Production of K0S, Λ (Λ¯), Ξ±, and Ω± in jets and in the underlying event in pp and p–Pb collisions

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/ Production of K0S, Λ (Λ¯), Ξ±, and Ω± in jets and in the underlying event in pp and p–Pb collisions © CERN, for the benefit of the ALICE Collaboration. Article funded by SCOAP3 Published version ALICE Collaboration ALICE Collaboration. (2023). Production of K0S, Λ (Λ¯), Ξ±, and Ω± in jets and in the underlying event in pp and p–Pb collisions. Journal of High Energy Physics, 2023(7), Article 136. https://doi.org/10.1007/JHEP07(2023)136 2023 JHEP07(2023)136 Published for SISSA by Springer Received:December 9, 2022 Accepted:March 1, 2023 Published:July 17, 2023 Production of K0 S,Λ(Λ), Ξ±, and Ω±in jets and in the underlying event in pp and p–Pb collisions The ALICE collaboration E-mail: [email protected] Abstract: The production of strange hadrons (K0 S,Λ,Ξ±, and Ω±), baryon-to-meson ratios (Λ/K0 S,Ξ/K0 S, and Ω/K0 S), and baryon-to-baryon ratios (Ξ/Λ,Ω/Λ, and Ω/Ξ) associated with jets and the underlying event were measured as a function of transverse momentum (pT) in pp collisions at √s= 13 TeV and p−Pb collisions at √sNN = 5.02 TeV with the ALICE detector at the LHC. The inclusive production of the same particle species and the corresponding ratios are also reported. The production of multi-strange hadrons, Ξ±and Ω±, and their associated particle ratios in jets and in the underlying event are measured for the first time. In both pp and p–Pb collisions, the baryon-to-meson and baryon-to-baryon yield ratios measured in jets differ from the inclusive particle production for low and intermediate hadron pT(0.6–6 GeV/c). Ratios measured in the underlying event are in turn similar to those measured for inclusive particle production. In pp collisions, the particle production in jets is compared with Pythia 8 predictions with three colour-reconnection implementation modes. None of them fully reproduces the data in the measured hadron pTregion. The maximum deviation is observed for Ξ±and Ω±which reaches a factor of about six. The event multiplicity dependence is further investigated in p−Pb collisions. In contrast to what is observed in the underlying event, there is no significant event-multiplicity dependence for particle production in jets. The presented measurements provide novel constraints on hadronisation and its Monte Carlo description. In particular, they demonstrate that the fragmentation of jets alone is insufficient to describe the strange and multi-strange particle production in hadronic collisions at LHC energies. Keywords: Hadron-Hadron Scattering , Jet Physics, Particle and Resonance Production ArXiv ePrint: 2211.08936 Open Access, Copyright CERN, for the benefit of the ALICE Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP07(2023)136 JHEP07(2023)136 Contents 1 Introduction 1 2 ALICE detector and data selection 3 3 Analysis 4 3.1 Charged-particle jet reconstruction 4 3.2 Strange particle reconstruction 5 3.3 Matching of strange particles to jets 6 3.4 Corrections for strange particle reconstruction and feed-down 7 3.5 Systematic uncertainties 8 4 Results and discussion 11 4.1 Particle production and yield ratios in pp collisions at √s= 13 TeV 11 4.2 Production and ratios of JE particles in p–Pb collisions at √sNN = 5.02 TeV 17 5 Summary 20 A Particle candidate selection criteria 23 B Comparison of Ξ/Λand Ω/Λratios to colour-rope predictions in pp collisions at √s= 13 TeV 24 CpT-differential particle density and ratios for event multiplicity classes in p–Pb collisions at √sNN = 5.02 TeV 25 The ALICE collaboration 32 1 Introduction High-energy heavy-ion collisions at the LHC create a hot and dense form of matter called quark−gluon plasma (QGP) [1–6]. The droplet of QGP created in the collision rapidly expands as a strongly-coupled liquid and cools down until a temperature near the phase transition at which the deconfined partons hadronise into ordinary colour-neutral matter [7–9]. Systematic studies of particle production, transverse momentum spectra and correlations of identified particles allow to investigate the properties of the partonic phase and the hadronisation process itself. The interpretation of heavy-ion results (with hot nuclear matter effects) and extraction of the QGP properties require studies of particle production in small collision systems, proton–proton (pp) and proton–nucleus (pA). Previously, the measurements in these small – 1 – JHEP07(2023)136 collision systems were thought of as a necessary foundation to quantify the initial and final state effects of the so-called cold nuclear matter. However, during the last decade, the study of small systems has gained increased interest as a research field in its own right. In particular, similar effects as those present in heavy-ion collisions have been observed in pp and p–Pb collisions where the formation of a QGP was not expected [10–16]. These include, for example, the long-range angular correlations [10–12] and the non-vanishing elliptic flow coefficient measured using multi-particle cumulant analyses [13,14]. The magnitude of these effects increases smoothly with system size and particle multiplicity from pp, p–Pb to Pb–Pb collisions. Another new feature observed in high-multiplicity pp and p–Pb collisions is the enhancement of the baryon-to-meson yield ratios, p/π and Λ/K0 S, at intermediate transverse momentum pT(2–6GeV/c) [17–20], which is qualitatively similar to that observed in Pb–Pb collisions. Moreover, the strange to non-strange hadron ratio increases continuously as a function of charged-particle multiplicity density from lowmultiplicity pp to high-multiplicity p–Pb collisions to eventually reach the values observed in Pb–Pb collisions [18,20,21]. These findings suggest the possible existence of a common underlying mechanism which would determine the chemical composition of particles produced from small to large collision systems. On the other hand, measurements of jet production at midrapidity in small systems do not exhibit nuclear modifications [22–29]. The enhancement of baryon-to-meson yield ratios in the intermediate pTregion has been related to the interplay of radial flow and parton recombination [6,30,31]. In a recent study, the ALICE Collaboration investigated baryon-to-meson yield ratios in two separate parts of the event – inside a jet and in the event portion perpendicular to the jet cone – in pp collisions at √s= 7 and 13 TeV and p–Pb collisions at √sNN = 5.02 TeV [32,33]. The results show that the enhancement of the Λ/K0 Sratio at intermediate pTobtained from inclusive particle production measurements in Pb–Pb collisions at √s= 2.76 TeV [19] and in high-multiplicity pp collisions [34] is not present in the low-zfragmentation products of jets. In addition to these effects, one can expect that particle production in this pTregion results from the hard fragmentation of partons of pTin the 4–8GeV/crange (momentum fraction z=phadron T/pparton T≈0.5). This is due to the steeply falling power-law spectrum characteristic for parton production. This so-called “leading particle effect” was described in terms of a “trigger bias” [35]. Studying the yield ratios of particles associated with jets allows us to explore a larger zrange, providing new constraints on whether the baryon-to- meson yield ratio enhancement originates from jet fragmentation. In this article, the pT-differential baryon-to-meson and multi-strange-to-strange particle ratios are studied in jets reconstructed using the charged-particle component (chargedparticle jets) and in the underlying event associated to jets. This provides further understanding of the contribution of soft and hard processes to the enhancement of the baryon- to-meson yield ratios at intermediate pTand of the strange particle yields as a function of multiplicity in small systems. In particular, the measurement of the production of K0 S, Λ(Λ), Ξ±, and Ω±in charged-particle jets and in the underlying event in pp collisions at √s= 13 TeV and p–Pb collisions at √sNN = 5.02 TeV is reported. Strange particles are reconstructed in the pseudorapidity range |η|<0.75. Jets are reconstructed with a transverse momentum pch T,jet >10 GeV/cand in the pseudorapidity range |ηjet|<0.35 with – 2 – JHEP07(2023)136 a resolution parameter R= 0.4(referred to as jet radius in the following). The strange particles produced inside a jet are characterised as a function of the distance between the particle momentum vector and the jet axis in the η–ϕplane, where ϕis the azimuthal angle. The results presented in this article surpass the precision of the previous ALICE pT- differential measurements [32] both in pp at √s= 7 TeV and p–Pb at √sNN = 5.02 TeV collisions. The studies are extended to the multi-strange sector and the charged-particle multiplicity dependence is investigated as well. The baryon-to-meson and baryon-to- baryon yield ratios inside jets are compared to the same ratios obtained from inclusive events and the underlying event. Results measured in pp collisions are compared with Pythia 8[36] simulations. The article is structured as follows. In section 2, the ALICE apparatus and the data samples used for the analysis are presented. In section 3, the methods adopted for chargedparticle jet reconstruction, strange particle reconstruction, and particle–jet matching are described. This section also includes the estimate of the associated systematic uncertainties. The measurement of strange hadron pTdistributions and the corresponding yield ratios, together with their comparison with model predictions, are presented and discussed in section 4. The paper is summarised in section 5. 2 ALICE detector and data selection The ALICE apparatus and its performance are described in refs. [37,38]. This analysis mainly relies on the central barrel tracking system and the forward V0 detector [39]. The central barrel detectors used for this analysis are the Inner Tracking System (ITS) [40], the Time Projection Chamber (TPC) [41], and the Time-Of-Flight detector (TOF) [42–44]. These detectors cover the pseudorapidity region |η|<0.9and are located inside a large solenoidal magnet providing a 0.5 T magnetic field. The ITS, the innermost barrel detector, consists of six cylindrical layers of high spatial resolution silicon detectors using three different technologies. The two innermost layers (Silicon Pixel Detector, SPD) are based on silicon pixel technology and cover |η|<2.0and |η|<1.4, respectively. The SPD is used to reconstruct the primary vertex of the collision and short track segments, which are called "tracklets". The four outer ITS layers consist of silicon drift (SDD) and strip (SSD) detectors, with the innermost (outermost) layer having a radius r= 15 (43) cm. The SDD and SSD are able to measure the specific ionization energy loss (dE/dx) with a relative resolution of about 10% in the low-pTregion (up to ∼1GeV/c) [40]. The ITS is also used to reconstruct and identify low-momentum particles down to 100 MeV/cthat cannot reach the TPC. The TPC is a large cylindrical gaseous detector filled with a Ne-CO2gas mixture. The radial and longitudinal dimensions of the TPC are about 85 < r < 250 cm and −250 < z < 250 cm, respectively. As the main tracking device, the TPC provides full azimuthal acceptance for tracks in the region |η|<0.9. In addition, it provides chargedhadron identification via the dE/dxmeasurement. At low pT, the dE/dxresolution of 5.2% for a minimum ionizing particle allows track-by-track particle identification [41]. On the other hand, at intermediate and high pT(&2.0GeV/c), the energy loss distributions – 3 – JHEP07(2023)136 of different particle species start to overlap. Therefore, from there on, particles have to be statistically separated via a multi-Gaussian fit to the dE/dxdistributions. The TOF, located at a radius of 3.7 m, outside of the TPC, measures the flight time of the particles. It consists of a cylindrical array of multi-gap resistive plate chambers with an intrinsic time resolution of 50 ps. It covers the range |η|<0.9with full azimuthal acceptance. It can provide particle identification over a broad pTrange (0.5.pT.2.7GeV/c). The total time-of-flight resolution, including the collision time resolution, is about 90 ps in pp and p–Pb collisions [34]. The V0 detector, composed of two scintillator arrays, V0A (covering a pseudorapidity range of 2.8< η < 5.1) and V0C (−3.7< η < −1.7), is utilized for triggering and event classification based on charged-particle multiplicity. Data from pp collisions at √s= 13 TeV and from p–Pb collisions at √sNN = 5.02 TeV are used in this analysis. The pp and p–Pb data samples were recorded with the ALICE detector in 2016–2017 and 2016, respectively. These data were collected with a minimum bias (MB) trigger requiring at least one hit in both V0A and V0C in coincidence with the bunch crossing. Interaction vertices are reconstructed by the extrapolation of ITS tracklets towards the average beam line. Pileup events, due to multiple interactions in the triggered bunch crossing, are removed by exploiting the correlation between the number of SPD hits and tracklets. The coordinate of the primary vertex along the beam direction is required to be within ±10 cm with respect to the nominal position of the ALICE interaction point. After event selection, the pp sample consists of 1.5billion events. The integrated luminosity of Lint = 9.38 ±0.47 nb−1based on the visible cross section observed by the V0 trigger was extracted from a van der Meer scan [45]. About 500 million events from the p–Pb samples were selected, which correspond to an integrated luminosity of Lint = 295 ±11 µb−1[46]. The p–Pb events are divided into three multiplicity classes based on the total charge deposited in the V0A (in the Pb-going direction). The multiplicity intervals and their corresponding mean charged-particle density (dNch/dη) measured at midrapidity (|η|< 0.5) are given in ref. [47]. 3 Analysis 3.1 Charged-particle jet reconstruction The charged-particle jets are reconstructed using the FastJet package [48] with the anti-kT algorithm [49] with a resolution parameter R= 0.4. As the inputs, the charged particles are reconstructed using the ITS and TPC information. Tracks with pT>0.15 GeV/care accepted over the pseudorapidity range |ηtrk|<0.9and with azimuthal angle 0<ϕ<2π. The reconstructed jet axis pseudorapidity is required to be in the range |ηjet|<0.35. This condition ensures that the jet cone is fully contained within the η-acceptance for strange particles. A selection on the charged-particle jet pT,pch T,jet >10 GeV/c, is applied to ensure that the jet originates from the hard scattering process [32]. In a hadron–hadron collider event, the two outgoing partons from the hard scattering are accompanied by particles that arise, e.g. from multiple parton interactions, which form a background for the jet production measurement. In pp collisions, the pTdensity per unit – 4 – JHEP07(2023)136 area in the η–ϕplane (ρch bkg) of this background is determined from the kTalgorithm [50,51] to be around 1GeV/crad−1, which is negligible and, hence, not subtracted in this analysis. The background density in events with at least one jet in pT>10 GeV/cis around 3GeV/crad−1in p–Pb collisions, which is more significant than pp collisions. Hence the reconstructed pTof the jet is corrected for the background contribution [52] using the formula pch T,jet =prec T,jet −ρch bkg ×Ajet,(3.1) where prec T,jet is the reconstructed jet pTand Ajet is the jet area. Ajet is calculated by the active ghost area method of FastJet, with a ghost area of 0.005 [53]. The estimation of the background density ρch bkg in sparse systems such as p–Pb collisions is based on the method described in ref. [54]. This method allows to circumvent problems arising from the use of ghost jets applicable to larger collision systems [54]. Under this method, empty areas are instead accounted for by applying a correction factor to the background density as follows ρch bkg =C×median (prec T,jet Ajet ),with C=PiAi Aacc ,(3.2) where Aiis the area of each kTjet with at least one real track, i.e. excluding ghosts and Aacc is the area of the charged-particle acceptance, namely (2×0.9)×2π. The background estimate is made more accurate by excluding the two clusters with the largest pTfrom the ρbkg calculation as given by eq. (3.2). 3.2 Strange particle reconstruction The strange particles K0 S,Λ,Λ,Ξ±, and Ω±are measured at midrapidity (|η|<0.75) via the reconstruction of their specific weak decay topology. The following charged decay channels with the corresponding branching ratios (B.R.) [55] are used: K0 S→π++π−B.R. = (69.20 ±0.05)%, Λ(Λ) →p(p) + π−(π+)B.R. = (63.9±0.5)%, Ξ−(Ξ+)→Λ(Λ) + π−(π+)B.R. = (99.887 ±0.035)%, Ω−(Ω+)→Λ(Λ) + K−(K+)B.R. = (67.8±0.7)%. The proton, pion, and kaon tracks (daughter tracks) are identified via their measured energy deposition in the TPC [38]. The identification of the V0candidates (K0 Sand Λ (Λ) that decay into two oppositely charged daughter particles) and cascade candidates (Ξ±and Ω±that decay into a “bachelor” charged meson, identified as π±or K±, plus a V0decaying particle, giving the cascade decay topology) follow those presented in earlier ALICE publications [17,21,34,56–58]. In addition, the contributions of pileup collisions outside the trigger bunch crossing (“out-of-bunch pileup”) are removed. This is achieved by requiring that at least one of the tracks corresponding to charged particle decays matches a hit in a “fast” detector (either the ITS or the TOF detector). The selections in this analysis are summarised in tables 5,6in appendix A. The signal extraction is performed as a function of pT. The invariant mass distribution in each pTinterval is fitted with a Gaussian function for the signal and a linear function – 5 – JHEP07(2023)136 0.45 0.5 0.55 ) 2 c (GeV/ - π + π M 4 10 5 10 6 10 Count = 5.02 TeV NN sPb −ALICE p | < 0.75 η | - π + π → S 0 K c < 0.8 GeV/ T p0.7 < 1.09 1.1 1.11 1.12 1.13 1.14 ) 2 c (GeV/ - πp M 4 10 5 10 6 10 Count = 5.02 TeV NN sPb −ALICE p | < 0.75 η | - π p→ Λ c < 0.8 GeV/ T p0.7 < 1.3 1.31 1.32 1.33 1.34 ) 2 c (GeV/ - πΛ M 3 10 4 10 Count = 5.02 TeV NN sPb −ALICE p | < 0.75 η | - πΛ → - Ξ c < 0.8 GeV/ T p0.7 < 1.65 1.66 1.67 1.68 1.69 1.7 ) 2 c (GeV/ - KΛ M 3 10 Count = 5.02 TeV NN sPb −ALICE p | < 0.75 η | - KΛ → - Ω c < 1.6 GeV/ T p1.2 < Figure 1. Invariant mass distribution for K0 S,Λ,Ξ−, and Ω−in different pTintervals in MB p–Pb collisions at √sNN = 5.02 TeV. The candidates are reconstructed in |η|<0.75. The grey areas are used to determine the background (red dashed lines), see text for details. for the combinatorial background. Examples of the invariant mass distribution fits for all particles are shown in figure 1. This allows for the extraction of the mean (µ) and width (σ) of the signal. The “peak” region is defined as that within ±6σfor V0s and ±3σ (±4σ) for cascades in pp (and p–Pb) collisions with respect to µfor each pTinterval. The “background” regions are defined on both sides of the peak region (see the gray areas in figure 1). The pT-differential yields of strange particles are obtained by subtracting the integral of the background fit function in the peak region from the total bin count in the same region (see ref. [56] for the details). 3.3 Matching of strange particles to jets The strategy for obtaining strange hadrons associated to hard scatterings, selected by charged-particle jets (JE particles), follows that presented in ref. [32]. Particles are defined as located inside the jet cones (JC) if their distance to the jet axis in the η–ϕplane R(particle,jet) = q(ηparticle −ηjet)2+ (ϕparticle −ϕjet)2(3.3) is less than a given value Rmax, R(particle,jet) < Rmax,(3.4) – 6 – JHEP07(2023)136 where Rmax = 0.4to be consistent with the value of the jet resolution parameter used for the jet reconstruction. The remaining contribution from the underlying event (UE) in the JC selection, which refers to particles not associated with jet fragmentation, is estimated in the perpendicular cone (PC) to the jet axis with radius R=RPC. The default value is RPC = 0.4. Since the η–ϕacceptance of the JC-selected particles differs from that for UE estimations, to subtract the UE component from the JC selection, a density distribution is defined dρ dpT =1 Nev ×1 Aacc ×dN dpT ,(3.5) where dN/dpTis the pT-differential particle production yield, and Nev and Aacc are the number of events and the area of the η–ϕacceptance for a given selection. For JC and PC selections, Nev corresponds to the number of events containing at least one jet with pch T,jet >10 GeV/c. The η–ϕacceptance area, Aacc, is calculated via Aacc =απR2,(3.6) where Ris the cone radius for the corresponding selection and αis a correction factor used to account for the partial geometrical overlap among jets on the η–ϕplane. The αfactor is calculated via a Monte Carlo (MC) sampling approach using measured distributions of strange particles and jets as inputs. The value of αis around 1.06 and it is insensitive to particle species and event multiplicities. It gives a minor correction on the particle density normalization since the production rate for jets with pch T,jet >10 GeV/cis low even in highmultiplicity p–Pb collisions. With the normalization defined in eq. (3.5), the pT-differential production density distribution of JE particles is obtained by subtracting the density distribution of particles with the UE selection from that with the JC selection, namely: dρJE dpT =dρJC dpT−dρUE dpT .(3.7) In addition, to compare with the JE particles, the production yield of inclusive particles is also normalized according to eq. (3.5). For the MB analysis, Nev corresponds to the number of selected MB events. For the event-activity differential analysis in p–Pb collisions, Nev corresponds to the number of selected events in the corresponding event activity interval. The acceptance area, Aacc, of particles in the inclusive analysis is given by Aacc = ∆η×∆ϕ, (3.8) where ∆η= 2 ×0.75 and ∆ϕ= 2π, correspond to the ηand ϕacceptances of inclusive particles, respectively. 3.4 Corrections for strange particle reconstruction and feed-down The reconstruction efficiencies of each particle are obtained from MC simulated data. For this purpose, Pythia 8 (for pp collisions) [36] and DPMJet (for p–Pb collisions) [59] are used and the simulated data are propagated through the ALICE detector by GEANT3 [60]. – 7 – JHEP07(2023)136 0 2 4 6 8 10 12 5− 10 4− 10 3− 10 2− 10 1− 10 0 2 4 6 8 10 12 5− 10 4− 10 3− 10 2− 10 1− 10 0 2 4 6 8 10 12 5− 10 4− 10 3− 10 0 2 4 6 8 10 12 5− 10 4− 10 3− 10 Inclusive Perp. cone (par, jet) < 0.4R Particle in jets = 13 TeVsALICE pp | < 0.75 η Particle | = 0.4R, T kJet: antic > 10 GeV/ ch T, jet p | < 0.35 jet η | )c (GeV/ T p)c (GeV/ T p S 0 KΛ + Λ + Ξ + - Ξ+ Ω + - Ω -1 rad)c (GeV/ T p/d ρ d -1 rad)c (GeV/ T p/d ρ d Figure 4.pT-differential density, dρ/dpT, of K0 S(top left panel), Λ(top right panel), Ξ(bottom left panel), and Ω(bottom right panel) in pp collisions at √s= 13 TeV. The spectra of JE particles (red triangles), associated with hard scatterings, are compared with that of JC (green squares) and UE (blue open circles) selections. The results from inclusive measurements (black closed circles) are presented as well. The statistical uncertainties are represented by the vertical error bars and the systematic uncertainties by the boxes. findings suggest that the production mechanism of Ωbaryons, as strange-quark triplets, in jets may be similar to that in the UE. This conclusion can be further confirmed in future measurements using a larger data sample. The pT-differential densities of inclusive K0 S,Λ,Ξ, and Ωare compared with simulations with Pythia 8event generator [36] in the left panels of figure 6. The Pythia 8Monte Carlo simulation studies are performed with the CR-BLC model [62], in which the minimisation of the string potential is implemented considering the SU(3) multiplet structure of QCD, allowing for the formation of “baryonic” configurations where two colours can combine coherently to form anti-colours. From the CR-BLC model [62], three modes (labeled as mode 0,2, and 3) are suggested by the authors – each applying different constraints on the allowed reconnections among the colour sources. In particular, considerations are given to the causal connections among strings involved in the reconnection and to the time dilation caused by relative boosts of the strings. The density spectra using Pythia 8are normalized in the same way as data described in section 3.3. The left-bottom panels of figure 6show the ratios between Pythia 8simulations and data. Since for each particle species the three CR-BLC modes give almost identical pT-differential density spectra, the – 14 – JHEP07(2023)136 0 2 4 6 8 10 12 0.2 0.4 0.6 0 2 4 6 8 10 12 0.05 0.1 0 2 4 6 8 10 12 0 0.01 0.02 0 2 4 6 8 10 12 0.1 0.2 0 2 4 6 8 10 12 0 0.05 0 2 4 6 8 10 12 0 0.2 Inclusive Perp. cone Particle in jets = 13 TeVsALICE pp | < 0.75 η Particle | = 0.4R, T kJet: antic > 10 GeV/ ch T, jet p | < 0.35 jet η | )c (GeV/ T p S 0 2K Λ + Λ S 0 2K + Ξ + - Ξ S 0 2K + Ω + - Ω Λ + Λ + Ξ + - Ξ Λ + Λ + Ω + - Ω+ Ξ + - Ξ + Ω + - Ω Baryon-to-meson ratioBaryon-to-baryon ratio Figure 5.pT-dependent strange baryon-to-meson (top) and baryon-to-baryon (bottom) yield ratios in pp collisions at √s= 13 TeV. For each case, the results of JE particles (red triangles) are compared with that of inclusive (black closed circles) and UE (blue open circles) particles. The statistical uncertainties are represented by the vertical error bars and the systematic uncertainties by the boxes. results corresponding to different CR-BLC modes are presented as bands (unless explicitly stated otherwise). The inclusive density spectra obtained with Pythia 8underestimate the data for all particle species and the pTdependence follows a power-law trend, which does not reproduce the moderate peaks on the spectra around pT= 2 GeV/cpresent in data. This results in a “valley” structure in Pythia-to-data ratios in the interval of 1< pT<4GeV/c. For K0 S and Λ, the value of the MC/data ratio reaches the minimum of around 0.4at pT≃2GeV/c, then it increases with pTfor pT>2GeV/cand shows a saturation trend with a value that rises to 0.8at pT>6GeV/c. For the multi-strange baryons, Ξand Ω, the ratio decreases with strange-quark content and baryon mass. The minimum values of the ratio are around 0.2and 0.1for Ξand Ω, respectively. In analogy with the left panel of figure 6, the right panel shows the comparisons of pT-differential densities for JE particles with the corresponding Pythia 8simulations for the different CR-BLC modes. The JE-particle density spectra from Pythia 8are obtained following the same approach applied to data as detailed in section 3.3.Pythia 8 simulations overestimate the density of K0 Smesons and Λbaryons in jets for pT<2GeV/c, while, in general, a better agreement is observed for pT>2GeV/c. The MC/data ratio for those two particle species are almost identical, as seen in the lower panels of figure 6. But, in general, the pT-differential density obtained from the generator is softer than that in the data. For Ξbaryons, Pythia 8overestimates their production associated with jets over the measured pTrange, 0.9< pT<8GeV/c, by a factor of around three to six depending on pT. A possible explanation is that the strings containing partons produced in the hard scattering processes have higher string tension during the PYTHIA fragmentation. – 15 – JHEP07(2023)136 0 2 4 6 8 10 12 6− 10 5− 10 4− 10 3− 10 2− 10 1− 10 1 -1 rad)c (GeV/ T p / d ρ d 16)× ( S 0 K 4)× (Λ + Λ 2)× ( + Ξ + - Ξ+ Ω + - Ω PYTHIA 8 CR-BLC mode 0 CR-BLC mode 2 CR-BLC mode 3 = 13 TeVsALICE pp Inclusive 0 2 4 6 8 10 12 0.4 0.6 0.8 S 0 K 0 2 4 6 8 10 12 0.4 0.6 0.8 Λ + Λ 0 2 4 6 8 10 12 0.2 0.4 0.6 + Ξ + - Ξ 0 2 4 6 8 10 12 )c (GeV/ T p 0.1 0.2 + Ω + - Ω CR-BLC PYTHIA / Data 0 2 4 6 8 10 12 4− 10 3− 10 2− 10 1− 10 1 10 -1 rad)c (GeV/ T p / d ρ d 16)× ( S 0 K 4)× (Λ + Λ 2)× ( + Ξ + - Ξ+ Ω + - Ω PYTHIA 8 CR-BLC mode 0 CR-BLC mode 2 CR-BLC mode 3 = 13 TeVsALICE pp Particle in jets 0 2 4 6 8 10 12 1 2 S 0 K 0 2 4 6 8 10 12 1 2 Λ + Λ 0 2 4 6 8 10 12 4 6 + Ξ + - Ξ 0 2 4 6 8 10 12 )c (GeV/ T p 0.5 1 1.5 + Ω + - Ω CR-BLC PYTHIA / Data Figure 6.pT-differential density distributions for inclusive (left) and within jets (right) K0 S(black closed circles), Λ(red open circles), Ξ(blue squares), and Ω(green inverted triangles) in pp collisions at √s= 13 TeV. The spectra in data are compared with Pythia 8CR-BLC simulations. Three modes, labeled as mode 0(solid line), 2(dashed line), and 3(dash-dotted line) are adopted in the simulations. The Pythia-to-data ratios are shown in the four bottom panels where the spread of the three Pythia 8CR-BLC implementation modes are presented as bands. For clarity, some of the spectra were scaled with the factors indicated in the legends. The statistical uncertainties are represented by the vertical error bars and the systematic uncertainties by the boxes. It is likely that in Pythia 8the ss-diquark string production rate is much higher than that within the jet fragmentation found in data. Moreover, since the probability for an ss-diquark combining with another single s-quark to form an Ωbaryon is lower than the probability combining with u- and d-quarks to form a Ξbaryon, the density of Ωproduced in jets is underestimated. For pT>2GeV/c, the corresponding ΩMC/data ratio reaches about 0.5with mild dependence on pT. This is indicative of the vastly overestimated production density of Ξbaryons in jets within the generator. The pT-differential particle ratios from the JE and the inclusive selections are compared with the Pythia 8simulations in figure 7.Pythia 8CR-BLC tunes generally agree with the Λ/K0 Sratios for both JE particles and inclusive measurements, despite that the simulations do not reproduce the individual density spectra neither in jets nor in the inclusive sample. Large discrepancies between data and the MC model are observed for all the other cases containing multi-strange hadrons in the numerator over the measured pTacceptance. As stated in ref. [63], although the string junction mechanism applied in Pythia 8CR-BLC tunes increases the baryon production probabilities, the ss-diquark – 16 – JHEP07(2023)136 0 2 4 6 8 10 12 0.2 0.4 0.6 0 2 4 6 8 10 12 0.05 0.1 CR-BLC 0 2 4 6 8 10 12 0 0.01 0.02 0 2 4 6 8 10 12 0.1 0.2 0 2 4 6 8 10 12 0 0.05 Inclusive Particle in jets 0 2 4 6 8 10 12 0 0.2 = 13 TeVsALICE pp | < 0.75 η Particle | = 0.4R, T kJet: antic > 10 GeV/ ch T, jet p | < 0.35 jet η | S 0 2K Λ + Λ S 0 2K + Ξ + - Ξ S 0 2K + Ω + - Ω Λ + Λ + Ξ + - Ξ Λ + Λ + Ω + - Ω + Ξ + - Ξ + Ω + - Ω )c (GeV/ T p Baryon-to-meson ratioBaryon-to-baryon ratio Figure 7.pT-dependent strange baryon-to-meson (top) and baryon-to-baryon (bottom) ratios in pp collisions at √s= 13 TeV. For each case, the results of JE (red triangles) and inclusive (black closed circles) particles are compared with Pythia 8CR-BLC simulations. The bands correspond to the spread of simulations of the three different CR-BLC implementation modes. The statistical uncertainties are represented by the vertical error bars and the systematic uncertainties by the boxes. is disfavoured in the Pythia fragmentation due to the phase-space constraint on high invariant mass strings. This results in Pythia 8largely underestimating the inclusive particle ratios containing multi-strange hadrons in the numerator. Since the density of Ξ in jets is overestimated by Pythia 8, then the Ξ/K0 Sand Ξ/Λratios given by Pythia 8 increase dramatically with pTand raise to unrealistic large values. At the same time, the Ω/Ξratio in jets is suppressed in Pythia 8generated events. It seems that Pythia 8 qualitatively reproduces the pTdependence for Ω/K0 Sand Ω/Λratios in jets. However, this may be due to the unrealistic enhancement of the ss-diquark produced by strings containing partons from hard scatterings in the model. It is worth noticing that the colour rope mechanism [64], in which the strange particle production is enhanced via interactions between strings [63], vastly overestimates the Ξand Ωproduction in jets at high pT(see the illustration shown in figure 10 in appendix B). The enhancement of the multi-strange production in colour rope predictions results from the higher local energy density in the region where an energetic jet is present. In summary, the measurements presented in this section provide important constraints on the production mechanisms of particles, especially for the multi-strange baryon, associated with hard partons. 4.2 Production and ratios of JE particles in p–Pb collisions at √sNN = 5.02 TeV The proton to π±ratios and strange baryon-to-meson yield ratios measured at high multiplicity in small collision systems (pp and p–Pb) [17,18,20,21,34,65,66] exhibit an – 17 – JHEP07(2023)136 0 2 4 6 8 10 12 5− 10 4− 10 3− 10 2− 10 1− 10 0 2 4 6 8 10 12 5− 10 4− 10 3− 10 2− 10 1− 10 0 2 4 6 8 10 12 5− 10 4− 10 3− 10 2− 10 0 2 4 6 8 10 12 4− 10 3− 10 Inclusive Perp. cone (par, jet) < 0.4R Particle in jets = 5.02 TeV NN sPb −ALICE p | < 0.75 η Particle | = 0.4R, T kJet: antic > 10 GeV/ ch T, jet p | < 0.35 jet η | S 0 KΛ + Λ + Ξ + - Ξ+ Ω + - Ω )c (GeV/ T p)c (GeV/ T p -1 rad)c (GeV/ T p/d ρ d -1 rad)c (GeV/ T p/d ρ d Figure 8.pT-differential density, dρ/dpT, of K0 S(top left panel), Λ(top right panel), Ξ(bottom left panel), and Ω(bottom right panel) in p–Pb collisions at √sNN = 5.02 TeV. The spectra of JE particles (red triangles), associated with hard scatterings, are compared with that of JC (green squares) and UE (blue open circles) selections. The results from inclusive measurements (black closed circles) are presented as well. The statistical uncertainties are represented by the vertical error bars and the systematic uncertainties by the boxes. enhancement at intermediate pT∼3GeV/cwith respect to the low-multiplicity events, qualitatively reminiscent of that measured in Pb–Pb collisions [19,67–69]. In the latter, the enhancement is considered as the fingerprint of hydrodynamic evolution of the colourdeconfined matter state, the quark–gluon plasma, created under extreme conditions of high temperature and energy density. To further constrain the particle production mechanisms in small collision systems, the study of strange particle production within charged-particle jets is extended to p–Pb collisions at √sNN = 5.02 TeV in both MB events and in events selected in various multiplicity intervals. Figure 8shows the pT-differential densities of K0 S,Λ,Ξ, and Ωin MB p–Pb collisions at √sNN = 5.02 TeV. For each case, the density distribution of JE particles is compared with that from JC and UE selections, and that of inclusive particles. In general, the particle densities measured in p–Pb collisions have the same order of magnitude as the corresponding ones in pp collisions shown in figure 4. Similar to the case in pp collisions, the density of JC-selected particles is dominated by those associated with hard scattering at high pT(pT>3GeV/c). As in pp, the pT-dependent density of JE particles is considerably less steep than the inclusive ones. The UE component given by the PC selection is mainly – 18 – JHEP07(2023)136 0 2 4 6 8 10 12 0.1 0.2 0.3 0.4 0 2 4 6 8 10 12 0 0.02 0.04 0.06 0.08 0 2 4 6 8 10 12 0 0.01 0.02 0.03 0 2 4 6 8 10 12 0 0.2 0 2 4 6 8 10 12 0 0.05 0 2 4 6 8 10 12 0.1 0.2 0.3 0.4 10%−0 40%−10 100%−40 Pb−MB p MB pp = 13 TeVsALICE pp = 5.02 TeV NN s Pb −p | < 0.75ηParticle | = 0.4R, T kJet: antic > 10 GeV/ ch T, jet p | < 0.35 jet η| S 0 2K Λ + Λ S 0 2K + Ξ + - Ξ S 0 2K + Ω + - Ω Λ + Λ + Ξ + - Ξ Λ + Λ + Ω + - Ω+ Ξ + - Ξ + Ω + - Ω )c (GeV/ T p Baryon-to-meson ratioBaryon-to-baryon ratio Figure 9.pT-dependent strange baryon-to-meson (top) and baryon-to-baryon (bottom) ratios for particles produced within jets in p–Pb collisions at √sNN = 5.02 TeV. For each case, the results for different event multiplicity classes are compared with that in pp collisions at √s= 13 TeV. The statistical uncertainties are represented by the vertical error bars and the systematic uncertainties by the boxes. See the text for details. located in the low-pTregion but the contribution is larger than in pp collisions. The UE fractions are 61.3% (47.0%), 80.4% (65.5%), 90.0% (73.4%) and 61.4% (62.0%) for K0 S, Λ, and Ξin the interval of 0.9< pT<1.6GeV/cand for Ωin the interval of 0.9< pT<2.2GeV/cin p–Pb (pp) collisions, respectively. This follows the expectation that the multiplicity of particles within the UE is correlated with the number of nucleon−nucleon interactions and the energy density of the system. The pT-dependent densities of K0 S mesons and Λand Ξbaryons with different selections are also measured for various event multiplicity classes, presented in figures 11,12, and 13 in appendix C. The magnitude of the density increases with the event multiplicity, and the results in the low multiplicity class become almost identical to those in pp collisions. The ratios of JE particles measured in p–Pb collisions at √sNN = 5.02 TeV are shown in figure 9. The results of Λ/K0 S,Ξ/K0 S, and Ξ/Λratios are presented in three event multiplicity classes, from high (0–10%), intermediate (10–40%) to low (40–100%) multiplicities, and compared with that in MB events. Since the multiplicity differential analysis is challenging for Ωbaryons due to the low number of candidates the ratios of Ω/K0 S,Ω/Λand Ω/Ξare only given for the MB events. All ratios are also compared with the corresponding measurements in pp collisions at √s= 13 TeV. Similar to what is observed in ref. [32], the Λ/K0 Sratio obtained in p–Pb collisions is systematically higher than that in pp collisions for 2< pT<4GeV/c: in this pTinterval the ratio in p–Pb collisions also increases with the event multiplicity. The differences are quantified in terms of standard deviations considering statistical and systematic uncertainties. The differences are 0.8σbetween MB p–Pb – 19 – JHEP07(2023)136 collisions and pp collisions and 1.1σbetween high- (0–10%) and low-multiplicity (40–100%) p–Pb collisions. A similar behavior is observed in ratios between other particle species. However, due to substantial uncertainties, variations between collision systems or among different event multiplicity classes for p–Pb collisions are much less significant than for the Λ/K0 Sratio. The observed deviations remain to be studied with better statistical precision. The comparisons of the ratios for JE particles to those for inclusive and UE particles for different event multiplicity classes are shown in figures 14,15, and 16 in appendix C for Λ/K0 S,Ξ/K0 S, and Ξ/Λ, respectively. Similar to what is observed in pp collisions, in each event multiplicity class, the pTdependence of the ratio for UE particles is consistent with that of inclusive particles within uncertainties. Furthermore, the enhancement of particle ratios at intermediate pTis not observed in jets, suggesting that the enhancement of particle ratios observed at high multiplicity in small systems is only present in the UE and not a feature of jet fragmentation. 5 Summary The production of K0 Smesons and Λ,Ξ, and Ωbaryons is measured separately for particles associated with hard scatterings and the underlying event for the first time at the LHC in pp collisions at √s= 13 TeV and p–Pb collisions at √sNN = 5.02 TeV. The results in pp collisions are compared with Pythia 8CR-BLC simulations. Pythia 8simulations reproduce fairly well the Λ/K0 Sratio in data. However, large discrepancies between data and simulations are observed in the ratios including multi-strange baryons. In p–Pb collisions, the strange baryon-to-meson and baryon-to-baryon yield ratios associated with jets for different event multiplicity classes have a similar trend. Better statistical precision is required to clarify their multiplicity and collision system dependence. The enhancement in the ratio at intermediate pTfound in the inclusive particle measurements in high-multiplicity p–Pb and Pb–Pb collisions is not present for particles associated with hard scatterings selected by jets reconstructed from charged particles for pch T,jet >10 GeV/c. Moreover, as the enhancement has been linked to the interplay of radial flow and parton recombination at intermediate pT, its absence within the jet cone demonstrates that these effects are indeed limited to the soft particle production processes. 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 – 20 – JHEP07(2023)136 (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 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), – 21 – JHEP07(2023)136 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: Marie Skłodowska Curie, European Research Council, Strong 2020 — Horizon 2020 (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. – 22 – JHEP07(2023)136 A Particle candidate selection criteria Topological variable pp p–Pb V0transverse decay radius >0.5cm >0.5cm DCA of V0daughter track to PV >0.06 cm >0.06 cm DCA between V0daughter tracks <1σ < 1σ CPA of V0>0.97 (0.995)>0.97 (0.995) Track selection Daughter track pseudorapidity interval |η|<0.8|η|<0.8 Daughter track Ncrossed rows ≥70 ≥70 Daughter track Ncrossed rows/Nfindable ≥0.8≥0.8 TPC dE/dx < 5σ < 5σ Candidate selection Pseudorapidity interval |η|<0.75 |η|<0.75 Proper decay length <20 (30) cm <20 (30) cm Competing mass >0.005 (0.010) GeV/c2>0.005 (0.010) GeV/c2 Table 5.K0 S(Λand Λ) candidate selection criteria of topological variables, daughter tracks and V0 candidates. 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Arnaldi 55, I.C. Arsene 19, M. Arslandok 137, A. Augustinus 32, R. Averbeck 97, M.D. Azmi 15, A. Badalà 52, J. Bae 104, Y.W. Baek 40, X. Bai 118, R. Bailhache 63, Y. Bailung 47, A. Balbino 29, A. Baldisseri 128, B. Balis 2, D. Banerjee 4, Z. Banoo 91, R. Barbera 26, F. Barile 31, L. Barioglio 95, M. Barlou78, G.G. Barnaföldi 136, L.S. Barnby 85, V. Barret 125, L. Barreto 110, C. Bartels 117, K. Barth 32, E. Bartsch 63, N. Bastid 125, S. Basu 75, G. Batigne 103, D. Battistini 95, B. Batyunya 141, D. Bauri46, J.L. Bazo Alba 101, I.G. Bearden 83, C. Beattie 137, P. Becht 97, D. Behera 47, I. Belikov 127, A.D.C. Bell Hechavarria 135, F. Bellini 25, R. Bellwied 114, S. Belokurova 140, V. Belyaev 140, G. Bencedi 136, S. Beole 24, A. Bercuci 45, Y. Berdnikov 140, A. Berdnikova 94, L. Bergmann 94, M.G. Besoiu 62, L. Betev 32, P.P. Bhaduri 132, A. Bhasin 91, M.A. Bhat 4, B. Bhattacharjee 41, L. Bianchi 24, N. Bianchi 48, J. Bielčík 35, J. Bielčíková 86, J. Biernat 107, A.P. Bigot 127, A. Bilandzic 95, G. Biro 136, S. Biswas 4, N. Bize 103, J.T. Blair 108, D. Blau 140, M.B. Blidaru 97, N. Bluhme38, C. Blume 63, G. Boca 21,54, F. Bock 87, T. Bodova 20, A. Bogdanov140, S. Boi 22, J. Bok 57, L. Boldizsár 136, A. Bolozdynya 140, M. Bombara 37, P.M. Bond 32, G. Bonomi 131,54, H. Borel 128, A. Borissov 140, A.G. Borquez Carcamo 94, H. Bossi 137, E. Botta 24, Y.E.M. Bouziani 63, L. Bratrud 63, P. Braun-Munzinger 97, M. Bregant 110, M. Broz 35, G.E. Bruno 96,31, M.D. Buckland 23, D. Budnikov 140, H. Buesching 63, S. Bufalino 29, O. Bugnon103, P. Buhler 102, Z. Buthelezi 67,121, 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, M. Carabas 124, A.A. Carballo 32, F. Carnesecchi 32, R. Caron 126, L.A.D. Carvalho 110, J. Castillo Castellanos 128, F. Catalano 24,29, C. Ceballos Sanchez 141, I. Chakaberia 74, P. Chakraborty 46, S. Chandra 132, S. Chapeland 32, M. Chartier 117, S. Chattopadhyay 132, S. Chattopadhyay 99, T.G. Chavez 44, T. Cheng 97,6, C. Cheshkov 126, B. Cheynis 126, V. Chibante Barroso 32, D.D. Chinellato 111, E.S. Chizzali II,95, J. Cho 57, S. Cho 57, P. Chochula 32, P. Christakoglou 84, C.H. Christensen 83, P. Christiansen 75, T. Chujo 123, M. Ciacco 29, C. Cicalo 51, F. Cindolo 50, M.R. Ciupek97, G. ClaiIII,50, F. Colamaria 49, J.S. Colburn100, D. Colella 96,31, M. Colocci 32, M. Concas IV,55, G. Conesa Balbastre 73, Z. Conesa del Valle 72, G. Contin 23, J.G. Contreras 35, M.L. Coquet 128, T.M. CormierI,87, P. Cortese 130,55, M.R. Cosentino 112, F. Costa 32, S. Costanza 21,54, C. Cot 72, J. Crkovská 94, P. Crochet 125, R. Cruz-Torres 74, E. Cuautle64, P. Cui 6, A. Dainese 53, M.C. Danisch 94, A. Danu 62, P. Das 80, P. Das 4, S. Das 4, A.R. Dash 135, S. Dash 46, A. De Caro 28, G. de Cataldo 49, J. de Cuveland38, A. De Falco 22, D. De Gruttola 28, N. De Marco 55, – 32 – JHEP07(2023)136 C. De Martin 23, S. De Pasquale 28, S. Deb 47, R.J. Debski 2, K.R. Deja133, R. Del Grande 95, L. Dello Stritto 28, W. Deng 6, P. Dhankher 18, D. Di Bari 31, A. Di Mauro 32, R.A. Diaz 141,7, T. Dietel 113, Y. Ding 126,6, R. Divià 32, D.U. Dixit 18, Ø. Djuvsland20, U. Dmitrieva 140, A. Dobrin 62, B. Dönigus 63, J.M. Dubinski133, A. Dubla 97, S. Dudi 90, P. Dupieux 125, M. Durkac106, N. Dzalaiova12, T.M. Eder 135, R.J. Ehlers 87, V.N. Eikeland20, F. Eisenhut 63, D. Elia 49, B. Erazmus 103, F. Ercolessi 25, F. Erhardt 89, M.R. Ersdal20, B. Espagnon 72, 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 48, M. Fasel 87, P. Fecchio29, A. Feliciello 55, G. Feofilov 140, A. Fernández Téllez 44, L. Ferrandi 110, M.B. Ferrer 32, A. Ferrero 128, C. Ferrero 55, A. Ferretti 24, V.J.G. Feuillard 94, V. Filova35, D. Finogeev 140, F.M. Fionda 51, F. Flor 114, A.N. Flores 108, S. Foertsch 67, I. Fokin 94, S. Fokin 140, E. Fragiacomo 56, E. Frajna 136, 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 114, P. Ganoti 78, C. Garabatos 97, J.R.A. Garcia 44, E. Garcia-Solis 9, K. Garg 103, C. Gargiulo 32, K. Garner135, P. Gasik 97, A. Gautam 116, M.B. Gay Ducati 65, M. Germain 103, C. Ghosh132, M. Giacalone 25, P. Giubellino 97,55, P. Giubilato 27, A.M.C. Glaenzer 128, P. Glässel 94, E. Glimos120, D.J.Q. Goh76, V. Gonzalez 134, L.H. González-Trueba 66, M. Gorgon 2, S. Gotovac33, V. Grabski 66, L.K. Graczykowski 133, E. Grecka 86, A. Grelli 58, 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. Gundem 63, T. Gunji 122, W. Guo 6, A. Gupta 91, R. Gupta 91, S.P. Guzman 44, L. Gyulai 136, M.K. Habib97, C. Hadjidakis 72, F.U. Haider 91, H. Hamagaki 76, A. Hamdi 74, M. Hamid6, Y. Han 138, R. Hannigan 108, M.R. Haque 133, J.W. Harris 137, A. Harton 9, H. Hassan 87, D. Hatzifotiadou 50, P. Hauer 42, L.B. Havener 137, S.T. Heckel 95, E. Hellbär 97, H. Helstrup 34, M. Hemmer 63, T. Herman 35, G. Herrera Corral 8, F. Herrmann135, S. Herrmann 126, K.F. Hetland 34, B. Heybeck 63, H. Hillemanns 32, C. Hills 117, B. Hippolyte 127, B. Hofman 58, B. Hohlweger 84, G.H. Hong 138, M. Horst 95, A. Horzyk 2, R. Hosokawa14, Y. Hou 6, P. Hristov 32, C. Hughes 120, P. Huhn63, L.M. Huhta 115, C.V. Hulse 72, T.J. Humanic 88, A. Hutson 114, D. Hutter 38, J.P. Iddon 117, R. Ilkaev140, H. Ilyas 13, M. Inaba 123, G.M. Innocenti 32, M. Ippolitov 140, A. Isakov 86, T. Isidori 116, 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, L. Jaffe38, C. Jahnke111, M.J. Jakubowska 133, M.A. Janik 133, T. Janson69, M. Jercic89, S. Jia 10, A.A.P. Jimenez 64, F. Jonas 87, J.M. Jowett 32,97, J. Jung 63, M. Jung 63, A. Junique 32, A. Jusko 100, M.J. Kabus 32,133, J. Kaewjai105, P. Kalinak 59, A.S. Kalteyer 97, A. Kalweit 32, V. Kaplin 140, A. Karasu Uysal 71, D. Karatovic 89, O. Karavichev 140, T. Karavicheva 140, P. Karczmarczyk 133, E. Karpechev 140, U. Kebschull 69, R. Keidel 139, D.L.D. Keijdener58, M. Keil 32, B. Ketzer 42, A.M. Khan 6, S. Khan 15, A. Khanzadeev 140, Y. Kharlov 140, A. Khatun 116,15, A. Khuntia 107, M.B. Kidson113, B. Kileng 34, B. Kim 16, C. Kim 16, D.J. Kim 115, E.J. Kim 68, J. Kim 138, J.S. Kim 40, J. Kim 94, J. Kim 68, M. Kim 18,94, S. Kim 17, T. Kim 138, K. Kimura 92, S. Kirsch 63, I. Kisel 38, S. Kiselev 140, A. Kisiel 133, J.P. Kitowski 2, – 33 – JHEP07(2023)136 J.L. Klay 5, J. Klein 32, S. Klein 74, C. Klein-Bösing 135, M. Kleiner 63, T. Klemenz 95, A. Kluge 32, A.G. Knospe 114, C. Kobdaj 105, T. Kollegger97, A. Kondratyev 141, N. Kondratyeva 140, E. Kondratyuk 140, J. Konig 63, S.A. Konigstorfer 95, P.J. Konopka 32, G. Kornakov 133, S.D. Koryciak 2, A. Kotliarov 86, V. Kovalenko 140, M. Kowalski 107, V. Kozhuharov 36, I. Králik 59, A. Kravčáková 37, L. Kreis97, M. Krivda 100,59, F. Krizek 86, K. Krizkova Gajdosova 35, M. Kroesen 94, M. Krüger 63, D.M. Krupova 35, E. Kryshen 140, V. Kučera 32, C. Kuhn 127, P.G. Kuijer 84, T. Kumaoka123, D. Kumar132, 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 57, J.Y. Kwon 57, Y. Kwon 138, S.L. La Pointe 38, P. La Rocca 26, Y.S. Lai74, A. Lakrathok105, M. Lamanna 32, R. Langoy 119, P. Larionov 32, E. Laudi 32, L. Lautner 32,95, R. Lavicka 102, T. Lazareva 140, R. Lea 131,54, H. Lee 104, G. Legras 135, J. Lehrbach 38, R.C. Lemmon 85, I. León Monzón 109, M.M. Lesch 95, E.D. Lesser 18, M. Lettrich95, P. Lévai 136, X. Li10, X.L. Li6, J. Lien 119, R. Lietava 100, B. Lim 24,16, S.H. Lim 16, V. Lindenstruth 38, A. Lindner45, C. Lippmann 97, A. Liu 18, D.H. Liu 6, J. Liu 117, I.M. Lofnes 20, C. Loizides 87, S. Lokos 107, J. Lomker 58, P. Loncar 33, J.A. Lopez 94, X. Lopez 125, E. López Torres 7, P. Lu 97,118, J.R. Luhder 135, M. Lunardon 27, G. Luparello 56, Y.G. Ma 39, A. Maevskaya140, M. Mager 32, T. Mahmoud42, A. Maire 127, M.V. Makariev 36, M. Malaev 140, G. Malfattore 25, N.M. Malik 91, Q.W. Malik19, S.K. Malik 91, L. Malinina V II,141, D. Mal’Kevich 140, D. Mallick 80, N. Mallick 47, G. Mandaglio 30,52, V. Manko 140, F. Manso 125, V. Manzari 49, Y. Mao 6, G.V. Margagliotti 23, A. Margotti 50, A. Marín 97, C. Markert 108, P. Martinengo 32, J.L. Martinez114, M.I. Martínez 44, G. Martínez García 103, S. Masciocchi 97, M. Masera 24, A. Masoni 51, L. Massacrier 72, A. Mastroserio 129,49, 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 121, A.F. Mechler63, Y. Melikyan 43,140, A. Menchaca-Rocha 66, E. Meninno 102,28, A.S. Menon 114, M. Meres 12, S. Mhlanga113,67, Y. Miake123, L. Micheletti 55, L.C. Migliorin126, D.L. Mihaylov 95, K. Mikhaylov 141,140, A.N. Mishra 136, D. Miśkowiec 97, A. Modak 4, A.P. Mohanty 58, B. Mohanty 80, M. Mohisin Khan V,15, M.A. Molander 43, Z. Moravcova 83, C. Mordasini 95, D.A. Moreira De Godoy 135, I. Morozov 140, A. Morsch 32, T. Mrnjavac 32, V. Muccifora 48, S. Muhuri 132, J.D. Mulligan 74, A. Mulliri22, M.G. Munhoz 110, R.H. Munzer 63, H. Murakami 122, S. Murray 113, L. Musa 32, J. Musinsky 59, J.W. Myrcha 133, B. Naik 121, A.I. Nambrath 18, B.K. Nandi46, R. Nania 50, E. Nappi 49, A.F. Nassirpour 75, A. Nath 94, C. Nattrass 120, M.N. Naydenov 36, A. Neagu19, A. Negru124, L. Nellen 64, 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 50, S. Noh 11, P. Nomokonov 141, J. Norman 117, N. Novitzky 123, P. Nowakowski 133, A. Nyanin 140, J. Nystrand 20, M. Ogino 76, A. Ohlson 75, V.A. Okorokov 140, J. Oleniacz 133, A.C. Oliveira Da Silva 120, M.H. Oliver 137, A. Onnerstad 115, C. Oppedisano 55, A. Ortiz Velasquez 64, J. Otwinowski 107, M. Oya92, K. Oyama 76, Y. Pachmayer 94, S. Padhan 46, D. Pagano 131,54, G. Paić 64, A. Palasciano 49, S. Panebianco 128, H. Park 123, H. Park 104, J. Park 57, J.E. Parkkila 32, R.N. Patra91, B. Paul 22, H. Pei 6, – 34 – JHEP07(2023)136 T. Peitzmann 58, X. Peng 6, M. Pennisi 24, L.G. Pereira 65, D. Peresunko 140, G.M. Perez 7, S. Perrin 128, Y. Pestov140, V. Petráček 35, V. Petrov 140, M. Petrovici 45, R.P. Pezzi 103,65, S. Piano 56, M. Pikna 12, P. Pillot 103, O. Pinazza 50,32, L. Pinsky114, C. Pinto 95, S. Pisano 48, M. Płoskoń 74, M. Planinic89, F. Pliquett63, M.G. Poghosyan 87, B. Polichtchouk 140, S. Politano 29, N. Poljak 89, A. Pop 45, S. Porteboeuf-Houssais 125, V. Pozdniakov 141, K.K. Pradhan 47, S.K. Prasad 4, S. Prasad 47, R. Preghenella 50, F. Prino 55, C.A. Pruneau 134, I. Pshenichnov 140, M. Puccio 32, S. Pucillo 24, Z. Pugelova106, S. Qiu 84, L. Quaglia 24, R.E. Quishpe114, S. Ragoni 14,100, A. Rakotozafindrabe 128, L. Ramello 130,55, F. Rami 127, S.A.R. Ramirez 44, T.A. Rancien73, M. Rasa 26, S.S. Räsänen 43, R. Rath 50, M.P. Rauch 20, I. Ravasenga 84, K.F. Read 87,120, C. Reckziegel 112, A.R. Redelbach 38, K. Redlich V I,79, C.A. Reetz 97, 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 62, M. Rodríguez Cahuantzi 44, K. Røed 19, R. Rogalev 140, E. Rogochaya 141, T.S. Rogoschinski 63, D. Rohr 32, D. Röhrich 20, P.F. Rojas44, S. Rojas Torres 35, P.S. Rokita 133, G. Romanenko 141, F. Ronchetti 48, A. Rosano 30,52, E.D. Rosas64, A. Rossi 53, A. Roy 47, S. Roy46, N. Rubini 25, O.V. Rueda 114,75, D. Ruggiano 133, R. Rui 23, B. Rumyantsev141, P.G. Russek 2, R. Russo 84, A. Rustamov 81, E. Ryabinkin 140, Y. Ryabov 140, A. Rybicki 107, H. Rytkonen 115, W. Rzesa 133, O.A.M. Saarimaki 43, R. Sadek 103, S. Sadhu 31, S. Sadovsky 140, J. Saetre 20, K. Šafařík 35, S.K. Saha 4, S. Saha 80, B. Sahoo 46, R. Sahoo 47, S. Sahoo60, D. Sahu 47, P.K. Sahu 60, J. Saini 132, K. Sajdakova37, S. Sakai 123, M.P. Salvan 97, S. Sambyal 91, I. Sanna 32,95, T.B. Saramela110, D. Sarkar 134, N. Sarkar132, P. Sarma41, V. Sarritzu 22, V.M. Sarti 95, M.H.P. Sas 137, J. Schambach 87, H.S. Scheid 63, 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 120, R. Schotter 127, A. Schröter 38, J. Schukraft 32, K. Schwarz97, K. Schweda 97, G. Scioli 25, E. Scomparin 55, J.E. Seger 14, Y. Sekiguchi122, D. Sekihata 122, I. Selyuzhenkov 97,140, S. Senyukov 127, J.J. Seo 57, D. Serebryakov 140, L. Šerkšnyt˙e 95, A. Sevcenco 62, T.J. Shaba 67, A. Shabetai 103, R. Shahoyan32, A. Shangaraev 140, A. Sharma90, B. Sharma 91, D. Sharma 46, H. Sharma 107, M. Sharma 91, S. Sharma 76, S. Sharma 91, U. Sharma 91, A. Shatat 72, O. Sheibani114, K. Shigaki 92, M. Shimomura77, J. Shin11, S. Shirinkin 140, Q. Shou 39, Y. Sibiriak 140, S. Siddhanta 51, 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 47, S. Singh 15, V.K. Singh 132, V. Singhal 132, T. Sinha 99, B. Sitar 12, M. Sitta 130,55, T.B. Skaali19, G. Skorodumovs 94, M. Slupecki 43, N. Smirnov 137, R.J.M. Snellings 58, E.H. Solheim 19, J. Song 114, A. Songmoolnak105, F. Soramel 27, R. Spijkers 84, I. Sputowska 107, J. Staa 75, J. Stachel 94, I. Stan 62, P.J. Steffanic 120, S.F. Stiefelmaier 94, D. Stocco 103, I. Storehaug 19, P. Stratmann 135, S. Strazzi 25, C.P. Stylianidis84, A.A.P. Suaide 110, C. Suire 72, M. Sukhanov 140, M. Suljic 32, R. Sultanov 140, V. Sumberia 91, S. Sumowidagdo 82, S. Swain60, I. Szarka 12, S.F. Taghavi 95, G. Taillepied 97, 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, – 35 – JHEP07(2023)136 G.F. Tassielli 31, A. Tauro 32, G. Tejeda Muñoz 44, A. Telesca 32, L. Terlizzi 24, C. Terrevoli 114, G. Tersimonov3, S. Thakur 4, D. Thomas 108, A. Tikhonov 140, A.R. Timmins 114, M. Tkacik106, T. Tkacik 106, A. Toia 63, R. Tokumoto92, 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, A. Tumkin 140, R. Turrisi 53, T.S. Tveter 19, K. Ullaland 20, B. Ulukutlu 95, A. Uras 126, M. Urioni 54,131, G.L. Usai 22, M. Vala37, N. Valle 21, L.V.R. van Doremalen58, M. van Leeuwen 84, C.A. van Veen 94, R.J.G. van Weelden 84, P. Vande Vyvre 32, D. Varga 136, Z. Varga 136, M. Vasileiou 78, A. Vasiliev 140, O. Vázquez Doce 48, V. Vechernin 140, E. Vercellin 24, S. Vergara Limón44, L. Vermunt 97, R. Vértesi 136, M. Verweij 58, L. Vickovic33, Z. Vilakazi121, O. Villalobos Baillie 100, G. Vino 49, A. Vinogradov 140, T. Virgili 28, V. Vislavicius83, A. Vodopyanov 141, B. Volkel 32, M.A. Völkl 94, K. Voloshin140, S.A. Voloshin 134, 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 135, S.L. Weyhmiller 137, J. Wiechula 63, J. Wikne 19, G. Wilk 79, J. Wilkinson 97, G.A. Willems 135, B. Windelband94, 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. Yamaguchi92, S. Yang20, S. Yano 92, Z. Yin 6, I.-K. Yoo 16, J.H. Yoon 57, S. Yuan20, A. Yuncu 94, V. Zaccolo 23, C. Zampolli 32, F. Zanone 94, N. Zardoshti 32,100, A. Zarochentsev 140, P. Závada 61, N. Zaviyalov140, M. Zhalov 140, B. Zhang 6, L. Zhang 39, S. Zhang 39, X. Zhang 6, Y. Zhang118, 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 55, 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 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 – 36 – JHEP07(2023)136 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 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 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 – 37 – JHEP07(2023)136 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 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 University of Cape Town, Cape Town, South Africa 114 University of Houston, Houston, Texas, United States – 38 – JHEP07(2023)136 115 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 – 39 –