Anisotropic flow and flow fluctuations of identified hadrons in Pb–Pb collisions at √sNN = 5.02 TeV

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Anisotropic flow and flow fluctuations of identified hadrons in Pb–Pb collisions at √sNN = 5.02 TeV © CERN, for the benefit of the ALICE Collaboration. Article funded by SCOAP3 Published version ALICE Collaboration ALICE Collaboration. (2023). Anisotropic flow and flow fluctuations of identified hadrons in Pb–Pb collisions at √sNN = 5.02 TeV. Journal of High Energy Physics, 2023(5), Article 243. https://doi.org/10.1007/JHEP05(2023)243 2023 JHEP05(2023)243 Published for SISSA by Springer Received:June 22, 2022 Accepted:September 1, 2022 Published:May 31, 2023 Anisotropic flow and flow fluctuations of identified hadrons in Pb–Pb collisions at √sNN = 5.02 TeV The ALICE collaboration E-mail: [email protected] Abstract: The first measurements of elliptic flow of π±, K±, p+¯p, K0 S,Λ+Λ,φ,Ξ−+Ξ+, and Ω−+ Ω+using multiparticle cumulants in Pb–Pb collisions at √sNN = 5.02 TeV are presented. Results obtained with two- (v2{2}) and four-particle cumulants (v2{4}) are shown as a function of transverse momentum, pT, for various collision centrality intervals. Combining the data for both v2{2}and v2{4}also allows us to report the first measurements of the mean elliptic flow, elliptic flow fluctuations, and relative elliptic flow fluctuations for various hadron species. These observables probe the event-by-event eccentricity fluctuations in the initial state and the contributions from the dynamic evolution of the expanding quark–gluon plasma. The characteristic features observed in previous pT-differential anisotropic flow measurements for identified hadrons with two-particle correlations, namely the mass ordering at low pTand the approximate scaling with the number of constituent quarks at intermediate pT, are similarly present in the four-particle correlations and the combinations of v2{2}and v2{4}. In addition, a particle species dependence of flow fluctuations is observed that could indicate a significant contribution from final state hadronic interactions. The comparison between experimental measurements and CoLBT model calculations, which combine the various physics processes of hydrodynamics, quark coalescence, and jet fragmentation, illustrates their importance over a wide pTrange. Keywords: Collective Flow, Heavy Ion Experiments, Particle Correlations and Fluctuations ArXiv ePrint: 2206.04587 Open Access, Copyright CERN, for the benefit of the ALICE Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP05(2023)243 JHEP05(2023)243 Contents 1 Introduction 1 2 Experimental setup 3 3 Analysis procedure 4 3.1 Event and track selection 4 3.2 Selection of π±,K±, and p+p5 3.3 Reconstruction of φmesons 5 3.4 Reconstruction of K0 Sand Λ+Λ6 3.5 Reconstruction of Ξ−+Ξ+and Ω−+Ω+6 3.6 Flow observables 7 3.7 Flow extraction methods 7 4 Systematic uncertainties 9 5 Results 11 5.1 Mass ordering and scaling properties 12 5.2 Results on flow fluctuations 14 5.3 Comparison with models 16 6 Summary 20 The ALICE collaboration 27 1 Introduction The primary goal of the ultra-relativistic heavy-ion collision programme at the Large Hadron Collider (LHC) is to study the properties of the quark–gluon plasma (QGP), a novel state of strongly interacting matter at high temperatures and energy densities [1,2]. Studies of the azimuthal anisotropy of particle production have contributed significantly to the characterization of the system created in heavy-ion collisions [3,4]. Anisotropic flow reflects the conversion of the initial state spatial anisotropy into final state anisotropies in momentum space. This translation is facilitated by interactions between the constituents of the quark–gluon plasma (QGP) [5–7] and at later stages, after hadronisation, between the produced particles. Anisotropic flow is quantified by studying the azimuthal distribution of particles emitted in the plane transverse to the beam direction [3]. This is usually expressed in terms of a Fourier series in the azimuthal angle ϕ[8,9] according to Ed3N dp3=1 2π d2N pTdpTdη(1+2 ∞ X n=1 vn(pT, η) cos[n(ϕ−Ψn)]),(1.1) – 1 – JHEP05(2023)243 where E,N,p,pT,ϕ, and ηare the energy, yield, momentum, transverse momentum, azimuthal angle, and pseudorapidity of particles, respectively, and Ψnis the azimuthal angle of the symmetry plane of order n [10,11]. The vncoefficients are given by vn=hcos[n(ϕ−Ψn)]i,(1.2) where hi denote an average over all particles in a single event. The second Fourier coefficient, v2, is usually referred to as elliptic flow. It is the dominant harmonic in heavy-ion collisions with large values of impact parameter (i.e. non-central collisions). Its value is sensitive to some of the basic transport coefficients of the QGP, e.g. the shear viscosity over entropy density ratio (η/s). The study of elliptic flow has been instrumental in establishing the strongly-coupled QGP paradigm, first in collisions at the Relativistic Heavy Ion Collider (RHIC) [12–15] and, since 2010, in collisions at the Large Hadron Collider (LHC) [16–18]. Around the start of the LHC heavy-ion program, it was realised that elliptic flow is also a sensitive probe of the initial state of heavy-ion collisions [19]. Its magnitude fluctuates from one event to the other, reflecting the event-by-event fluctuating energy-density profiles of the nuclear overlap region prior to the formation of the QGP. Initial event-by-event geometry fluctuations lead to the fluctuations of even-harmonic anisotropic flow and generate non-zero odd harmonics. In fact, the initial geometry fluctuations lead to hvk ni 6=hvnik and the development of different order symmetry planes Ψnin different kinematic regions in pTor η[20–22]. Thus, a comprehensive investigation of the final state flow fluctuations is crucial for understanding the event-by-event initial geometry fluctuations and their impact on the system dynamic evolution. Studies of charged particles in Pb–Pb collisions at LHC energies indicated non-Gaussian initial state fluctuations and, consequently, made it possible to constrain their probability distribution function (p.d.f.) [23,24]. Studies of flow fluctuations have so far been performed both experimentally and in theoretical model calculations for the measurements integrated over a large kinematic range [25–28]. On the other hand, a pT-differential study, and in particular with identified hadrons, has not been done before. These studies can provide insights on the interplay between the expansion of the system and its late-stage, highly-dissipative hadronic phase, as well as particle production mechanisms. Similar studies in the past for various flow coefficients have been pivotal in establishing the need to include viscous corrections in hydrodynamic models and, consequently, in constraining the value of η/s to be very close to the conjectured lower limit of 1/4πcalculated for infinitely strongly coupled gauge theories via the AdS/CFT correspondence [29]. Detailed studies of how anisotropic flow develops for different particle species as a function of pTfor various centrality intervals (i.e. an estimate of the degree of overlap between the two colliding nuclei) of Pb–Pb collisions at LHC energies [26–28] confirmed a number of qualitative features already observed at RHIC [12–15]: the mass ordering of vnat low pTand the particle type (i.e. mesons versus baryons) grouping at intermediate pT. The former originates from the interplay between radial flow and the anisotropic expansion of the system because of a thermalized expanding source with a common flow velocity for the produced particles [30], while the latter is interpreted as an indication of hadron formation – 2 – JHEP05(2023)243 via quark coalescence in this momentum range [31,32]. These studies also revealed, for the first time, similar qualitative features in the 1% most central Pb–Pb collisions [27], a category of events known as ultracentral with no prevailing ellipsoidal geometry. In addition, new results on the non-linear flow modes of higher harmonics [33] illustrated unambiguously that the aforementioned features can still be observed after the non-linear response of the system, the latter being proportional to the product of lower-order initial spatial anisotropies [34–37]. All these studies relied on measuring various flow harmonics using variations of twoparticle correlation techniques. One of the disadvantages of such approaches is that they are sensitive to non-flow effects, i.e. correlations between (mainly two) particles not associated with the common symmetry plane. In order to suppress such contributions, one could measure multiparticle cumulants which need a larger data sample to reach the same level of uncertainties as to their two-particle counterpart measurements. This is possible now by combining the entire data set of Pb–Pb collisions at the centre-of-mass energy per nucleon pair √sNN = 5.02 TeV from the LHC Run 2. Furthermore, the usage of higherorder cumulants opens up the possibility to study, for the first time, the particle species dependence of flow fluctuations. The first measurements of elliptic flow and flow fluctuations using twoand four-particle cumulants for π±,K±, p+p,K0 S,Λ+Λ,φ,Ξ−+Ξ+, and Ω−+Ω+in Pb–Pb collisions at √sNN = 5.02 TeV are presented in this article. Results obtained with the generic framework [38–40], which corrects detector inefficiencies and non-uniformities in the azimuthal acceptance, are reported for a wide range of transverse momenta (0.2< pT<6 GeV/c) in the 10–60% centrality interval. Centrality is expressed as percentiles of the inelastic hadronic cross section, with low percentage values corresponding to head-on collisions. The studies are performed separately for particles and antiparticles, and the results are compatible within the statistical uncertainties. Therefore, v2is the average between results for particles and antiparticles which for the rest of the article will be denoted as π±, K±, p+p, etc. This article is organized as follows: the experimental setup is presented in section 2, while the analysis procedure, particle identification (PID), reconstruction methods, and flow measurement techniques are described in section 3. Section 4outlines the evaluation of systematic uncertainties. The v2of π±,K±, p+p,K0 S,Λ+Λ,φ,Ξ−+Ξ+, and Ω−+Ω+ and the corresponding flow fluctuations are reported and compared to hydrodynamic calculations in section 5. The article concludes with a summary in section 6. 2 Experimental setup The ALICE detector [41,42] has been designed to allow detailed physics studies under the extreme conditions created in heavy-ion collisions. ALICE consists of a central barrel that contains several detectors with full or limited azimuthal coverage and a set of forward detectors. The central region is located in a solenoid magnet which generates up to a 0.5T field parallel to the beam direction. – 3 – JHEP05(2023)243 The main tracking detectors, positioned in the central barrel, are the Inner Tracking System (ITS) [41] and the Time Projection Chamber (TPC) [43]. The ITS consists of six layers of silicon detectors employing three different technologies. The two innermost layers are Silicon Pixel Detectors (SPD), followed by two layers of Silicon Drift Detectors (SDD). Finally, the two outermost layers are double-sided Silicon Strip Detectors (SSD). The SPD is also used for event selection and vertex reconstruction. The TPC surrounds the ITS and is also employed for precise tracking of charged particles and for particle identification via the specific energy loss, dE/dx. The dE/dxis extracted using a truncated-mean procedure, resulting in a dE/dxresolution for the 5%most central Pb–Pb collisions of around 6.5%, which improves for more peripheral collisions [42]. The detector provides a separation by at least 2 standard deviations (σ) for π±,K±, and p+pat pT<0.7 GeV/c and the possibility to identify particles on a statistical basis for pT>2 GeV/c [42]. The Time of Flight detector (TOF) [44] is located around the TPC and is used for particle identification by measuring the flight time of particles from the collision point with a resolution of about 80 ps [42]. The start time for the TOF measurement is provided by the T0 detector with a resolution of about 25 ps [42,45], two arrays of Cherenkov counters covering the pseudorapidity ranges −3.3< η < −3.0(T0C) and 4.6< η < 4.9(T0A), or from a combinatorial algorithm that uses the particle arrival times at the TOF detector itself [42,44]. Both methods of estimating the start time are fully efficient for the 60%most central Pb–Pb collisions. The TOF provides a 3σseparation between π±–K±and K±–p+pup to pT= 2.5 GeV/c and pT= 4 GeV/c, respectively [42]. The ITS, TPC, and TOF detectors cover the full azimuth within |η|<0.9. In the forward region, two scintillator arrays (V0) [46] are used for triggering, event selection, and the determination of the collision centrality [47]. The V0 consists of two systems, the V0C and V0A, positioned at −3.7< η < −1.7and 2.8< η < 5.1, respectively. In addition, two tungsten-quartz neutron Zero Degree Calorimeters (ZDCs), installed 112.5 meters from the interaction point on each side, are used for event selection. More details on the ALICE setup and the performance of the detectors can be found in refs. [41,42]. 3 Analysis procedure 3.1 Event and track selection The data sample used in this analysis consists of Pb–Pb collisions at √sNN = 5.02 TeV recorded by the ALICE detector in the years 2015 and 2018 LHC data-taking campaigns. A minimum bias trigger was provided by requiring signals in both V0A and V0C scintillator arrays. In addition, the sample of semi-central collisions was enhanced by an online selection based on the V0 signal amplitudes. Beam-induced background events (i.e. beam–gas interactions) were removed offline utilizing the V0 and ZDC timing information. Pileup of collisions from different bunch crossings in the TPC was rejected by comparing multiplicity estimates from the V0 detector to those of tracking detectors at midrapidity, exploiting the difference in readout times between the systems. The primary vertex position, determined from tracks reconstructed in the ITS and TPC, was required to be within ±10 cm from the – 4 – JHEP05(2023)243 nominal interaction point along the beam direction. These selection criteria were met by approximately 245 million events in the 10–60% centrality interval. The collision centrality was estimated from the amplitudes of the signals measured in the V0 detector [47]. Charged-particle tracks, used to measure the v2of π±,K±, p+p,φ-mesons, and inclusive charged particles, were reconstructed using the ITS and TPC within |η|<0.8and 0.2< pT<10 GeV/c. Each track was required to have a minimum number of 70 TPC space points (out of a maximum of 159) with a χ2per TPC space point lower than 4 and at least 2 hits in the ITS with a χ2per ITS hit smaller than 36. Moreover, tracks with a distance of closest approach (DCA) larger than 2 cm in the longitudinal direction were rejected. In the transverse plane, a pT-dependent DCA selection of the form 0.0105+0.0350 p−1.1 Tcm was applied. These selection criteria lead to an efficiency of about 80% for primary tracks at pT>0.5 GeV/c and contamination from fake tracks (random associations of space points) and secondary charged particles (i.e. particles originating from weak decays, conversions, and secondary hadronic interactions in the detector material) of about 5% at pT≈1 GeV/c. 3.2 Selection of π±,K±, and p+p Particle identification of π±,K±, and p+pis performed using the dE/dxfrom the TPC and the time of flight from the TOF system, if available. The identification is based on the normalised difference between the measured and the expected signal for a given species (σTPC and σTOF, respectively). It uses the correlation between nσTPC and nσTOF in a Bayesian approach [48], where the signals converted into probabilities are folded with the expected abundances (priors) of each particle species. The minimal probability threshold has been set to 0.95 for π±and 0.85 for K±and p+p. In addition, particles are selected by requiring |nσTPC|<3and |nσTOF|<3for each species in the whole pTrange. This procedure ensures a high purity of the studied sample, thus reducing the uncertainties due to particle misidentification. The resulting purity, estimated using Monte Carlo (MC) simulations, is higher than 95% for π±for 0.2< pT<10 GeV/c, above 80% for K±for 0.3< pT<6 GeV/c, and reaches values larger than 90% for p+pfor 0.5< pT<6 GeV/c. 3.3 Reconstruction of φmesons The φmeson is reconstructed in the decay channel φ→K++ K−with a branching ratio of 49.2% [49]. Its decay products are selected using the same criteria for primary K±(see section 3.2). The φmeson yield is obtained from the invariant mass (MK+K−) reconstructed from all possible K±pairs after subtracting the combinatorial background evaluated using the like-sign kaon pairs in each pTand centrality interval. The resulting MK+K−distribution is parametrised as a sum of a Breit–Wigner (BW) distribution and a third-order polynomial function that accounts for residual contamination within the invariant mass range of 0.99 < MK+K−<1.07 GeV/c2. The pT-differential yield of φ mesons is extracted by integration of the BW distribution and used for the v2extraction together with the background yield (see eq. 3.14). The procedure of the reconstruction of φmeson is identical to the previous measurements [28], while the extraction of v2{4}(pT) is slightly different and explained in section 3.7. – 5 – JHEP05(2023)243 3.4 Reconstruction of K0 Sand Λ+Λ The reconstruction of K0 Sand Λ+Λis based on identifying their secondary vertices called V0s in the decay channels K0 S→π++π−and Λ→p + π−(Λ→p + π+) with branching ratios of 69.2% and 63.9% [49], respectively. Selection criteria related to the distinctive Vshaped decay topology and requirements on the characteristics of the daughter particles are applied to suppress the large combinatorial background. The invariant mass is calculated assuming that the daughter particles, identified using the TPC (|nσTPC|<3) over the entire pTrange, are either a π+π−pair or a pπ−(pπ+) pair. The V0candidates are selected with an invariant mass between 0.4 and 0.6 GeV/c2for K0 Sand 1.08 and 1.16 GeV/c2for Λ+Λ. The daughter tracks are reconstructed within |η|<0.8using the same TPC track quality requirements described in section 3.1 for charged tracks. In addition, the ratio between the number of space points and the number of crossed rows in the TPC is required to be larger than 0.8, the minimum DCA of daughter tracks to the primary vertex is 0.1 cm, and the maximum DCA of daughter tracks to the secondary vertex is 0.5 cm. Only V0candidates produced at a radial distance between 5 and 100 cm from the beam line and with a cosine of the pointing angle (the angle between the line connecting the primary and V0vertices and the V0momentum vector) larger than 0.998 are accepted. To reduce the contamination from Λ+Λand electron–positron pairs coming from γconversions, an additional selection is applied in the Armenteros–Podolanski variables [50] of the K0 Scandidates, similar to what is done in ref. [28]. To obtain the pT-differential yield of K0 Sand Λ+Λ, the invariant mass distributions in various pTintervals are parametrised as a sum of two Gaussian distributions with the same mean and a third-order polynomial function which accounts for residual background. The K0 Sand Λ+Λyields are extracted by integration of the Gaussian distributions and are not corrected for feed-down from higher mass baryons (e.g. Ξ±,Ω±), but these have a negligible effect on v2[26]. 3.5 Reconstruction of Ξ−+Ξ+and Ω−+Ω+ The Ξ−+Ξ+and Ω−+Ω+are reconstructed through the cascade topology of the following weak decays: Ξ−→Λ + π−(Ξ+→Λ + π+) and Ω−→Λ+K−(Ω+→Λ + K+) with branching ratios of 99.9% and 67.8% [49], respectively, with a subsequent Λ(Λ) decay. Candidates are found by applying topological and kinematic criteria first to select the V0 with an invariant mass between 1.08 and 1.16 GeV/c2and then to match it with one of the remaining secondary tracks. They are selected by requiring the DCA between the V0 and the track to be less than 0.3 cm, the cosine of the pointing angle to be at least 0.999 and 0.998 for the cascade and V0, respectively, the DCA between the V0and primary vertex to be larger than 0.05 cm, the minimum DCA of V0daughter tracks to the primary vertex to be 0.1 cm, the maximum DCA of V0daughter tracks to be 1.0 cm, and the minimum DCA of the daughter track to the primary vertex to be 0.03 cm. Only Ξ−+Ξ+ and Ω−+Ω+candidates produced at a radial distance between 0.9 and 100 cm from the beam line with the same radial distance reported in section 3.4 for V0are accepted. Each of the three daughter tracks is also required to have pT>0.15 GeV/c within |η|<0.8and to pass the TPC track quality criteria detailed above for charged tracks. In addition, the – 6 – JHEP05(2023)243 daughter tracks are checked for compatibility with the pion, kaon, or proton hypotheses by selecting particles with |nσTPC|<3for each species. The pT-differential yield of Ξ−+Ξ+ and Ω−+Ω+is obtained by fitting the invariant mass distributions with a sum of two Gaussian distributions with the same mean and a third-order polynomial function that describe the signal and the background, respectively. 3.6 Flow observables A common approach to study the event-by-event flow fluctuations for a given flow coefficient is by using the twoand multiparticle cumulants [51,52], which have different sensitivities to effects stemming from non-flow and flow fluctuations, vn{2}=hv2 ni1/2+δn,(3.1) vn{4}=h2hv2 ni2−hv4 nii1/4,(3.2) where δndenotes the two-particle non-flow effects. Assuming that flow fluctuation σvnis relatively small compared to vn, which was found to be true for non-central heavy-ion collisions at the LHC [53–55], and also assuming that non-flow effects can be experimentally removed (or largely suppressed, e.g. by using appropriate ηgaps) in two-particle correlation measurements, the vncan be written as [56] v2 n{2}=hvni2+σ2 vn,(3.3) v2 n{4} ≈ hvni2−σ2 vn,(3.4) where hvniand σvnare the mean and fluctuations of the anisotropic flow coefficient, respectively. These two quantities correspond to the first and second moments of the eventby-event vndistribution. The observable hvniis expected to be free from flow fluctuations and to only reflect the true elliptic flow from the flow symmetry plane. Both hvniand σvncan be calculated using the measured vn{2}and vn{4}as hvni ≈ sv2 n{2}+v2 n{4} 2,(3.5) σvn≈sv2 n{2}−v2 n{4} 2.(3.6) Furthermore, the relative flow fluctuations F(vn)are defined as F(vn) = σvn hvni.(3.7) 3.7 Flow extraction methods The measurement of the pT-differential vncoefficients of identified hadrons is performed using twoand four-particle cumulant method [51], according to vn{2}(pT) = dn{2} pcn{2},(3.8) vn{4}(pT) = −dn{4} (−cn{4})3/4.(3.9) – 7 – JHEP05(2023)243 0 1 2 3 4 5 0 0.05 0.1 q n / {4} 2 v 20%−10 0 1 2 3 4 5 0 0.05 0.1 30%−20 0 1 2 3 4 5 ) c (GeV/ q n / T p 0 0.05 0.1 q n / {4} 2 v 40%−30 0 1 2 3 4 5 ) c (GeV/ q n / T p 0 0.05 0.1 50%−40 0 1 2 3 4 5 ) c (GeV/ q n / T p 0 0.05 0.1 60%−50 0 1 2 3 4 5 0 0.05 0.1 ± π ± K S 0 K )p p( ± π ± K S 0 K )p p( )Λ(Λ φ )Ξ(Ξ )Ω(Ω )Λ(Λ φ )Ξ(Ξ )Ω(Ω ALICE = 5.02 TeV NN s Pb −Pb | < 0.8η| Figure 4. The dependence of v2{4}/nqon pT/nq, where nqis the number of constituents quarks, for different particle species and centralities in Pb–Pb collisions at √sNN = 5.02 TeV. The vertical error bars and the filled boxes represent statistical and systematic uncertainties, respectively. 0 2 4 6 8 10 0 0.1 0.2 0.3 > 2 < v 20%−10 0 2 4 6 8 10 0 0.1 0.2 0.3 30%−20 0 2 4 6 8 10 )c (GeV/ T p 0 0.1 0.2 0.3 > 2 < v 40%−30 0 2 4 6 8 10 )c (GeV/ T p 0 0.1 0.2 0.3 50%−40 0 2 4 6 8 10 )c (GeV/ T p 0 0.1 0.2 0.3 60%−50 0 2 4 6 8 10 0 0.1 0.2 0.3 ± h ± π ± K S 0 K ± h ± π ± K S 0 K )p p( )Λ(Λ )Ξ(Ξ )Ω(Ω )p p( )Λ(Λ )Ξ(Ξ )Ω(Ω ALICE = 5.02 TeV NN s Pb −Pb | < 0.8η| Figure 5. The dependence of the mean value of v2(hv2i) on pTfor different particle species and centralities in Pb–Pb collisions at √sNN = 5.02 TeV. The vertical error bars and the filled boxes represent statistical and systematic uncertainties, respectively. 5.2 Results on flow fluctuations The measurements of v2with twoand four-particle cumulants provide the first opportunity to investigate the first moments of the v2distribution for different particle species. Figure 5 presents the mean value of v2, denoted as hv2i, as a function of pTfor the same combination – 14 – JHEP05(2023)243 0 2 4 6 8 10 0 0.05 0.1 0.15 2 v σ 20%−10 0 2 4 6 8 10 0 0.05 0.1 0.15 30%−20 0 2 4 6 8 10 )c (GeV/ T p 0 0.05 0.1 0.15 2 v σ 40%−30 0 2 4 6 8 10 )c (GeV/ T p 0 0.05 0.1 0.15 50%−40 0 2 4 6 8 10 )c (GeV/ T p 0 0.05 0.1 0.15 60%−50 0 2 4 6 8 10 0 0.05 0.1 0.15 ± h ± π ± K S 0 K ± h ± π ± K S 0 K )p p( )Λ(Λ )Ξ(Ξ )Ω(Ω )p p( )Λ(Λ )Ξ(Ξ )Ω(Ω ALICE = 5.02 TeV NN s Pb −Pb | < 0.8η| Figure 6. The pTdependence of the standard deviation of v2(σv2) for different particle species and centralities in Pb–Pb collisions at √sNN = 5.02 TeV. The vertical error bars and the filled boxes represent statistical and systematic uncertainties, respectively. 0 2 4 6 8 10 0 0.2 0.4 0.6 ) 2 F(v 20%−10 0 2 4 6 8 10 0 0.2 0.4 0.6 30%−20 0 2 4 6 8 10 )c (GeV/ T p 0 0.2 0.4 0.6 ) 2 F(v 40%−30 0 2 4 6 8 10 )c (GeV/ T p 0 0.2 0.4 0.6 50%−40 0 2 4 6 8 10 )c (GeV/ T p 0 0.2 0.4 0.6 60%−50 0 2 4 6 8 10 0 0.2 0.4 0.6 ± h ± π ± K S 0 K ± h ± π ± K S 0 K )p p( )Λ(Λ )Ξ(Ξ )Ω(Ω )p p( )Λ(Λ )Ξ(Ξ )Ω(Ω ALICE = 5.02 TeV NN s Pb −Pb | < 0.8η| Figure 7. The relative elliptic flow fluctuations (F(v2)) as a function of pTfor different particle species and centralities in Pb–Pb collisions at √sNN = 5.02 TeV. The vertical error bars and the filled boxes represent statistical and systematic uncertainties, respectively. of hadrons and centrality intervals as in the previous figures. Assuming that the nonflow contribution in v2 2{2,|∆η|>0.8}is negligible [21,36], this mean value is calculated according to eq. 3.5 by replacing v2 2{2}with v2 2{2,|∆η|>0.8}measurement. Figure 6presents the transverse momentum dependence of the second moment of the v2distribution, i.e. the standard deviation σv2, measured for the first time for different – 15 – JHEP05(2023)243 particle species. As in the previous case, assuming negligible non-flow contribution in v2 2{2,|∆η|>0.8},σv2is approximated according to eq. 3.6. The data points of both hv2iand σv2show, as expected, the same qualitative features as in the previous cases of figures 2and 3: namely the mass ordering developing at low values of pTand the particle type grouping that is evident at higher pT. Combining hv2iand σv2, one can quantify the relative v2fluctuations (F(v2)) according to eq. 3.7. This quantity is displayed in figure 7as a function of pTand centrality intervals for the various particle species presented in this article. It can be seen that for central events, there is no significant pTor particle species dependence. However, for more peripheral collisions, and in particular starting from the interval 30–40% and above, the data points exhibit a non-monotonic transverse momentum dependence, with a minimum in F(v2)that lies at higher values of pTfor baryons than for mesons. In addition, the F(v2) for baryons in 1< pT<3 GeV/c is systematically lower than for mesons. Interestingly, the momentum region where this apparent particle type grouping develops for F(v2)does not coincide with the region where a similar grouping is reported for measurements of v2. This could point to a different origin for these two observations. For pT>3 GeV/c, all data points converge into a universal band within the uncertainties. The origin of this characteristic behaviour of F(v2)is studied using hydrodynamical models in the following section. Finally, to further study the nature of flow fluctuations, figure 8presents the pTdependence of the ratio v2{4}/v2{2,|∆η|>0.8}. This ratio is expected to be sensitive to the fluctuations within the picture of initial state models. Within these models, the spatial eccentricity 2fluctuates from event to event. These fluctuations are transferred through the low viscosity QGP to the final state and are imprinted in how v2fluctuates. Since v2∝2, the ratio v2{4}/v2{2,|∆η|>0.8}is expected to reflect the ratio between 2{4}and 2{2}, which have positive and negative contributions from the initial eccentricity fluctuations, and thus can provide strong constraints on initial state models. It can be seen that for central collisions, this ratio does not exhibit any significant pTor particle species dependence. Starting from the 20–30% centrality interval, however, a decrease in the ratio can be seen between 1 and 5 GeV/c. It becomes progressively more pronounced for more peripheral events. In addition, starting from the 30–40% centrality interval, and similar to the picture that develops for F(v2), the data points indicate a particle type grouping, with the values for baryons being systematically larger than the ones for mesons. This apparent dependence on particle species highlights that final state effects play a significant role in these observables. 5.3 Comparison with models The comparison of results from anisotropic flow studies with hydrodynamic calculations has been instrumental in constraining some of the basic transport coefficients of the QGP. However, such comparisons were limited until now to the low pTregion, i.e. in ranges where the mass ordering discussed in the previous section is prominent. One of the first attempts to provide a unified physics picture throughout the entire transverse momentum range for different particle species was presented recently in ref. [60]. In this article, the authors – 16 – JHEP05(2023)243 0 2 4 6 8 10 0.6 0.8 1 1.2 | > 0.8}η∆{2,| 2 / v {4} 2 v 20%−10 0 2 4 6 8 10 0.6 0.8 1 1.2 30%−20 0 2 4 6 8 10 )c (GeV/ T p 0.6 0.8 1 1.2 | > 0.8}η∆{2,| 2 / v {4} 2 v 40%−30 0 2 4 6 8 10 )c (GeV/ T p 0.6 0.8 1 1.2 50%−40 0 2 4 6 8 10 )c (GeV/ T p 0.6 0.8 1 1.2 60%−50 0 2 4 6 8 10 0.6 0.8 1 1.2 ± h ± π ± K S 0 K ± h ± π ± K S 0 K )p p( )Λ(Λ )Ξ(Ξ )Ω(Ω )p p( )Λ(Λ )Ξ(Ξ )Ω(Ω ALICE = 5.02 TeV NN s Pb −Pb | < 0.8η| Figure 8. The ratio v2{4}/v2{2,|∆η|>0.8}as a function of pTfor different particle species and centralities in Pb–Pb collisions at √sNN = 5.02 TeV. The vertical error bars and the filled boxes represent statistical and systematic uncertainties, respectively. 0 1 2 3 4 5 6 7 8 9 10 )c (GeV/ T p 0 0.1 0.2 0.3 {4} 2 v ± π ± K )p p( ± π ± K )p p( ALICE CoLBT Hydro+coal+frag = 5.02 TeV NN sPb −Pb | < 0.8η| 20%−10 ± π ± K )pp( 0 1 2 3 4 5 6 7 8 9 10 )c (GeV/ T p 0 0.1 0.2 0.3 {4} 2 v ± π ± K )p p( ± π ± K )p p( ALICE CoLBT Hydro+coal+frag = 5.02 TeV NN sPb −Pb | < 0.8η| 50%−40 ± π ± K )pp( Figure 9. The pT-differential v2{4}for π±,K±, and p+pmeasured in Pb–Pb collisions at √sNN = 5.02 TeV compared with expectations of the same quantity from the CoLBT hydrodynamic model with quark coalescence [60]. The left and right panels present the comparison for the 10–20% and 40–50% centrality intervals, respectively. The vertical error bars and the filled boxes represent statistical and systematic uncertainties of the data, respectively. The thickness of the model curves reflect the uncertainties of the hydrodynamic calculations. used the CoLBT hydrodynamic model [59] which allows for the simultaneous description of the evolution of parton showers and the bulk medium. The latter is prescribed by a (3+1)-D viscous hydrodynamic model that is initialized at τ0= 0.6fm/cand uses a value of specific shear viscosity η/s = 0.10. The freeze-out temperature is set to Tfo = 150 MeV, beyond which a hadronic after-burner describes the interactions between hadrons. The remaining parameters of the model were adjusted to reproduce the measured yields, pT – 17 – JHEP05(2023)243 0 1 2 3 4 5 6 7 8 9 10 )c (GeV/ T p 0 0.1 0.2 0.3 {4} 2 v ± π ± K )p p( ± π ± K )p p( ALICE CoLBT Hydro+frag = 5.02 TeV NN sPb −Pb | < 0.8η| 50%−40 ± π ± K )pp( Figure 10. The pT-differential v2{4}for π±,K±, and p+pmeasured in Pb–Pb collisions at √sNN = 5.02 TeV compared with expectations of the same quantity from the CoLBT hydrodynamic model without quark coalescence [60] in 40–50% centrality interval. The vertical error bars and the filled boxes represent statistical and systematic uncertainties of the data, respectively. The thickness of the model curves reflect the uncertainties of the hydrodynamic calculations. 0 1 2 3 4 5 6 7 0.1 0.2 0.3 > 2 < v ± π ± K )p p( ± π ± K )p p( ALICE = 5.02 TeV NN sPb −Pb | < 0.8η| 50%−40 (a) 0 1 2 3 4 5 6 7 )c (GeV/ T p 0.05 0.1 0.15 2 v σ ± π ± K )p p( CoLBT (b) 0 1 2 3 4 5 6 7 0.6 0.8 1 {2} 2 /v{4} 2 v (c) Hydro+coal+frag 0 1 2 3 4 5 6 7 )c (GeV/ T p 0.2 0.4 0.6 0.8 ) 2 F(v (d) Figure 11. The pT-differential (a) hv2i, (b) σv2, (c) v2{4}/v2{2}, and (d) F(v2)for π±,K±, and p+pmeasured in one indicative centrality interval (40–50%) of Pb–Pb collisions at √sNN = 5.02 TeV compared with expectations of the same quantities from the CoLBT hydrodynamic model [60]. The vertical error bars and the filled boxes represent statistical and systematic uncertainties of the data, respectively. The thickness of the model curves reflect the uncertainties of the hydrodynamic calculations. spectra, and integrated vnof unidentified charged hadrons in Pb–Pb collisions. One of the important ingredients which is introduced in this model is the way hadrons emerge, with the typical hydrodynamic freeze-out at low pTbeing complemented by a quark coalescence prescription at intermediate pTand fragmentation at high pT[60]. – 18 – JHEP05(2023)243 Figure 9presents the evolution of v2{4}as a function of pTfor π±,K±, and p+pfor two characteristic centrality intervals, 10–20% (central) and 40–50% (peripheral), in the left and right panels, respectively. The measurements are compared with the expectations for the same particle species from the CoLBT hydrodynamic model, represented by the shaded bands. It can be seen that the model describes the pTdependence of v2{4}over the entire pTrange. In particular, at low values of pT(<2–3 GeV/c) where the hydrodynamic expansion of the medium plays a dominant role, the model describes both the increase as a function of pTand the mass ordering. The v2{4}reaches a peak value at around pT ≈3GeV/c for pions and kaons and at pT≈4GeV/c for protons, before decreasing at high pT. This can be naturally explained by the interplay between the hydrodynamical expansion, hadron production through quark coalescence, and jet fragmentation [60]. Within the CoLBT model, the hydrodynamic contribution to v2is dominant for all particle species up to pT= 4 GeV/c, whereas the jet fragmentation plays an increasingly important role for pT>6 GeV/c. In the intermediate pTregion (4–6 GeV/c), quark coalescence contributes in CoLBT to the development of the value of v2, even though in the model this mechanism accounts for less than 25%of the total particle yield. This is because the value of v2from coalescence is significantly larger than the v2from fragmentation up to 6 GeV/c. The additional mechanism of the coalescence prescription in the model is important to reproduce the experimental results quantitatively and to provide the proper connection between the low and high pTregions. In the former, the mass ordering develops, while in the latter the fragmentation is the dominant particle production mechanism and no significant particle species dependence is observed. It is known that neither the hydrodynamic expansion nor the fragmentation alone leads to the precise NCQ scaling development. Such contributions in the final v2could consequently give a natural explanation for the significant deviation from a universal NCQ scaling observed in figure 4. On the other hand, the model calculation with only contributions from hydrodynamic expansion and fragmentation but without the contribution from quark coalescence significantly underestimates v2{4}for pTabove 3 GeV/cfor π±, K±, shown in figure 10. Nevertheless, the crossing of v2of pions, kaons, and protons can develop according to CoLBT with a combination of the hydrodynamic expansion coupled only to jet fragmentation (for details refer to figure 4 in ref. [60]). This might challenge the prevailing idea discussed in the literature (see, e.g., ref. [61]) that the crossing can be attributed to quark coalescence. It is important to note that the development of the crossing point in the absence of coalescence in CoLBT arises from a particle species-dependent pTvalue where fragmentation becomes dominant over hydrodynamics. To further investigate the coalescence contributions on flow fluctuations, figures 11 and 12 present the comparison of the pT-differential hv2i(panel a), σv2(panel b), v2{4}/v2{2}(panel c), and F(v2)(panel d) for π±,K±, and p+pwith the calculation from CoLBT model with the combinations of hydrodynamics, quark coalescence, and jet fragmentation as well as with CoLBT model with only the combinations of hydrodynamics and jet fragmentation, respectively [60]. The 40–50% centrality interval was chosen as representative for these comparisons. The model without quark coalescence contribution describes qualitatively the features and the pTdependence of the measurements, but signif- – 19 – JHEP05(2023)243 0 1 2 3 4 5 6 7 0.1 0.2 0.3 > 2 < v ± π ± K )p p( ± π ± K )p p( ALICE = 5.02 TeV NN sPb −Pb | < 0.8η| 50%−40 (a) 0 1 2 3 4 5 6 7 )c (GeV/ T p 0.05 0.1 0.15 2 v σ ± π ± K )p p( CoLBT (b) 0 1 2 3 4 5 6 7 0.6 0.8 1 {2} 2 /v{4} 2 v (c) Hydro+frag 0 1 2 3 4 5 6 7 )c (GeV/ T p 0.2 0.4 0.6 0.8 ) 2 F(v (d) Figure 12. The pT-differential (a) hv2i, (b) σv2, (c) v2{4}/v2{2}, and (d) F(v2)for π±,K±, and p+pmeasured in one indicative centrality interval (40–50%) of Pb–Pb collisions at √sNN = 5.02 TeV compared with expectations of the same quantities from the CoLBT hydrodynamic model without quark coalescence [60]. The vertical error bars and the filled boxes represent statistical and systematic uncertainties of the data, respectively. The thickness of the model curves reflect the uncertainties of the hydrodynamic calculations. icantly underestimates hv2iof pion and kaon for pTabove 3 GeV/c. This is very different from what has been observed in figures 9and 11. Despite the sizable uncertainties of CoLBT calculations, the contribution from quark coalescence seems non-negligible for σv2, v2{4}/v2{2}, and F(v2), when comparing the calculations of hydro+coal+frag (shown in figure 11) and hydro+frag (shown in figure 12). 6 Summary In summary, the first measurement of pT-differential elliptic flow using twoand fourparticle cumulants for π±,K±, p+p,K0 S,Λ+Λ,φ,Ξ−+Ξ+, and Ω−+Ω+in Pb–Pb collisions at √sNN = 5.02 TeV is presented. The mean elliptic flow, elliptic flow fluctuations, and relative elliptic flow fluctuations are obtained for various particle species. Differences in the value of relative flow fluctuations for different particle species are observed, suggesting that final state hadronic interactions further modify the flow fluctuations. A distinct mass ordering is found in the 10–60% centrality interval for pT<3 GeV/c, which arises from the interplay between the elliptic and radial flow. In the intermediate pTrange, the magnitude of v2{4},hv2i, and σv2for baryons is larger than that for mesons by about 50%. In addition, particles show an approximate constituent quark scaling. This scaling is tested for v2{4}, which is expected to measure flow with little (or no) non-flow contamination. NCQ scaling describes the data no better than ±20%, an accuracy similar to what was reported for the – 20 – JHEP05(2023)243 v2using two-particle correlations. Furthermore, the relative flow fluctuation F(v2)for the identified hadrons shows an apparent splitting between baryons and mesons for centrality above 30%, which suggests a significant role for final-state interactions in developing this observable. Last but not least, CoLBT hydrodynamic calculations with the implementation of quark coalescence describe the measurements over a large pTrange, which confirms the relevance of the quark coalescence hadronization mechanism in the particle production in Pb–Pb collisions at the LHC. Acknowledgments 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 2467N36] 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 Technol- – 21 – JHEP05(2023)243 ogy (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 University Politehnica of Bucharest, 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), Thailand Science Research and Innovation (TSRI) and National Science, Research and Innovation Fund (NSRF), 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. 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Suljic 32, V. Sumberia 91, S. Sumowidagdo 82, – 30 – JHEP05(2023)243 S. Swain60, I. Szarka 12, U. Tabassam13, S.F. Taghavi 96, G. Taillepied 98, J. Takahashi 111, G.J. Tambave 20, S. Tang 125,6, Z. Tang 118, J.D. Tapia Takaki 116, N. Tapus124, L.A. Tarasovicova 135, M.G. Tarzila 45, G.F. Tassielli 31, A. Tauro 32, A. Telesca 32, L. Terlizzi 24, C. Terrevoli 114, G. Tersimonov3, D. Thomas 108, A. Tikhonov 140, A.R. Timmins 114, M. Tkacik106, T. Tkacik 106, A. Toia 63, R. Tokumoto93, N. Topilskaya 140, M. Toppi 48, F. Torales-Acosta18, T. Tork 72, A.G. Torres Ramos 31, A. Trifiró 30,52, A.S. Triolo 30,52, S. Tripathy 50, T. Tripathy 46, S. Trogolo 32, V. Trubnikov 3, W.H. Trzaska 115, T.P. Trzcinski 133, R. Turrisi 53, T.S. Tveter 19, K. Ullaland 20, B. Ulukutlu 96, A. Uras 126, M. Urioni 54,131, G.L. Usai 22, M. Vala37, N. Valle 21, S. Vallero 55, L.V.R. van Doremalen58, M. van Leeuwen 84, C.A. van Veen 95, R.J.G. van Weelden 84, P. Vande Vyvre 32, D. Varga 136, Z. Varga 136, M. Varga-Kofarago 136, M. Vasileiou 78, A. Vasiliev 140, O. Vázquez Doce 96, V. Vechernin 140, E. Vercellin 24, S. Vergara Limón44, L. Vermunt 98, R. Vértesi 136, M. Verweij 58, L. Vickovic33, Z. Vilakazi121, O. Villalobos Baillie 101, G. Vino 49, A. Vinogradov 140, T. Virgili 28, V. Vislavicius83, A. Vodopyanov 141, B. Volkel 32, M.A. Völkl 95, K. Voloshin140, S.A. Voloshin 134, G. Volpe 31, B. von Haller 32, I. Vorobyev 96, N. Vozniuk 140, J. Vrláková 37, B. Wagner20, C. Wang 39, D. Wang39, M. Weber 103, A. Wegrzynek 32, F.T. Weiglhofer38, S.C. Wenzel 32, J.P. Wessels 135, S.L. Weyhmiller 137, J. Wiechula 63, J. Wikne 19, G. Wilk 79, J. Wilkinson 98, G.A. Willems 135, B. Windelband95, M. Winn 128, J.R. Wright 108, W. Wu39, Y. Wu 118, R. Xu 6, A. Yadav 42, A.K. Yadav 132, S. Yalcin 71, Y. Yamaguchi93, K. Yamakawa93, S. Yang20, S. Yano 93, Z. Yin 6, I.-K. Yoo 16, J.H. Yoon 57, S. Yuan20, A. Yuncu 95, V. Zaccolo 23, C. Zampolli 32, H.J.C. Zanoli58, F. Zanone 95, N. Zardoshti 32,101, A. Zarochentsev 140, P. Závada 61, N. Zaviyalov140, M. Zhalov 140, B. Zhang 6, S. Zhang 39, X. Zhang 6, Y. Zhang118, Z. Zhang 6, M. Zhao 10, V. Zherebchevskii 140, Y. Zhi10, N. Zhigareva140, D. Zhou 6, Y. Zhou 83, J. Zhu 98,6, Y. Zhu6, G. ZinovjevI,3, N. Zurlo 131,54 1A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia 2AGH University of Science and Technology, Cracow, Poland 3Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, Kiev, Ukraine 4Bose Institute, Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS), Kolkata, India 5California Polytechnic State University, San Luis Obispo, California, United States 6Central China Normal University, Wuhan, China 7Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana, Cuba 8Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City and Mérida, Mexico 9Chicago State University, Chicago, 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 – 31 – JHEP05(2023)243 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 Strahlenund 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 Indian Institute of Technology Bombay (IIT), Mumbai, India 47 Indian Institute of Technology Indore, Indore, India 48 INFN, Laboratori Nazionali di Frascati, Frascati, Italy 49 INFN, Sezione di Bari, Bari, Italy 50 INFN, Sezione di Bologna, Bologna, Italy 51 INFN, Sezione di Cagliari, Cagliari, Italy 52 INFN, Sezione di Catania, Catania, Italy 53 INFN, Sezione di Padova, Padova, Italy 54 INFN, Sezione di Pavia, Pavia, Italy 55 INFN, Sezione di Torino, Turin, Italy 56 INFN, Sezione di Trieste, Trieste, Italy 57 Inha University, Incheon, Republic of Korea 58 Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University/Nikhef, Utrecht, Netherlands 59 Institute of Experimental Physics, Slovak Academy of Sciences, Košice, Slovak Republic 60 Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India 61 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 62 Institute of Space Science (ISS), Bucharest, Romania 63 Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 64 Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico City, Mexico – 32 – JHEP05(2023)243 65 Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 66 Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, Mexico 67 iThemba LABS, National Research Foundation, Somerset West, South Africa 68 Jeonbuk National University, Jeonju, Republic of Korea 69 Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik, Fachbereich Informatik und Mathematik, Frankfurt, Germany 70 Korea Institute of Science and Technology Information, Daejeon, Republic of Korea 71 KTO Karatay University, Konya, Turkey 72 Laboratoire de Physique des 2 Infinis, Irène Joliot-Curie, Orsay, France 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 Department, University of Rajasthan, Jaipur, India 93 Physics Program and International Institute for Sustainability with Knotted Chiral Meta Matter (SKCM2), Hiroshima University, Hiroshima, Japan 94 Physikalisches Institut, Eberhard-Karls-Universität Tübingen, Tübingen, Germany 95 Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 96 Physik Department, Technische Universität München, Munich, Germany 97 Politecnico di Bari and Sezione INFN, Bari, Italy 98 Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany 99 Saga University, Saga, Japan 100 Saha Institute of Nuclear Physics, Homi Bhabha National Institute, Kolkata, India 101 School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom 102 Sección Física, Departamento de Ciencias, Pontificia Universidad Católica del Perú, Lima, Peru 103 Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria 104 SUBATECH, IMT Atlantique, Nantes Université, CNRS-IN2P3, Nantes, France 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 – 33 – JHEP05(2023)243 111 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil 112 Universidade Federal do ABC, Santo Andre, Brazil 113 University of Cape Town, Cape Town, South Africa 114 University of Houston, Houston, Texas, United States 115 University of Jyväskylä, Jyväskylä, Finland 116 University of Kansas, Lawrence, Kansas, United States 117 University of Liverpool, Liverpool, United Kingdom 118 University of Science and Technology of China, Hefei, China 119 University of South-Eastern Norway, Kongsberg, Norway 120 University of Tennessee, Knoxville, Tennessee, United States 121 University of the Witwatersrand, Johannesburg, South Africa 122 University of Tokyo, Tokyo, Japan 123 University of Tsukuba, Tsukuba, Japan 124 University Politehnica of Bucharest, Bucharest, Romania 125 Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France 126 Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de Lyon, Lyon, France 127 Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France, Strasbourg, France 128 Université Paris-Saclay Centre d’Etudes de Saclay (CEA), IRFU, Départment de Physique Nucléaire (DPhN), Saclay, France 129 Università degli Studi di Foggia, Foggia, Italy 130 Università del Piemonte Orientale, Vercelli, Italy 131 Università di Brescia, Brescia, Italy 132 Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata, India 133 Warsaw University of Technology, Warsaw, Poland 134 Wayne State University, Detroit, Michigan, United States 135 Westfälische Wilhelms-Universität Münster, Institut für Kernphysik, Münster, Germany 136 Wigner Research Centre for Physics, Budapest, Hungary 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 IDeceased II Also at: Max-Planck-Institut für Physik, Munich, Germany III Also at: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Bologna, Italy IV Also at: Dipartimento DET del Politecnico di Torino, Turin, Italy VAlso at: Department of Applied Physics, Aligarh Muslim University, Aligarh, India V I Also at: Institute of Theoretical Physics, University of Wroclaw, Poland V II Also at: An institution covered by a cooperation agreement with CERN – 34 –