only: jets originating from a gluon in the PYTHIA 8 simulation are used. 3. 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. 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. 5.

Medium Response: the emission occurs as described for the Fractional Collinear case, but the original jet constituent $p_{\mathrm{T}}$ 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 guided the values of the tunable parameters used in the phenomenological modifications. Specifically, the $p_{\rm loss}$ 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 $p_{\rm loss}$ 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 fact that the $p_{\rm loss}$ 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 $p_{\rm loss}$ for the Fractional Large Angle model, and the excess inside of the jet cone was used to fix the value of $p_{\rm loss}$ for the Fractional Collinear and Medium Response models. The modifications were then compared to existing fragmentation measurements, as discussed below. The values of $f_{\rm loss}$ were set to 25% and 10%, which provide a charged hadron $R_{\rm AA}$ 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, $R_{\rm mod}$,

[TABLE]

shown for $R=0.4$ jets as a function of $p_{\mathrm{T}}$ in the left panel of Fig. 2. The medium response adds energy to the jet cone, resulting in $R_{\rm mod}>1$. For the fractional collinear model, the jet does not lose energy most of the time, which results in $R_{\rm mod}\approx 1$. In the case of fractional large-angle radiation, the jet will lose energy, resulting in $R_{\rm mod}<1$. 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 $p_{\mathrm{T}}{}>100$ GeV/$c$ from ATLAS [53] and $R=0.3$ photon-tagged jets with $p_{\mathrm{T}}{}>30$ 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 $100<p_{\rm T}<200$ 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 $40<p_{\rm T}<100$ 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 $p_{\rm T}$, 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 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 $p_{\mathrm{T}}$, which corresponds to a significant reduction in measured yield in a given $p_{\rm T}$ 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 $\pm 1$ are taken into account.

Prior: for the Bayesian unfolding procedure, a prior distribution is needed. The PYTHIA 8 jet $p_{\mathrm{T}}$ 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 $p_{\mathrm{T}}$-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-$p_{\mathrm{T}}$ cut-off of the data that serves as input to the unfolding procedure is varied by $\pm 5$ GeV/$c$. Small-$R$ jets are expected to be most sensitive to this cut due to their low $p_{\rm T,jet}$ 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 $\pm 1$ 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 Tabs. 1, 2, and 3 for 0–10%, 30–50%, and 60–80% Pb–Pb collisions, respectively. The uncertainties of the $R_{\mathrm{AA}}$ 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 $R_{\mathrm{AA}}$ uncertainty is an additional uncertainty associated with the calculation of the $\left<T_{\mathrm{AA}}\right>$, given in Ref. [85]. The systematic uncertainties on the $R_{\rm AA}$ 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 $R_{\rm AA}$ double ratios and the cross section ratios.

7 Results

In this section, the $p_{\mathrm{T}}$-differential inclusive charged-particle jet production yields (Fig. 3), the nuclear modification factors $R_{\mathrm{AA}}$ (Figs. 4, 5, 6), the ratios of the jet cross sections for different $R$ (Fig. 7), and the $R_{\mathrm{AA}}$ double ratios representing the change of nuclear modification with respect to resolution parameter of $R=0.2$ (Fig. 8) are reported.

The $p_{\mathrm{T}}$-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 $p_{\rm T}$. 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 $\left<T_{\mathrm{AA}}\right>$ [85] of the given centrality class

[TABLE]

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 $\left<T_{\mathrm{AA}}\right>$ 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 [49, 36, 62].

Figure 4 shows the nuclear modification factors using both the new ML-based estimator and the established area-based estimator for the 0–10%, 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.

Figure 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 [101, 61] 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 $p_{\rm T}$. 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 $p_{\rm T}$ 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:

[TABLE]

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 [109, 64, 66, 19, 63, 62]. 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 $\sigma(R=0.2)$/$\sigma(R=0.6)$ 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 $p_{\rm T}$-dependence in the jet cross section ratios in pp collisions, which becomes stronger at lower jet $p_{\rm T}$, resulting from the $p_{\rm T}$ 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 $p_{\rm T}$. This indicates that there is an $R$-dependence to the evolution of the jet cross section in pp collisions with $p_{\rm T}$ which may impact the dependence of the $R_{\rm AA}$ on $R$ and $p_{\rm T}$, as discussed below.

The double ratio of the nuclear modification factor, which compares $R_{\mathrm{AA}}^{R}$ for different $R$ to $R_{\mathrm{AA}}^{R=0.2}$, is used to quantify the variation of the nuclear modification factor with respect to the jet resolution parameter. It is defined as

[TABLE]

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 $R_{\rm AA}$ 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 $R_{\rm AA}$ double ratios are shown in Fig. 8 for 0–10% and 30–50% central Pb–Pb collisions. The $R_{\mathrm{AA}}^{0.4/0.2}$ ratio in 0–10% and 30–50% central collisions and the $R_{\mathrm{AA}}^{0.6/0.2}$ ratio in 30–50% central collisions are consistent with unity, indicating no significant $R$-dependence or $p_{\mathrm{T}}{}$-dependence. In contrast, the $R_{\mathrm{AA}}^{0.6/0.2}$ ratio in 0–10% central collisions is below unity at lower jet $p_{\mathrm{T}}$ 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 $R_{\mathrm{AA}}^{0.6/0.2}$ ratio, and approximately 1.6 (1.0) sigma for $R_{\mathrm{AA}}^{0.4/0.2}$ 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 the $R_{\rm AA}$ 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 $p_{\rm T}$ compared to narrower jets. The larger $R$ jets at a given $p_{\mathrm{T}}$ 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 $R_{\rm AA}$ 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 $R_{\rm AA}$ 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 previously compared to the jet $R_{\rm AA}$, 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 $R_{\rm AA}$ 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 $p_{\rm T}$ 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 $R_{\rm AA}$ 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 $p_{\rm T}$-dependence to the substructure of the jet [21]. Additionally, quenching effects are expected to be larger at lower jet $p_{\rm T}$. The double ratio in Fig. 8 is in contrast with the $R_{\rm cp}$ 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 $\sim$ 3% difference in energy loss between $R=0.6$ and $R=0.2$ is needed to create a difference in the $R_{\rm AA}$ 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, Tabs. 1, 2, and 3). Still, this method allowed for the measurement of $R=0.6$ jets down to a low jet $p_{\mathrm{T}}$ 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 $\sqrt{s_{\mathrm{NN}}}=5.02$ 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.

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), Cubaenergía, 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’Energie 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 Schwerionenforschung 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) and Dirección General de Asuntos del Personal Academico (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 (COMSATS), 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 Stiinta si Tehnologie Politehnica Bucuresti, 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), European Union; Academy of Finland (Center of Excellence in Quark Matter) (grant nos. 346327, 346328), Finland; Programa de Apoyos para la Superación del Personal Académico, UNAM, Mexico.

Appendix A Machine learning methods

A novel background estimator based on machine learning is used to correct the $p_{\mathrm{T}}$-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 $p_{\mathrm{T}}$ value for each jet candidate. Following this approach, 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 network model, which (as in Ref. [73]) demonstrates a slightly improved performance compared to other explored algorithms. This shallow neural network is implemented as a three-layer perceptron with 100 nodes in the first two layers and 50 in the last. The activation function chosen 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 applicability 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 ranging from [math] 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 $p_{\mathrm{T}}{}$. For particles with $p_{\mathrm{T}}\geq$ 4 GeV/$c$, the transverse momentum 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 properties 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 unimportant 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 unimportant 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 transverse momentum was excluded from this list due to its correlation with the area-based corrected transverse momentum. Based on these considerations, the following input features were selected: the jet transverse momentum corrected by the standard area-based method, the first radial moment of constituent momenta (jet angularity), the number of constituents within the jet, and the transverse momenta of the eight leading (highest $p_{\mathrm{T}}$) particles within the jet. For the training, the applied 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 $p_{\mathrm{T}}$ at detector level, $p^{\rm target}_{\rm T,jet}$. 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,

[TABLE]

where ${p}_{\mathrm{T,\,jet}}^{\mathrm{raw}}$ is the reconstructed jet transverse momentum before any background correction. With this definition of the target jet $p_{\mathrm{T}}$, the background is also defined implicitly; it consists of all the particles from the thermal model. As an alternative definition, the target jet $p_{\mathrm{T}}$ could also be defined as the detector-level jet $p_{\mathrm{T}}$ 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 disadvantages, 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 similar 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 respect 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 $\Delta\phi=\phi_{\rm jet}-\psi_{\rm EP}$, where $\psi_{\rm EP}$ represents the event plane orientation. Jets are classified as in-plane $\Delta\phi<30^{o}$, mid-plane ( $60^{o}>\Delta\phi>30^{o}$), or out-of-plane ($\Delta\phi>60^{o}$). The AB method does not correct explicitly for flow-like effects, which manifests itself as a dependence of the $\delta p_{\rm T}$ distributions on $\Delta\phi$. This can be seen in the dashed lines in Fig. 9, where the mean of the $\delta p_{\rm T}$ 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. 9, where the $\delta p_{\rm T}$ 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.

Appendix C The ALICE Collaboration

S. Acharya  126, D. Adamová  86, A. Adler70, G. Aglieri Rinella  32, M. Agnello  29, N. Agrawal  51, Z. Ahammed  134, S. Ahmad  15, S.U. Ahn  71, I. Ahuja  37, A. Akindinov  140, M. Al-Turany  97, D. Aleksandrov  140, B. Alessandro  56, H.M. Alfanda  6, R. Alfaro Molina  67, B. Ali  15, A. Alici  25, N. Alizadehvandchali  115, A. Alkin  32, J. Alme  20, G. Alocco  52, T. Alt  64, I. Altsybeev  140, J.R. Alvarado  44, M.N. Anaam  6, C. Andrei  45, A. Andronic  125, V. Anguelov  94, F. Antinori  54, P. Antonioli  51, N. Apadula  74, L. Aphecetche  103, H. Appelshäuser  64, C. Arata  73, S. Arcelli  25, M. Aresti  52, R. Arnaldi  56, J.G.M.C.A. Arneiro  110, I.C. Arsene  19, M. Arslandok  137, A. Augustinus  32, R. Averbeck  97, M.D. Azmi  15, A. Badalà  53, J. Bae  104, Y.W. Baek  40, X. Bai  119, R. Bailhache  64, Y. Bailung  48, A. Balbino  29, A. Baldisseri  129, B. Balis  2, D. Banerjee  4, Z. Banoo  91, R. Barbera  26, F. Barile  31, L. Barioglio  95, M. Barlou78, G.G. Barnaföldi  46, L.S. Barnby  85, V. Barret  126, L. Barreto  110, C. Bartels  118, K. Barth  32, E. Bartsch  64, N. Bastid  126, S. Basu  75, G. Batigne  103, D. Battistini  95, B. Batyunya  141, D. Bauri47, J.L. Bazo Alba  101, I.G. Bearden  83, C. Beattie  137, P. Becht  97, D. Behera  48, I. Belikov  128, A.D.C. Bell Hechavarria  125, F. Bellini  25, R. Bellwied  115, S. Belokurova  140, V. Belyaev  140, G. Bencedi  46, S. Beole  24, Y. Berdnikov  140, A. Berdnikova  94, L. Bergmann  94, M.G. Besoiu  63, L. Betev  32, P.P. Bhaduri  134, A. Bhasin  91, M.A. Bhat  4, B. Bhattacharjee  41, L. Bianchi  24, N. Bianchi  49, J. Bielčík  35, J. Bielčíková  86, J. Biernat  107, A.P. Bigot  128, A. Bilandzic  95, G. Biro  46, S. Biswas  4, N. Bize  103, J.T. Blair  108, D. Blau  140, M.B. Blidaru  97, N. Bluhme38, C. Blume  64, G. Boca  21,55, F. Bock  87, T. Bodova  20, A. Bogdanov140, S. Boi  22, J. Bok  58, L. Boldizsár  46, M. Bombara  37, P.M. Bond  32, G. Bonomi  133,55, H. Borel  129, A. Borissov  140, A.G. Borquez Carcamo  94, H. Bossi  137, E. Botta  24, Y.E.M. Bouziani  64, L. Bratrud  64, P. Braun-Munzinger  97, M. Bregant  110, M. Broz  35, G.E. Bruno  96,31, M.D. Buckland  23, D. Budnikov  140, H. Buesching  64, S. Bufalino  29, P. Buhler  102, Z. Buthelezi  68,122, A. Bylinkin  20, S.A. Bysiak107, M. Cai  6, H. Caines  137, A. Caliva  97, E. Calvo Villar  101, J.M.M. Camacho  109, P. Camerini  23, F.D.M. Canedo  110, S.L. Cantway  137, M. Carabas  113, A.A. Carballo  32, F. Carnesecchi  32, R. Caron  127, L.A.D. Carvalho  110, J. Castillo Castellanos  129, F. Catalano  32,24, C. Ceballos Sanchez  141, I. Chakaberia  74, P. Chakraborty  47, S. Chandra  134, S. Chapeland  32, M. Chartier  118, S. Chattopadhyay  134, S. Chattopadhyay  99, T. Cheng  97,6, C. Cheshkov  127, B. Cheynis  127, V. Chibante Barroso  32, D.D. Chinellato  111, E.S. Chizzali  II,95, J. Cho  58, S. Cho  58, P. Chochula  32, P. Christakoglou  84, C.H. Christensen  83, P. Christiansen  75, T. Chujo  124, M. Ciacco  29, C. Cicalo  52, F. Cindolo  51, M.R. Ciupek97, G. ClaiIII,51, F. Colamaria  50, J.S. Colburn100, D. Colella  96,31, M. Colocci  25, M. Concas  IV,56, G. Conesa Balbastre  73, Z. Conesa del Valle  130, G. Contin  23, J.G. Contreras  35, M.L. Coquet  129, T.M. CormierI,87, P. Cortese  132,56, M.R. Cosentino  112, F. Costa  32, S. Costanza  21,55, C. Cot  130, J. Crkovská  94, P. Crochet  126, R. Cruz-Torres  74, P. Cui  6, A. Dainese  54, M.C. Danisch  94, A. Danu  63, P. Das  80, P. Das  4, S. Das  4, A.R. Dash  125, S. Dash  47, A. De Caro  28, G. de Cataldo  50, J. de Cuveland38, A. De Falco  22, D. De Gruttola  28, N. De Marco  56, C. De Martin  23, S. De Pasquale  28, R. Deb  133, S. Deb  48, R.J. Debski  2, K.R. Deja135, R. Del Grande  95, L. Dello Stritto  28, W. Deng  6, P. Dhankher  18, D. Di Bari  31, A. Di Mauro  32, B. Diab  129, R.A. Diaz  141,7, T. Dietel  114, Y. Ding  6, R. Divià  32, D.U. Dixit  18, Ø. Djuvsland20, U. Dmitrieva  140, A. Dobrin  63, B. Dönigus  64, J.M. Dubinski  135, A. Dubla  97, S. Dudi  90, P. Dupieux  126, M. Durkac106, N. Dzalaiova12, T.M. Eder  125, R.J. Ehlers  74, F. Eisenhut  64, D. Elia  50, B. Erazmus  103, F. Ercolessi  25, F. Erhardt  89, M.R. Ersdal20, B. Espagnon  130, G. Eulisse  32, D. Evans  100, S. Evdokimov  140, L. Fabbietti  95, M. Faggin  27, J. Faivre  73, F. Fan  6, W. Fan  74, A. Fantoni  49, M. Fasel  87, P. Fecchio29, A. Feliciello  56, G. Feofilov  140, A. Fernández Téllez  44, L. Ferrandi  110, M.B. Ferrer  32, A. Ferrero  129, C. Ferrero  56, A. Ferretti  24, V.J.G. Feuillard  94, V. Filova  35, D. Finogeev  140, F.M. Fionda  52, F. Flor  115, A.N. Flores  108, S. Foertsch  68, I. Fokin  94, S. Fokin  140, E. Fragiacomo  57, E. Frajna  46, U. Fuchs  32, N. Funicello  28, C. Furget  73, A. Furs  140, T. Fusayasu  98, J.J. Gaardhøje  83, M. Gagliardi  24, A.M. Gago  101, C.D. Galvan  109, D.R. Gangadharan  115, P. Ganoti  78, C. Garabatos  97, T. García Chávez  44, E. Garcia-Solis  9, C. Gargiulo  32, K. Garner125, P. Gasik  97, A. Gautam  117, M.B. Gay Ducati  66, M. Germain  103, A. Ghimouz124, C. Ghosh134, M. Giacalone  51,25, P. Giubellino  97,56, P. Giubilato  27, A.M.C. Glaenzer  129, P. Glässel  94, E. Glimos  121, D.J.Q. Goh76, V. Gonzalez  136, M. Gorgon  2, S. Gotovac33, V. Grabski  67, L.K. Graczykowski  135, E. Grecka  86, A. Grelli  59, C. Grigoras  32, V. Grigoriev  140, S. Grigoryan  141,1, F. Grosa  32, J.F. Grosse-Oetringhaus  32, R. Grosso  97, D. Grund  35, G.G. Guardiano  111, R. Guernane  73, M. Guilbaud  103, K. Gulbrandsen  83, T. Gündem  64, T. Gunji  123, W. Guo  6, A. Gupta  91, R. Gupta  91, R. Gupta  48, K. Gwizdziel  135, L. Gyulai  46, R. Haake  137, M.K. Habib97, C. Hadjidakis  130, F.U. Haider  91, H. Hamagaki  76, A. Hamdi  74, M. Hamid6, Y. Han  138, R. Hannigan  108, J. Hansen  75, M.R. Haque  135, J.W. Harris  137, A. Harton  9, H. Hassan  87, D. Hatzifotiadou  51, P. Hauer  42, L.B. Havener  137, S.T. Heckel  95, E. Hellbär  97, H. Helstrup  34, M. Hemmer  64, T. Herman  35, G. Herrera Corral  8, F. Herrmann125, S. Herrmann  127, K.F. Hetland  34, B. Heybeck  64, H. Hillemanns  32, B. Hippolyte  128, F.W. Hoffmann  70, B. Hofman  59, B. Hohlweger  84, G.H. Hong  138, M. Horst  95, A. Horzyk  2, Y. Hou  6, P. Hristov  32, C. Hughes  121, P. Huhn64, L.M. Huhta  116, T.J. Humanic  88, A. Hutson  115, D. Hutter  38, R. Ilkaev140, H. Ilyas  13, M. Inaba  124, G.M. Innocenti  32, M. Ippolitov  140, A. Isakov  86, T. Isidori  117, M.S. Islam  99, M. Ivanov12, M. Ivanov  97, V. Ivanov  140, M. Jablonski  2, B. Jacak  74, N. Jacazio  32, P.M. Jacobs  74, S. Jadlovska106, J. Jadlovsky106, S. Jaelani  82, C. Jahnke  111, M.J. Jakubowska  135, M.A. Janik  135, T. Janson70, M. Jercic89, S. Jia  10, A.A.P. Jimenez  65, F. Jonas  87,125, J.M. Jowett   32,97, J. Jung  64, M. Jung  64, A. Junique  32, A. Jusko  100, M.J. Kabus  32,135, J. Kaewjai105, P. Kalinak  60, A.S. Kalteyer  97, A. Kalweit  32, V. Kaplin  140, A. Karasu Uysal  72, D. Karatovic  89, O. Karavichev  140, T. Karavicheva  140, P. Karczmarczyk  135, E. Karpechev  140, U. Kebschull  70, R. Keidel  139, D.L.D. Keijdener59, M. Keil  32, B. Ketzer  42, S.S. Khade  48, A.M. Khan  119,6, S. Khan  15, A. Khanzadeev  140, Y. Kharlov  140, A. Khatun  117,15, A. Khuntia  107, M.B. Kidson114, B. Kileng  34, B. Kim  104, C. Kim  16, D.J. Kim  116, E.J. Kim  69, J. Kim  138, J.S. Kim  40, J. Kim  69, M. Kim  18, S. Kim  17, T. Kim  138, K. Kimura  92, S. Kirsch  64, I. Kisel  38, S. Kiselev  140, A. Kisiel  135, J.P. Kitowski  2, J.L. Klay  5, J. Klein  32, S. Klein  74, C. Klein-Bösing  125, M. Kleiner  64, T. Klemenz  95, A. Kluge  32, A.G. Knospe  115, C. Kobdaj  105, T. Kollegger97, A. Kondratyev  141, N. Kondratyeva  140, E. Kondratyuk  140, J. Konig  64, 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. Kumaoka124, D. Kumar134, L. Kumar  90, N. Kumar90, S. Kumar  31, S. Kundu  32, P. Kurashvili  79, A. Kurepin  140, A.B. Kurepin  140, A. Kuryakin  140, S. 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. Lakrathok105, 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. Lelek2, R.C. Lemmon  85, I. León Monzón  109, M.M. Lesch  95, E.D. Lesser  18, P. Lévai  46, X. Li10, X.L. Li6, J. Lien  120, R. Lietava  100, I. Likmeta  115, B. Lim  24, S.H. Lim  16, V. Lindenstruth  38, A. Lindner45, 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. Maevskaya140, M. Mager  32, A. Maire  128, M.V. Makariev  36, M. Malaev  140, G. Malfattore  25, N.M. Malik  91, Q.W. Malik19, S.K. Malik  91, L. Malinina  I,VII,141, 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. Martinez115, 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. Matuoka110, A. Matyja  107, C. Mayer  107, A.L. Mazuecos  32, F. Mazzaschi  24, M. Mazzilli  32, J.E. Mdhluli  122, A.F. Mechler64, Y. Melikyan  43,140, A. Menchaca-Rocha  67, E. Meninno  102, A.S. Menon  115, M. Meres  12, S. Mhlanga114,68, Y. Miake124, L. Micheletti  56, L.C. Migliorin127, D.L. Mihaylov  95, K. Mikhaylov  141,140, A.N. Mishra  46, D. Miśkowiec  97, A. Modak  4, A.P. Mohanty  59, B. Mohanty80, M. Mohisin Khan  V,15, 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. Neagu19, A. Negru113, L. Nellen  65, S.V. Nesbo34, 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. Oya92, 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. Patra91, 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. Pestov140, 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. Pinsky115, C. Pinto  95, S. Pisano  49, M. Płoskoń  74, M. Planinic89, F. Pliquett64, 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, M. Puccio  32, S. Pucillo  24, Z. Pugelova106, S. Qiu  84, L. Quaglia  24, R.E. Quishpe115, S. Ragoni  14, A. Rakotozafindrabe  129, L. Ramello  132,56, F. Rami  128, T.A. Rancien73, M. Rasa  26, S.S. Räsänen  43, R. Rath  51, M.P. Rauch  20, I. Ravasenga  84, K.F. Read  87,121, C. Reckziegel  112, A.R. Redelbach  38, K. Redlich  VI,79, C.A. Reetz  97, H.D. Regules-Medel44, A. Rehman20, F. Reidt  32, H.A. Reme-Ness  34, Z. Rescakova37, K. Reygers  94, A. Riabov  140, V. Riabov  140, R. Ricci  28, M. Richter  19, A.A. Riedel  95, W. Riegler  32, C. Ristea  63, M. Rodríguez Cahuantzi  44, S.A. Rodríguez Ramírez  44, K. Røed  19, R. Rogalev  140, E. Rogochaya  141, T.S. Rogoschinski  64, D. Rohr  32, D. Röhrich  20, P.F. Rojas44, S. Rojas Torres  35, P.S. Rokita  135, G. Romanenko  141, F. Ronchetti  49, A. Rosano  30,53, E.D. Rosas65, K. Roslon  135, A. Rossi  54, A. Roy  48, S. Roy  47, N. Rubini  25, D. Ruggiano  135, R. Rui  23, P.G. Russek  2, R. Russo  84, A. Rustamov  81, E. Ryabinkin  140, Y. Ryabov  140, A. Rybicki  107, H. Rytkonen  116, W. Rzesa  135, O.A.M. Saarimaki  43, R. Sadek  103, S. Sadhu  31, S. Sadovsky  140, J. Saetre  20, K. Šafařík  35, P. Saha41, S.K. Saha  4, S. Saha  80, B. Sahoo  47, B. Sahoo  48, R. Sahoo  48, S. Sahoo61, D. Sahu  48, P.K. Sahu  61, J. Saini  134, K. Sajdakova37, S. Sakai  124, M.P. Salvan  97, S. Sambyal  91, I. Sanna  32,95, T.B. Saramela110, D. Sarkar  136, N. Sarkar134, P. Sarma  41, V. Sarritzu  22, V.M. Sarti  95, M.H.P. Sas  137, J. Schambach  87, H.S. Scheid  64, C. Schiaua  45, R. Schicker  94, A. Schmah94, C. Schmidt  97, H.R. Schmidt93, M.O. Schmidt  32, M. Schmidt93, N.V. Schmidt  87, A.R. Schmier  121, R. Schotter  128, A. Schröter  38, J. Schukraft  32, K. Schwarz97, K. Schweda  97, G. Scioli  25, E. Scomparin  56, J.E. Seger  14, Y. Sekiguchi123, D. Sekihata  123, I. Selyuzhenkov  97,140, S. Senyukov  128, J.J. Seo  58, D. Serebryakov  140, L. Šerkšnytė  95, A. Sevcenco  63, T.J. Shaba  68, A. Shabetai  103, R. Shahoyan32, A. Shangaraev  140, A. Sharma90, B. Sharma  91, D. Sharma  47, H. Sharma  54,107, M. Sharma  91, S. Sharma  76, S. Sharma  91, U. Sharma  91, A. Shatat  130, O. Sheibani115, K. Shigaki  92, M. Shimomura77, J. Shin11, S. Shirinkin  140, Q. Shou  39, Y. Sibiriak  140, S. Siddhanta  52, T. Siemiarczuk  79, T.F. Silva  110, D. Silvermyr  75, T. Simantathammakul105, R. Simeonov  36, B. Singh91, B. Singh  95, R. Singh  80, R. Singh  91, R. Singh  48, S. Singh  15, V.K. Singh  134, V. Singhal  134, T. Sinha  99, B. Sitar  12, M. Sitta  132,56, T.B. Skaali19, G. Skorodumovs  94, M. Slupecki  43, N. Smirnov  137, R.J.M. Snellings  59, E.H. Solheim  19, J. Song  115, A. Songmoolnak105, C. Sonnabend  32,97, F. Soramel  27, A.B. Soto-hernandez  88, R. Spijkers  84, I. Sputowska  107, J. Staa  75, J. Stachel  94, I. Stan  63, P.J. Steffanic  121, S.F. Stiefelmaier  94, D. Stocco  103, I. Storehaug  19, P. Stratmann  125, S. Strazzi  25, C.P. Stylianidis84, A.A.P. Suaide  110, C. Suire  130, M. Sukhanov  140, M. Suljic  32, R. Sultanov  140, V. Sumberia  91, S. Sumowidagdo  82, S. Swain61, I. Szarka  12, M. Szymkowski  135, S.F. Taghavi  95, G. Taillepied  97, J. Takahashi  111, G.J. Tambave  80, S. Tang  126,6, Z. Tang  119, J.D. Tapia Takaki  117, N. Tapus113, L.A. Tarasovicova  125, M.G. Tarzila  45, G.F. Tassielli  31, A. Tauro  32, G. Tejeda Muñoz  44, A. Telesca  32, L. Terlizzi  24, C. Terrevoli  115, S. Thakur  4, D. Thomas  108, A. Tikhonov  140, A.R. Timmins  115, M. Tkacik106, T. Tkacik  106, A. Toia  64, R. Tokumoto92, N. Topilskaya  140, M. Toppi  49, F. Torales-Acosta18, T. Tork  130, A.G. Torres Ramos  31, A. Trifiró  30,53, A.S. Triolo  32,30,53, S. Tripathy  51, T. Tripathy  47, S. Trogolo  32, V. Trubnikov  3, W.H. Trzaska  116, T.P. Trzcinski  135, A. Tumkin  140, R. Turrisi  54, T.S. Tveter  19, K. Ullaland  20, B. Ulukutlu  95, A. Uras  127, M. Urioni  55,133, G.L. Usai  22, M. Vala37, N. Valle  21, L.V.R. van Doremalen59, M. van Leeuwen  84, C.A. van Veen  94, R.J.G. van Weelden  84, P. Vande Vyvre  32, D. Varga  46, Z. Varga  46, M. Vasileiou  78, A. Vasiliev  140, O. Vázquez Doce  49, O. Vazquez Rueda  115, V. Vechernin  140, E. Vercellin  24, S. Vergara Limón44, L. Vermunt  97, R. Vértesi  46, M. Verweij  59, L. Vickovic33, Z. Vilakazi122, O. Villalobos Baillie  100, A. Villani  23, G. Vino  50, A. Vinogradov  140, T. Virgili  28, M.M.O. Virta  116, V. Vislavicius75, A. Vodopyanov  141, B. Volkel  32, M.A. Völkl  94, K. Voloshin140, S.A. Voloshin  136, G. Volpe  31, B. von Haller  32, I. Vorobyev  95, N. Vozniuk  140, J. Vrláková  37, C. Wang  39, D. Wang39, Y. Wang  39, A. Wegrzynek  32, F.T. Weiglhofer38, S.C. Wenzel  32, J.P. Wessels  125, J. Wiechula  64, J. Wikne  19, G. Wilk  79, J. Wilkinson  97, G.A. Willems  125, B. Windelband  94, M. Winn  129, J.R. Wright  108, W. Wu39, Y. Wu  119, R. Xu  6, A. Yadav  42, A.K. Yadav  134, S. Yalcin  72, Y. Yamaguchi  92, S. Yang20, S. Yano  92, Z. Yin  6, I.-K. Yoo  16, J.H. Yoon  58, S. Yuan20, A. Yuncu  94, V. Zaccolo  23, C. Zampolli  32, F. Zanone  94, N. Zardoshti  32, A. Zarochentsev  140, P. Závada  62, N. Zaviyalov140, M. Zhalov  140, B. Zhang  6, L. Zhang  39, S. Zhang  39, X. Zhang  6, Y. Zhang119, Z. Zhang  6, M. Zhao  10, V. Zherebchevskii  140, Y. Zhi10, D. Zhou  6, Y. Zhou  83, J. Zhu  97,6, Y. Zhu6, S.C. Zugravel  56, N. Zurlo  133,55

Affiliation Notes

I Deceased

II Also at: Max-Planck-Institut fur Physik, Munich, Germany

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

IV Also at: Dipartimento DET del Politecnico di Torino, Turin, Italy

V Also at: Department of Applied Physics, Aligarh Muslim University, Aligarh, India

VI Also at: Institute of Theoretical Physics, University of Wroclaw, Poland

VII Also at: An institution covered by a cooperation agreement with CERN

Collaboration Institutes

1 A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia

2 AGH University of Krakow, Cracow, Poland

3 Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, Kiev, Ukraine

4 Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS), Kolkata, India

5 California Polytechnic State University, San Luis Obispo, California, United States

6 Central China Normal University, Wuhan, China

7 Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana, Cuba

8 Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City and Mérida, Mexico

9 Chicago State University, Chicago, Illinois, United States

10 China Institute of Atomic Energy, Beijing, China

11 Chungbuk National University, Cheongju, Republic of Korea

12 Comenius University Bratislava, Faculty of Mathematics, Physics and Informatics, Bratislava, Slovak Republic

13 COMSATS University Islamabad, Islamabad, Pakistan

14 Creighton University, Omaha, Nebraska, United States

15 Department of Physics, Aligarh Muslim University, Aligarh, India

16 Department of Physics, Pusan National University, Pusan, Republic of Korea

17 Department of Physics, Sejong University, Seoul, Republic of Korea

18 Department of Physics, University of California, Berkeley, California, United States

19 Department of Physics, University of Oslo, Oslo, Norway

20 Department of Physics and Technology, University of Bergen, Bergen, Norway

21 Dipartimento di Fisica, Università di Pavia, Pavia, Italy

22 Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy

23 Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy

24 Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy

25 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Bologna, Italy

26 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Catania, Italy

27 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Padova, Italy

28 Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo Collegato INFN, Salerno, Italy

29 Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy

30 Dipartimento di Scienze MIFT, Università di Messina, Messina, Italy

31 Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari, Italy

32 European Organization for Nuclear Research (CERN), Geneva, Switzerland

33 Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia

34 Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway

35 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic

36 Faculty of Physics, Sofia University, Sofia, Bulgaria

37 Faculty of Science, P.J. Šafárik University, Košice, Slovak Republic

38 Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany

39 Fudan University, Shanghai, China

40 Gangneung-Wonju National University, Gangneung, Republic of Korea

41 Gauhati University, Department of Physics, Guwahati, India

42 Helmholtz-Institut für Strahlen- und Kernphysik, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

43 Helsinki Institute of Physics (HIP), Helsinki, Finland

44 High Energy Physics Group, Universidad Autónoma de Puebla, Puebla, Mexico

45 Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania

46 HUN-REN Wigner Research Centre for Physics, Budapest, Hungary

47 Indian Institute of Technology Bombay (IIT), Mumbai, India

48 Indian Institute of Technology Indore, Indore, India

49 INFN, Laboratori Nazionali di Frascati, Frascati, Italy

50 INFN, Sezione di Bari, Bari, Italy

51 INFN, Sezione di Bologna, Bologna, Italy

52 INFN, Sezione di Cagliari, Cagliari, Italy

53 INFN, Sezione di Catania, Catania, Italy

54 INFN, Sezione di Padova, Padova, Italy

55 INFN, Sezione di Pavia, Pavia, Italy

56 INFN, Sezione di Torino, Turin, Italy

57 INFN, Sezione di Trieste, Trieste, Italy

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, California, 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, Tennessee, United States

88 Ohio State University, Columbus, Ohio, 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, Texas, United States

109 Universidad Autónoma de Sinaloa, Culiacán, Mexico

110 Universidade de São Paulo (USP), São Paulo, Brazil

111 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil

112 Universidade Federal do ABC, Santo Andre, Brazil

113 Universitatea Nationala de Stiinta si Tehnologie Politehnica Bucuresti, Bucharest, Romania

114 University of Cape Town, Cape Town, South Africa

115 University of Houston, Houston, Texas, United States

116 University of Jyväskylä, Jyväskylä, Finland

117 University of Kansas, Lawrence, Kansas, 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, Tennessee, United States

122 University of the Witwatersrand, Johannesburg, South Africa

123 University of Tokyo, Tokyo, Japan

124 University of Tsukuba, Tsukuba, Japan

125 Universität Münster, Institut für Kernphysik, Münster, Germany

126 Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France

127 Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de Lyon, Lyon, France

128 Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France, Strasbourg, France

129 Université Paris-Saclay, Centre d’Etudes de Saclay (CEA), IRFU, Départment de Physique Nucléaire (DPhN), Saclay, France

130 Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France

131 Università degli Studi di Foggia, Foggia, Italy

132 Università del Piemonte Orientale, Vercelli, Italy

133 Università di Brescia, Brescia, Italy

134 Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata, India

135 Warsaw University of Technology, Warsaw, Poland

136 Wayne State University, Detroit, Michigan, United States

137 Yale University, New Haven, Connecticut, United States

138 Yonsei University, Seoul, Republic of Korea

139 Zentrum für Technologie und Transfer (ZTT), Worms, Germany

140 Affiliated with an institute covered by a cooperation agreement with CERN

141 Affiliated with an international laboratory covered by a cooperation agreement with CERN.