Light-flavor particle production in high-multiplicity pp collisions at √s=13 TeV as a function of transverse spherocity

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/ Light-flavor particle production in high-multiplicity pp collisions at √s=13 TeV as a function of transverse spherocity © CERN, for the beneft of the ALICE Collaboration. Article funded by SCOAP3 . Published version The ALICE collaboration The ALICE collaboration. (2024). Light-flavor particle production in high-multiplicity pp collisions at √s=13 TeV as a function of transverse spherocity. Journal of High Energy Physics, 2024, Article 184. https://doi.org/10.1007/JHEP05(2024)184 2024 JHEP05(2024)184 Published for SISSA by Springer Received: October 30, 2023 Accepted: April 9, 2024 Published: May 15, 2024 Light-flavor particle production in high-multiplicity pp collisions at √s=13 TeV as a function of transverse spherocity The ALICE collaboration E-mail: [email protected] Abstract: Results on the transverse spherocity dependence of light-flavor particle production ( π , K, p, ϕ , K∗0 ,K 0 S ,Λ,Ξ) at midrapidity in high-multiplicity pp collisions at √s = 13 TeV were obtained with the ALICE apparatus. The transverse spherocity estimator ( SpT=1 O ) categorizes events by their azimuthal topology. Utilizing narrow selections on SpT=1 O , it is possible to contrast particle production in collisions dominated by many soft initial interactions with that observed in collisions dominated by one or more hard scatterings. Results are reported for two multiplicity estimators covering different pseudorapidity regions. The SpT=1 O estimator is found to effectively constrain the hardness of the events when the midrapidity ( |η|< 0 . 8) estimator is used. The production rates of strange particles are found to be slightly higher for soft isotropic topologies, and severely suppressed in hard jet-like topologies. These effects are more pronounced for hadrons with larger mass and strangeness content, and observed when the topological selection is done within a narrow multiplicity interval. This demonstrates that an important aspect of the universal scaling of strangeness enhancement with final-state multiplicity is that high-multiplicity collisions are dominated by soft, isotropic processes. On the contrary, strangeness production in events with jet-like processes is significantly reduced. The results presented in this article are compared with several QCD-inspired Monte Carlo event generators. Models that incorporate a two-component phenomenology, either through mechanisms accounting for string density, or thermal production, are able to describe the observed strangeness enhancement as a function of SpT=1 O . Keywords: Hadron-Hadron Scattering , Particle and Resonance Production ArXiv ePrint: 2310.10236 Open Access, Copyright CERN, for the benefit of the ALICE Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP05(2024)184 JHEP05(2024)184 Contents 1 Introduction 1 2 Experimental setup and event selection 3 2.1 Event selection 4 3 Unweighted transverse spherocity SpT=1 O5 4 Measurements of transverse momentum spectra 8 4.1 Particle identification utilizing primary charged tracks 9 4.2 Particle identification utilizing weak decay topology 11 5 Corrections 12 6 Systematic uncertainties 14 6.1 Systematic uncertainties for analyses utilizing primary charged particles 15 6.2 Systematic uncertainties on analyses of long-lived particles 17 7 Results and discussion 19 7.1 High-multiplicity estimators and SpT=1 O20 7.2 Results of SpT=1 O-differential pTspectra at N|η|<0.8 tracklets 0–1% 21 7.3 Particle ratios for N|η|<0.8 tracklets 0–1% 26 7.4 Integrated yields as a function of SpT=1 O33 7.5 SpT=1 Oresults with a broadened multiplicity range 38 8 Summary and conclusions 42 The ALICE collaboration 49 1 Introduction Studies of high-multiplicity proton-proton (pp) and proton-lead ( p–Pb ) collisions have revealed that small collision systems exhibit signatures previously considered unique features of heavy-ion collisions. Some of these signatures, such as the enhanced production of strange hadrons [ 1 ], and collective flow [ 2 , 3 ], can be explained by the formation of a strongly interacting medium. Strangeness enhancement was one of the first proposed quark-gluon plasma (QGP) signatures [ 4 ], as the QGP-transition temperature is typically expected of being in the order of the strange quark mass, allowing for thermal production of strange quarks in a QGP. Collectivity was historically also expected to require thermalization and provide information on the equation of state [ 5 ], while more recently it has been understood how collectivity can build up from kinetic equilibrium alone [ 6 ]. However, the formation of a medium in these small systems challenges current theoretical frameworks, because their – 1 – JHEP05(2024)184 initial small volumes imply lifetimes so short that it is unclear to what degree the systems can equilibrate (see ref. [ 6 ] and references therein). The observation of collective flow, as well as strangeness enhancement in particular, implies that pp collisions at LHC and RHIC energies can no longer be described as semiincoherent sums of parton-parton collisions, an idea that has been central to most generalpurpose quantum chromodynamics (QCD)-inspired Monte-Carlo event generators, such as PYTHIA [ 7 ] and Herwig [ 8 ]. “Jet Universality” is another long-standing idea for the phenomenological understanding of QCD assuming that, while the partonic processes vary with system and beam energy, the produced color fields and their hadronization are universal. In the context of Lund strings, this implies that the string tension and string-fragmentation parameters are the same regardless of collision system. For a recent discussion, we refer to ref. [ 9 ]. The discovery of strangeness enhancement scaling with the multiplicity [ 10 , 11 ] violates the assumptions of jet universality, and for this reason QCD-inspired generators have incorporated additional phenomenological final-state pre-hadronization mechanisms, such as string percolation [ 12 ], color ropes [ 13 ], baryon junctions [ 14 ] and/or new types of baryon-favored color reconnection [ 15 ]. Furthermore, charm fragmentation fractions in pp collisions also differ significantly from the values measured in e+e− collisions [ 16 ]. In contrast, QGP-inspired models include a system evolution with volume and multiplicity and so the qualitative features of both collective flow and strangeness enhancement are expected. EPOS-LHC is an event generator where the initial interactions lead to a two-phase state (core-corona) consisting of a dense core of QGP, and a diluted corona [ 17 ]. Strangeness enhancement in EPOS-LHC is due to a change in the relative contribution of the corona (low strangeness production from pomerons) and core (high thermal strangeness production from QGP) with multiplicity. As can be seen from the previous discussion, strange-particle production appears to be a powerful probe for QGP-like effects in small systems, and the origin of the strangeness enhancement constitutes an important open question. Therefore, results on strangeness production in small systems can facilitate progress in the understanding of both QCD dynamics and hadronization, in addition to putting constraints on phenomenological models. ALICE has previously reported that strangeness enhancement as a function of the average multiplicity at midrapidity does not depend on the collision system nor center-of-mass energy per nucleon-nucleon collision, ranging from pp to p-Pb and √sNN = 2 . 76 TeV to √s = 13 TeV [ 18 ]. This universality implies a strong correlation between the underlying physics processes that drive both the enhancement and the multiplicity. In this article, we have tested if the observed universality can be broken by contrasting high-multiplicity events dominated by one or more hard scatterings, with events dominated by multiple softer interactions. At high transverse momentum ( pT ), strangeness production in hadronic collisions tends to originate from “hard”, perturbative QCD processes. These hadrons are either produced directly through flavor creation ( XX → s ¯s ) or flavor excitation (s X→ s X ), or indirectly as a result of radiation and/or hadronization associated with hard processes, e.g., through gluon splitting following the partonic evolution (g → s ¯s ). In contrast, the production of “soft”, lowpT strange hadrons ( pT≤ 2 GeV/c ) is dominated by non-perturbative QCD processes, – 2 – JHEP05(2024)184 where novel QCD dynamics could be found. The final-state azimuthal topology is expected to reflect which of these QCD processes are primarily driving particle production for a given event. Events dominated by one or more hard scatterings will presumably lead to pronounced back-to-back jet structures, while events that contain several softer scatterings will result in isotropic distributions. We note that previous results obtained by ALICE, both utilizing the same event shape estimator [ 19 ] and a similar one, the transverse sphericity [ 20 ], found evidence for this behavior in the way that ⟨pT⟩ would depend on the event topology. To identify the final-state azimuthal topology, we will here use a modified variant of the transverse spherocity ( SO ) estimator proposed in ref. [ 19 ], for being more sensitive to the underlying processes. The lower bound of SO aims to distinguish “Jet-like” events, which in this study are events characterized by an azimuthal topology similar to a pair of back-to-back jets (implying a tight clustering of particles with a difference in azimuthal angle ∆ ϕ≈ 0or π [ 19 ]), which on average produces a larger number of highpT hadrons compared with the SO - integrated distribution, thereby “hardening” the pT -differential spectra. Conversely, the upper bound of SO selects “Isotropic” events, defined by an azimuthal topology which is close to symmetric, with an absence of preferred direction. The hypothesis is that SO , by contrasting events with these different topologies, can be used to control the degree of QGP-like effects, like strangeness enhancement and radial flow, in high-multiplicity pp collisions [ 21 , 22 ]. In this article, we present the first results on strangeness production in high-multiplicity pp collisions at √s = 13 TeV as a function of the transverse spherocity. The results obtained for the light-flavor hadrons are presented as the sum of particles and anti-particles, explicitly as π+ + π− ,K + +K − ,K ∗0 + K∗0 , p + p ,Λ+ Λ ,Ξ − +Ξ + where the exceptions are K 0 S and ϕ . Hereinafter, the sum of particle and anti-particles will be referred to as π , K, K 0 S , K ∗0 , p, ϕ ,Λ, and Ξ, unless otherwise explicitly mentioned. The article is organized as follows. The ALICE main detectors used in this analysis are detailed in section 2. Section 2.1 describes the high-multiplicity definitions used throughout this article. The transverse spherocity observable, along with caveats, is defined in section 3. The details concerning particle identification (PID), yield extraction, and correction procedures are discussed in section 4. The details regarding experimental corrections and systematic uncertainties are discussed in sections 5and 6, respectively. The results are reported in section 7, and finally the summary and conclusions of our study are discussed in section 8. 2 Experimental setup and event selection A detailed description of the ALICE apparatus in its Run 1 and 2 configuration, as well as its performance, can be found in refs. [ 23 , 24 ]. This section will briefly describe the main ALICE detectors used for the event selection, SpT=1 O determination, and extraction of particle spectra. All radii in the following are given as distances from the beam axis. The detectors of the ALICE apparatus can be grouped as follows: the central barrel at midrapidity, the muon arm at forward rapidity, and the forward global detectors. To be able to efficiently trigger on inelastic pp collisions, ALICE employs two forward scintillator arrays, V0A and V0C, with a pseudorapidity coverage of 2 . 8 < η < 5 . 1and − 3 . 7 < η < − 1 . 7, respectively. – 3 – JHEP05(2024)184 Both SpT=1 O and particle yields are determined by using tracks in the central barrel. The main detectors for tracking in the central barrel are the Inner Tracking System (ITS) and the Time Projection Chamber (TPC). The ITS detector is composed of six layers of cylindrical silicon detectors with full azimuthal acceptance, where the radius of the innermost (outermost) layer is 3 . 9cm (43 cm). The innermost layers consist of two arrays of hybrid silicon pixel detectors (SPD), whose fine granularity provides high precision tracking closest to the primary vertex (PV). The SPD is also used to reconstruct tracklets, short two-point track segments covering the pseudorapidity region |η|<1.4 . The tracklets provide both efficient primary vertex determination and a precise estimate of the charged-particle multiplicity. The remaining layers of the ITS are only used for tracking in the analyses presented here. The TPC is the primary tracking device in ALICE that provides a full three-dimensional trajectory for each track. It is a large cylindrical detector that surrounds the ITS detector with an inner and outer radii of 85cm and 250cm, respectively. It has full azimuthal acceptance and a pseudorapidity coverage of − 0 . 9 < η < 0 . 9for full-length tracks. The ITS and TPC are situated inside the solenoidal L3 magnet with a uniform magnetic field of 0.5T. For global tracks, where the full information of the ITS and TPC are used together, a momentum resolution of 1–10% is achieved for momenta ranging from 0.05 to 100 GeV/c . The ALICE apparatus utilizes a broad range of different particle identification techniques to identify the mass of each analyzed particle. For weakly-decaying V0 (K 0 S ,Λ) and Cascades (Ξ), the TPC tracking is able to provide topological track matching of the decay products, where particles are then identified through peaks in invariant mass distributions. For the other particles, the PID is provided by the specific energy loss, d E /d x , measured in the TPC and the particle’s velocity (given a measured momentum in the TPC) is provided by the Time-of-flight (TOF) detector. The TOF consists of Multi-Gap Resistive Plate Chambers (MRPC), which are used for the particle identification by measuring the total time of flight of the identified hadrons. It is located at about 3.7 m from the interaction point, with a full azimuthal acceptance and has a pseudorapidity coverage of − 0 . 9 < η < 0 . 9. Details of the particle identification procedure are given in section 4. 2.1 Event selection Data used in this analysis were collected with a minimum bias trigger, which requires one or more hits in both V0 scintillator arrays in coincidence with proton beams from both directions. The contamination from beam-induced background is removed offline by using the timing information in the V0 detectors and taking into account the correlation between tracklets and clusters in the SPD detector, as discussed in detail in [ 24 ]. The primary vertex is reconstructed by correlating hits in the two SPD layers and only events with a primary vertex within ± 10cm of the nominal interaction point along the beam direction are accepted for this analysis. Due to the fast readout time of the SPD, any contamination from out-of-bunch pile-up is rejected. The contamination from in-bunch pile-up events is removed offline by excluding events with multiple vertices reconstructed in the SPD. Any remaining pile-up will be from collisions which produce little or no particles. Due to the required high-multiplicity event-selection, this will have a negligible impact on the final results presented in this article, The measurements reported here were performed on minimum-bias triggered events that additionally have at least one charged particle measured in the pseudorapidity interval |η|< 1 – 4 – JHEP05(2024)184 ( INEL > 0) [ 25 ], corresponding to about 75% of the total inelastic cross section. Two different multiplicity estimators are used, the total charge deposited in the full coverage of both V0 detectors (V0M), and the number of SPD tracklets within |η|<0.8(N|η|<0.8 tracklets ). For each of these estimators, the multiplicity is classified as a percentile, where 0% corresponds to the highest and 100% to the lowest multiplicity. The high-multiplicity events used throughout this article include the top-1% (0–1%) and the top-10% (0–10%) multiplicity percentiles, with a minimum of 10 reconstructed charged primary tracks at midrapidity ( |η|<0 .8). 3 Unweighted transverse spherocity SpT=1 O In this analysis, the unweighted transverse spherocity SpT=1 O is used to quantify the topology in the azimuthal plane. It is calculated as SpT=1 O=π2 4min ˆnΣi|ˆpT,i ׈n| Ntrks 2 .(3.1) The sum is calculated over all charged particles with pT> 0 . 15 GeV/c , where ˆpT represents the transverse momentum unit vector, Ntrks is the number of charged particles in a given event and ˆn is the unit vector that minimizes SpT=1 O . Loose selection criteria were chosen for primary tracks to ensure a high efficiency and uniform azimuthal acceptance over the full TPC volume. At least 50 or more measured clusters are required for a track in the TPC. Furthermore, the TPC tracks must be matched to hits in the ITS, but, to ensure homogeneous azimuthal acceptance, the tracks are not explicitly required to have hits in the SPD. These requirements improve the tracking precision, and reject tracks from out-of-bunch pile-up. Finally, selections of distance of closest approach (DCA) along the beam axis ( |DCAz|< 3 . 2cm), and in the xy-plane ( |DCAxy|< 2 . 4cm) are applied, ensuring that the reconstructed TPC track points to the primary vertex. Unlike the traditional transverse spherocity estimator discussed in [ 19 ], the transverse momentum ( pT ) of each track in this article is normalized to 1 ( pT = 1) when measuring the SpT=1 O . This modification was required to have similar sensitivity between neutral and charged particles. Otherwise, there would be significant differences between the production of neutral and charged kaons in jet-like events. This can be understood considering that for the traditional spherocity, a single highpT track will have a large weight in the spherocity calculation, which can occur for a charged kaon but never for a neutral kaon. By assigning the same weight to all measured primary charged tracks, one reduces the possible chargedvs.-neutral biases. However, this also means that results obtained with the two different spherocity estimators (traditional and unweighted) can only be qualitatively compared. Furthermore, as SpT=1 O is independent of the pT of the particles, it requires a substantial amount of particles to be a good measure of the event topology. For this reason, the number of charged tracks is required to be greater or equal to 10 for events included in this analysis, which limits the applicability of the unweighted spherocity estimator to only apply for high-multiplicity pp collisions, where the average d Nch/ d η is greater than 10. The values of SpT=1 O will, by construction, lie between 0 and 1. Having events with SpT=1 O≈ 0imply that |ˆpT׈n| ≈ 0for all tracks, which indicates that all tracks are parallel – 5 – JHEP05(2024)184 in the azimuthal plane, suggesting that the event is dominated by a single back-to-back dijet. Events where SpT=1 O→ 1 imply that all particles are uniformly distributed azimuthally, suggesting the absence of a preferred direction. Note that part of the isotropy of the event can be affected by anisotropic flow [ 2 ]. The unfolded SpT=1 O distributions are presented in figure 1, along with model predictions, for the three different multiplicity selections used in this article. Particle candidates that enter into the SpT=1 O calculation for the model predictions are required to meet the ALICE definition of a primary charged particle [ 26 ]. The correction of the SpT=1 O distributions is based on the Bayesian unfolding technique [ 27 ]. The same unfolding method used in previous ALICE publications [ 28 ] is applied here. It is crucial to emphasize that, unlike the distributions presented in figure 1, the individual particle pT -spectra are not corrected with Bayesian unfolding. A key aspect of the analysis presented in this article is the capability to compare the measured results with MC generator predictions. Extensive studies were performed to understand and mitigate any experimental biases due to the SpT=1 O selection. The experimental bias is evaluated by generating PYTHIA 8 1 events, that are propagated through a full ALICE simulation of the experimental apparatus. The SpT=1 O definition has been constructed to require that the generated events agree with the reconstructed simulated events, where the pTspectra have been corrected for the minimum bias reconstruction efficiency. 2 This ensures that the measured and generated results are directly comparable, where the measured results do not require further unfolding. This is achieved by adopting the following criteria, which minimizes the experimental uncertainty and makes SpT=1 O a robust and model-independent observable: •SpT=1 Oselection in quantiles. One major source of experimental bias is the smearing of the measured SpT=1 O distribution by detector effects. It was verified through MC studies that one can minimize the effect of the smearing by measuring SpT=1 O in quantiles, similar to how multiplicity classes are defined in ALICE [ 19 ]. The stability of the quantiles is due to the fact that spherocity distribution does not have large gradients. A robust model-to-data comparison is achieved by calculating the model comparison in measured percentiles, rather than for the numerical SpT=1 Oranges we report in this article. •Exclusion of neutral decay modes for resonance particles. The decay daughters of both ϕ and K ∗0 meet the standard ALICE definition of primary particles [ 26 ], subsequently leading to the resonance daughters entering the measurement of SpT=1 O for a given event. These daughters will contribute to the multiplicity estimate if measured at midrapidity, and as both the ϕ and K ∗0 resonances have charged and neutral decay modes, the branching ratio for the charged decay mode will artificially inflate in high-multiplicity events. This bias can be completely avoided by only including (and correcting for) the following charged decay modes for resonance particles in the resulting spectra: ϕ→K+K−and K∗0→Kπ. 1 The fully simulated PYTHIA events are the same used to obtain the tracking efficiency correction discussed in section 4. We refer to this section for technical details on the simulation. 2Secondary particles were rejected using MC information. – 6 – JHEP05(2024)184 =1 T p 0 S =1 T p 0 S/dN d ev N1/ Model-to-Data 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.6 0.8 1 1.2 1.4 0 0.02 0.04 0.06 0.08 0.1 = 13 TeVspp, ALICE )c 0.15 (GeV/≥ T p 10≥ ch N I, |<0.8η| tracklets N | < 0.8η| Data PYTHIA 8.2 Monash PYTHIA 8.2 Ropes Herwig 7.2 EPOS-LHC =1 T p 0 S =1 T p 0 S/dN d ev N1/ Model-to-Data 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.6 0.8 1 1.2 1.4 0 0.02 0.04 0.06 0.08 0.1 = 13 TeVspp, ALICE )c 0.15 (GeV/≥ T p 10≥ ch N I-III, |<0.8η| tracklets N | < 0.8η| Data PYTHIA 8.2 Monash PYTHIA 8.2 Ropes Herwig 7.2 EPOS-LHC =1 T p 0 S =1 T p 0 S/dN d ev N1/ Model-to-Data 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.6 0.8 1 1.2 1.4 0 0.02 0.04 0.06 0.08 0.1 = 13 TeVspp, ALICE )c 0.15 (GeV/≥ T p 10≥ ch NV0M I, | < 0.8η| Data PYTHIA 8.2 Monash PYTHIA 8.2 Ropes Herwig 7.2 EPOS-LHC Figure 1. Upper panels: the measured and fully corrected SpT=1 O distributions. Lower panels: ratio between model calculations and experimental data. These are presented for N|η|<0.8 tracklets I (top), I–III (middle) and V0M I (bottom). The roman numerals I (I–III) correspond the top 0–1% (0–10%) multiplicity for each respective estimator. The curves represent different model predictions, where the shaded area represents the statistical uncertainty of the models. The relative systematic uncertainty is shown as a gray area around unity in the lower panels. – 7 – JHEP05(2024)184 Selection variable Selection criteria Topology of Ξ DCA between daughters (V0and π)<1.6cm Cosine of pointing angle >0.97 Cascade transverse decay radius >0.8cm DCA of bachelor to PV >0.05 cm Topology of secondary Λ DCA between daughters (p and π)<1.6cm V0impact parameter >0.07 cm V0transverse decay radius >1.4cm Window around Λmass <0.006 (GeV/c2) DCA of daughters to PV >0.04 cm Common bachelor/daughter track selection Track pseudorapidity |η|<0.8 TPC clusters >70 TPC PID of daughters <5σ Table 5. Summary of the topological selection values used for the Ξcandidate selection. All impact parameter requirements on tracks are 2D (xy). the probabilities of Ξ ± and Ξ 0 decaying into Λ( ¯ Λ ) at given transverse momenta. The feed-down matrix is calculated in minimum bias events and the Ξ ±pT spectra used for the final correction come from the same high-multiplicity and spherocity event selections. The secondary yields do not exceed 25% of the total yields across the entire pT -range. 6 Systematic uncertainties The estimation of the total systematic uncertainties for each SpT=1 O distribution is performed using the methods described in ref. [ 28 ]. Two sources of systematic uncertainty are considered, and the total uncertainty is given as the sum in quadrature of the two components: • Monte Carlo non-closure: PYTHIA8 with Monash tune is the default model used to generate the spherocity response matrix and SpT=1 O distributions with and without the detector efficiency losses. The generated SpT=1 O distributions are defined as previously described in section 3. The unfolded SpT=1 O spectrum from the simulation is compared to the generated one. Thus, any statistically significant difference between the generated and unfolded distributions is referred to as MC non-closure and is added in quadrature to the total systematic uncertainty. This uncertainty is within 2%. For ϕ ,K ∗0 ,K 0 S ,Λ and Ξ, the systematic uncertainty comes for variations in how the decay products enters the SpT=1 O calculation, particle-by-particle, and is driven by the effect that the same particle can decay in different modes (e.g., shorter or longer lifetime for the weakly – 14 – JHEP05(2024)184 decaying particles). For this reason, it is expected that, even if the relative abundances of particle species in the MC are not the same as in data, the systematic effect will be similar. • Dependence on the choice of the MC model: EPOS-LHC is used as the alternative model to generate a different spherocity response matrix. This response matrix is used to unfold the SpT=1 O distributions. The ratio between the final unfolded distributions using PYTHIA8 and EPOS-LHC was quantified and added to the total systematic uncertainty. This uncertainty has a spherocity dependence. While in the interval SpT=1 O> 0 . 8this uncertainty is about 3%, for the interval SpT=1 O< 0 . 6the uncertainty is of about 5.8%. Similar to prior sections, the systematic uncertainties for analyses depending on primary charged particles are discussed in section 6.1. Likewise, the systematic uncertainties for longer-lived particles are discussed in section 6.2. We note that in all cases, checks in the spirit of ref. [ 37 ] were performed, comparing measurements of default and alternate criteria in quadrature. This is done in order to remove possible statistical effects, where the default and alternate selections are statistically correlated, from systematic uncertainties. 6.1 Systematic uncertainties for analyses utilizing primary charged particles The total systematic uncertainty on the pT spectra of π, K , pis divided into two categories. The first class includes the uncertainties that are common among the different PID techniques: the track selection criteria, the ITS-TPC and TPC-TOF matching efficiencies. These uncertainties are species and pT dependent. The second class includes systematic uncertainties that are technique dependent: signal extraction method and the estimation of the secondary particle correction. This study utilizes the same methods used in previous ALICE analyses [ 11 , 28 – 30 ]. Most of the systematic uncertainties cancel in the pT -differential particle ratios (K /π and p /π ) except the ones attributed to the signal extraction and feed-down correction. Moreover, at high transverse momentum (rTPC analysis) the procedure described in [ 30 ] is used to extract the signal extraction systematic uncertainty on the K /π and p /π ratios directly from fits to the dE/dx distributions. Table 6shows a summary of the relative systematic uncertainties on the pT spectra of π ,K,p, and the particle ratios. The results are shown for the spherocity classes: Jet-like and Isotropic [0–1]%, and the SpT=1 O unbiased case. The multiplicity class corresponds to N|η|<0.8 tracklets I−III . Similar results are obtained for the other multiplicity classes. The main contributions to the systematic uncertainty on the spectra of the resonance particles are listed in table 7. The uncertainties are evaluated in groups, where each group contains sources of systematic uncertainties that cannot be evaluated individually. The systematic uncertainties are pT -dependent, and the ranges listed in the table represent the minimum and maximum values. The maximum uncertainties for ϕ and K ∗0 are obtained at low pT ( pT< 1 . 5 GeV/c ), with the uncertainties reaching local minima at intermediate pT (1 . 5 < pT< 4 . 0 GeV/c ), and then approaching towards the maximum value at high pT ( pT> 4 . 0 GeV/c ). Both the correlated and uncorrelated sources defined in table 7are used to account for the uncertainty in the pT -differential particle spectra, but only the uncorrelated sources are considered for the SpT=1 O -dependent-toSpT=1 O -integrated ratios. – 15 – JHEP05(2024)184 Source πK p Jet-like Isotropic HM Jet-like Isotropic HM Jet-like Isotropic HM Common ITS–TPC matching efficiency 0.7–3 0.7–3 0.7–3 0.7–3 0.7–3 0.7–3 0.7–3 0.7–3 0.7–3 Track selection 0.13–1 0.13–1 0.13–1 0.13–2 0.13–2 0.13–2 0.13–2.7 0.13–2.7 0.13–3 TPC PID 0.2–1.8 0.2–1.9 0.2–1.8 2.8–6 2.8–6 2.8–6 1.6–9.8 1.6–10 1.6–9.9 Ncl 0.5–0.7 0.6–0.7 0.6–0.7 0.02–0.8 0.03–0.7 0–0.5 0.7–1.5 0.7–1.4 0.7–1.3 Feed-Down 0.3–0.9 0.3–0.9 0.3–0.9 — — — 1.1–10 1.1–10 1.1–10 TOF PID 0.03–2.9 0.03–1.8 0.02–2.1 0.3–8.7 0.3–4.2 0.2–4.8 0.08–4.8 0.08–6.9 0.09–5.4 Feed-Down 0–0.2 0–0.2 0–0.2 — — — 0.2–0.9 0.2–0.9 0.2–0.9 TPC–TOF matching efficiency 3 3 3 6 6 6 4 4 4 rTPC PID 0.65–2.8 0.68–2.9 0.67–2.8 2.7–10.7 2.5–10.7 2.5–10.3 4.6–15.0 4.8–14.9 4.7–14.1 Ncl 0.5–1.5 0.4–1.6 0.6–1.5 0.04–0.7 0.05–0.5 0.03–0.7 0.08–1.4 0.5–1.7 0.1–1.4 Feed-Down Negl. Negl. Negl. — — — 0–0.2 0–0.2 0–0.2 Total 1.2–4.4 1.2–4.5 1.5–5 3.1–10.9 3.1–10.9 3–11 3.1–15.7 3–15.3 3–15 Particle ratios K/π p/π Total — — — 0.3–11 0.3–11.4 0.4–11 0.8–15 0.7–14.6 1–14 Table 6. Summary of the relative systematic uncertainties on the pT spectra of π ,K,p, and the particle ratios. The uncertainties are reported as percentage values. The intervals represent the minimum and maximum values in the respective interval of identification. They are given for the spherocity classes: Jet-like and Isotropic [0–1]%, and the SpT=1 Ounbiased case. The multiplicity class corresponds to N|η|<0.8 tracklets I–III. – 16 – JHEP05(2024)184 Hadron: ϕK∗0 Topology: Jet-like HM Iso Jet-like HM Iso Uncorrelated sources Signal extraction 1–3 1–2 1.5–2.5 3–7 2–4 1–5 Track selection & PID 2–6 1–5 1–5 1–5 1–4 1–4 Background estimation 1–3 0–1 1–2 1–3 1–4 1–4 Correlated sources Tracking efficiency 2 2 Branching ratio 1 2 Hadronic interaction 2–3 0–2 Material budget 0–5 0–5 Total uncertainty 5–9 5–8 5–8 5–9 4–8 4–8 Table 7. The most relevant systematic uncertainties for the resonance analysis as a function of SpT=1 O . “HM” in this table represents the SpT=1 O -integrated spectra. The uncertainties are reported as percentage values. Uncertainties are pT-dependent, and ranges listed represent the minimum and maximum values presented in the final spectra (see text for details). The uncertainties related to signal extraction are estimated through variations of the fit range, constraints to the peak fit, and variations of the residual background function. “Track selection & PID” consists of varying the nσ requirements for valid resonance daughter candidates, as well as variations in the track quality criteria. This includes variations of the required crossed rows in the TPC, the initial vertex position along the beam axis, and the DCA along the beam axis (DCA z ). These variations are performed both in data and simulations, simultaneously. Finally, the background estimation includes changes in how the combinatorial background is evaluated (event-mixing, reflected mass hypothesis, like-charge pairs). The correlated sources apply equally across the different SpT=1 O selections, and cancel in the ratio. The hadronic interaction and material budget represent the uncertainties in interactions between particles and the ALICE detector, and the uncertainty in the hadronic interaction cross section of particles traversing material in ALICE. 6.2 Systematic uncertainties on analyses of long-lived particles The systematic uncertainties on long-lived weakly decaying particles, K0 S ,Λ( ¯ Λ ), and Ξ, are reported in table 8and have similar components for all particle species: • Selection criteria: estimated by carrying out the analysis using looser and tighter variations of the selections in tables 4and 5. • Track pile-up: assigned due to the requirement that at least one daughter (or bachelor track for Ξ) has a fast-detector signal (ITS or TOF), see section 4.2. This systematic uncertainty was found by varying the number of required tracks and fast signals, and reflects how well these conditions are modelled in the MC simulation. – 17 – JHEP05(2024)184 Topology: Jet-like Iso HM Jet-like/HM Iso/HM K0 S Selection cuts 3 3–4 3–4 Negl. 1 Track pile-up 1 1–3 1 0–2 0–2 Signal extraction 1–3 1–3 1–3 Negl. Negl. Efficiency 2 2 2 2 2 Material budget 4 4 4 — — Experimental bias 4 1 — 4 1 Total uncertainty 7 6–7 5–6 5 2–3 Λ(Λ) Selection cuts 1–5 2–6 4–5 0–1 0–3 Track pile-up 4–5 5 3–5 0–1.5 0–1 Signal extraction 2–6 2–6 2–6 0–2 0–1 Feed-down correction 1.0–1.5 1.0–1.5 1.0–1.5 Negl. Negl. Efficiency 2 2 2 2 2 Material budget 4 4 4 — — Experimental bias 4 1 — 4 1 Total uncertainty 8–10 8–9 7–9 5 3–4 Ξ± Selection cuts 0–1 0–1 0–1 Negl. Negl. Track pile-up 2–3 2–3 2–3 2 Negl. Signal extraction 0–1 0–1 0–1 1 Negl. Efficiency 2 2 2 2 2 Material budget 1–9 1–9 1–9 — — Experimental bias 3 3 — 3 3 Total uncertainty 5–10 5–10 4–10 4 3.5 Table 8. The most relevant systematic uncertainties for the long-lived particles K 0 S ,Λ( ¯ Λ ), and Ξ, as a function of SpT=1 O . “HM” in this table represents the SpT=1 O -unbiased spectra. The uncertainties are reported as percentage values. Uncertainties are pT -dependent, and ranges listed represent the minimum and maximum values presented in the final spectra (see text for details). • Signal extraction: estimated by varying the range in Minv for signal and background and the shape of the background. • Efficiency: accounts for possible variations of the tracking efficiency with multiplicity. The same uncertainty, 2%, as used in a previous detailed study of multiplicity-dependent strangeness production in √s= 13 TeV pp collisions [10] is assigned here. – 18 – JHEP05(2024)184 • Material budget: estimated by varying parameters in the Monte Carlo description of the ALICE apparatus, see ref. [24] for details. •Experimental bias: taken from table 1. See discussion in section 3for details. In addition to the above-mentioned uncertainties, there is also a contribution from evaluating the secondary yields for Λ( ¯ Λ ) particles. It was determined by varying Ξyields within their uncertainties, using an alternative method of constructing the feed-down matrix only from charged Ξbaryons, as well as a flat systematic uncertainty to account for the possible multiplicity dependence of the matrix (shown in table 8as “Feed-down correction”). Finally, we note that some of the systematic uncertainties cancel when we compare results in the same multiplicity class but with various SpT=1 O selections. This uncertainty is shown in table 8as “jet-like/HM” and “Iso/HM” and was estimated by doing the systematic variations for a jet-like selection and an unbiased (same multiplicity) selection in parallel and comparing the ratios of pT spectra with and without the variation. The studies were performed for various SpT=1 O and multiplicity selections. No strong dependence was found inside the relevant selections (e.g. for tighter or looser jet-like selections). However, one should note that such a dependence is hard to pin down with good precision due to the limited number of candidates for very tight selections. 7 Results and discussion The results presented in this section include the transverse momentum spectra, integrated yields, ⟨dN/dy⟩ , and mean transverse momentum, ⟨pT⟩ , as a function of SpT=1 O , as well as pT -differential ratios between particle species (mainly with respect to pions), and the pT -differential ratios-toπ relative to the SpT=1 O unbiased baseline (referred to as the “double ratio”). Finally, the strangeness enhancement as a function of SpT=1 O is investigated. In the following, the ⟨dN/dy⟩ and ⟨pT⟩ are calculated in the measured kinematic range, and corrected for the limited range by extrapolating the spectra to the unmeasured pT regions using Levy-Tsallis fits down to pT = 0. To account for the additional systematic uncertainties arising from this procedure, modified fit functions such as Boltzmann, mT - exponential, pT -exponential, Fermi-Dirac (only for fermions) and Bose-Einstein statistics (only for bosons), and Boltzmann-Gibbs blast-wave functions are used as variations. The systematic uncertainties arising due to extrapolations are evaluated from the RMS of the variations with respect to the Levy-Tsallis fits. The extrapolation uncertainty is added in quadrature to the systematic uncertainties obtained from the measured pT ranges to get the final systematic uncertainties for the ⟨dN/dy⟩ and ⟨pT⟩ . More details about this procedure can be found in ref. [ 10 ]. The experimental measurements will be compared with a broad selection of pp MC models, namely PYTHIA 8.2 [ 9 ] (both the default Monash tune and with the added rope hadronization framework), Herwig 7.2 [ 8 ], and EPOS-LHC [ 17 ]. PYTHIA 8.2 is a QCDinspired model that is built around the Lund-string model for hadronization [ 32 ]. The default Monash tune is unable to describe the strangeness enhancement in small systems, while the rope extension (layers of overlapping strings that increase the string tension) of the Lundstring model [ 13 ] has been introduced to accommodate this. For the PYTHIA 8.2 Ropes, – 19 – JHEP05(2024)184 note that this article only utilizes the “Flavour Ropes” for the model predictions, without the string-shoving mechanism [ 38 ] incorporated in the rope hadronization framework. Similarly, Herwig is a QCD-inspired pp generator centered around a cluster hadronization model [ 8 ], which has recently been extended to be able to describe the strangeness enhancement [ 11 ]. On the contrary, EPOS-LHC is a two-component core-corona model, which incorporates QGP features in the core to explain the strangeness enhancement. The effect of the SpT=1 O selection is discussed in section 7.1 for the two multiplicity estimators. It is shown that the spherocity selection for the events with the highest midrapidity multiplicity, 0–1% N|η|<0.8 tracklets , gives the best control of the event “hardness”, i.e. select events with a large ⟨pT⟩ . Results obtained using this estimator are therefore first presented in sections 7.2–7.4, followed by results obtained using other multiplicity estimators in section 7.5. 7.1 High-multiplicity estimators and SpT=1 O If one considers a high-multiplicity pp collision to be built up from independent subcollisions (a traditional MPI picture [ 39 ]), then there will be a trivial isotropization with increasing multiplicity. As previous ALICE measurements indicate a strong correlation between multiplicity and QGP-like effects, such as strangeness production [ 1 ], it is important for the study presented here to disentangle this possible trivial bias from the underlying physical properties of interest. To understand the impact of this on the SpT=1 O selection, the ⟨pT⟩ and the average pion yield ⟨ d Nπ/ d y⟩ , with different multiplicity and spherocity selection criteria are shown in figure 2. Results are shown for both the forward (V0M) and midrapidity ( N|η|<0.8 tracklets ) multiplicity estimators (described in section 2.1). A clear distinction is observed with respect to how the different multiplicity estimators relate to the SpT=1 O selection. This effect is solely driven by the rapidity region where the multiplicity is estimated, and not by properties of the ALICE apparatus. It is observed that the V0M multiplicity selection maintains a similar ⟨pT⟩ , but contains large variations in ⟨dNπ/dy⟩ for the different SpT=1 O selections. In contrast, the N|η|<0.8 tracklets selected events are characterized by large differences in ⟨pT⟩ among the event classes, selecting events according to their hardness. The implicit multiplicity dependence of SpT=1 O is minimized by sharply constraining the multiplicity using a midrapidity multiplicity estimation. This implies that the N|η|<0.8 tracklets multiplicity estimator, in tandem with a SpT=1 O selection, is best at separating events based on their hardness. Moreover, PYTHIA 8.2 model studies of the correlation between the average transverse momentum transfer of the hardest parton-parton interaction, ⟨ˆpT⟩ , and the average number of multi-parton interactions ⟨nMPI⟩ is presented in figure 3for multiplicity and SpT=1 O intervals of 0–1%. For each estimator, the multiplicity is calculated as the number of primary charged particles in the pseudorapidity interval(s) covered by that estimator. Figure 3shows that the hardest scattering for the events in the jet-like SpT=1 O 0–1% event category is significantly harder than for the SpT=1 O -integrated, high-multiplicity reference. This is observed both for the default PYTHIA 8.2 Monash, and with the rope hadronization framework enabled. This feature is present when the multiplicity is estimated both in the N|η|<0.8 tracklets and V0M pseudorapidity regions, but the effect is particularly strong when the multiplicity is estimated at midrapidity. Furthermore, the isotropic SpT=1 O 99–100% events are slightly softer than – 20 – JHEP05(2024)184 5 10 15 20 25 30 35 40 〉 y/d π N d〈 0.6 0.7 0.8 0.9 )c (GeV/〉 T p 〈 V0M I Jet-like [0-1]% Jet-like [0-10]% Isotropic [0-10]% Isotropic [0-1]% I |<0.8η| tracklets N Jet-like [0-1]% Jet-like [0-10]% Isotropic [0-10]% Isotropic [0-1]% III− I |<0.8η| tracklets N Jet-like [0-1]% Jet-like [0-10]% Isotropic [0-10]% Isotropic [0-1]% - π+ + π | < 0.8 η , |c 0.15 GeV/≥ T p 10, ≥ ch N = 13 TeVs , pp, ALICE Figure 2. Correlation between ⟨pT⟩ and ⟨ d Nπ/ d y⟩ as a function of SpT=1 O , in the 0–10% and 0–1% V0M and N|η|<0.8 tracklets multiplicity classes. The total systematic uncertainties are represented by empty boxes. The statistical uncertainty is smaller than the reported marker sizes. overall high-multiplicity events. In conjunction with the softer ⟨pT⟩ presented in figure 2, these findings suggest that the isotropic topologies are formed by multiple softer interactions, while the jet-like topologies have at least one hard scattering that is significantly harder than for the SpT=1 O -integrated selection. In the following, a complete set of results will be presented using N|η|<0.8 tracklets 0–1% to highlight the impact on the QCD dynamics of the extreme event topologies, while minimizing the effects of any trivial multiplicity (system size) dependence. The results are first presented for jet-like and isotropic events, utilizing a 0–10% and 90–100% SpT=1 O selection, respectively, to have a complete set of particle spectra. Furthermore, selected results are also presented for the most extreme 1% percentiles of SpT=1 O . 7.2 Results of SpT=1 O-differential pTspectra at N|η|<0.8 tracklets 0–1% The pT spectra for jet-like and isotropic events are presented in figures 4and 5for N|η|<0.8 tracklets 0–1% together with the SpT=1 O unbiased reference. The trends of the spectral shapes are consistent between all observed particle species, showcasing a significant hardening (softening) of the pT in the low (high) SpT=1 O selection, relative to the inclusive high-multiplicity event class, respectively. These trends are also well reflected in the model predictions. The PYTHIA 8.2 default Monash tune can describe the qualitative trends of the SpT=1 O selection. However, – 21 – JHEP05(2024)184 10 12 14 16 18 20 22 24 〉 nMPI 〈 15 20 25 30 35 )c (GeV/〉 T p 〈 | < 0.8 η , |c 0.15 GeV/≥ T p 10, ≥ ch N = 13 TeVs Simulation, pp, ALICE V0M I I |<0.8η| tracklets N 1%− 0 =1 T p 0 S 100%− 0 =1 T p 0 S 100%− 99 =1 T p 0 S 1%− 0 =1 T p 0 S 100%− 0 =1 T p 0 S 100%− 99 =1 T p 0 S I |<0.8η| tracklets NV0M I PYTHIA 8.2 Monash PYTHIA 8.2 Ropes 1%− 0 =1 T p 0 S 100%− 0 =1 T p 0 S 100%− 99 =1 T p 0 S 1%− 0 =1 T p 0 S 100%− 0 =1 T p 0 S 100%− 99 =1 T p 0 S Figure 3. PYTHIA 8.2 correlation study between ⟨ˆpT⟩ and ⟨nMPI⟩ as a function of SpT=1 O , in 0–1% V0M and N|η|<0.8 tracklets multiplicity classes. The default PYTHIA 8.2 Monash variation is compared to PYTHIA 8.2 with color rope hadronization. The total systematic and statistical uncertainties are smaller than the marker sizes. The grey band is an interpolation between the points, to more clearly illustrate the trend of each multiplicity and model variation. a large quantitative deviation from data is observed in the pT differential production of light-flavor hadrons, in particular the non-strange hadrons. The PYTHIA 8.2 rope tune is able to describe the measured data for strange hadrons very well, but overestimates the total amount of produced non-strange hadrons. Similar observations can also be seen when contrasting Herwig 7.2 with EPOS-LHC, where EPOS-LHC overestimates the total yields, but is able to describe the SpT=1 O -differential interplay for the mesons quite well. These large deviations are well known from previous studies [ 18 ]. The SpT=1 O -differential average pT ( ⟨pT⟩ ) and yield ⟨dN/dy⟩ are reported in figure 6as a function of the extracted particle masses. The measured ⟨pT⟩ values confirms what is qualitatively observed in the pT -differential spectra: there is a significant pT -hardening in jet-like events, and this trend is consistent across all measured light-flavor particle species. Furthermore, the ⟨pT⟩ of the integrated SpT=1 O high-multiplicity events are consistent with the ⟨pT⟩ of the isotropic sample. This observation indicates that average high-multiplicity events and SpT=1 O selected isotropic events are dominated by similar underlying physics processes. This shows that the SpT=1 O -integrated event class is not the arithmetic average of the jet-like and isotropic subsamples, indicating that jet-like events are rare outliers of a much more – 22 – JHEP05(2024)184 2 4 6 8 10 0.5 1 1.5 2 2.5 3− 10 2− 10 1− 10 1 ALICE p p+ 2 4 6 8 10 0.5 1 1.5 2 2.5 3 2 1 1 - K + K→ φ Integrated =1 T p 0 S 10%−: 0 =1 T p 0 S 100%−: 90 =1 T p 0 S PYTHIA 8.2 Monash PYTHIA 8.2 Ropes 2 4 6 8 10 0.5 1 1.5 2 2.5 3 2 1 1 Λ+Λ 2 4 6 8 10 )c (GeV/ T p 0.5 1 1.5 2 2.5 3 2 1 1 Ξ+Ξ = 13 TeVs pp: > 10 ch N (I), |<0.8η| tracklets N -1 )c) (GeV/ T pdy/(dN 2 (1/N) d-Integrated =1 T p O Ratio to S 2 4 6 8 10 1 1.5 2 2.5 3− 10 2− 10 1− 10 1 10 π+π 2 4 6 8 10 1 1.5 2 2.5 3 2 1 1 10 0 s K 2 4 6 8 10 1 1.5 2 2.5 3 2 1 1 10 KK+ 2 4 6 8 10 )c (GeV/ T p 1 1.5 2 2.5 3 2 1 1 10 π K→ *0 K -1 )c) (GeV/ T pdy/(dN 2 (1/N) d-Integrated =1 T p O Ratio to S Figure 4. Transverse momentum distribution of π, K , p,K ∗0 , ϕ , K0 S ,Λand Ξfor SpT=1 O classes selected for events at high-multiplicity, determined by events in the top 1% of N|η|<0.8 tracklets . The lower panels present the ratio between the SpT=1 O -integrated and SpT=1 O -differential events. Statistical and total systematic uncertainties are shown by error bars and boxes, respectively. The curves represent PYTHIA 8.2 model predictions of the same measurement. The average statistical uncertainties from the predictions across all particle species range in the order of 1–15% from low-to-high pT. – 23 – JHEP05(2024)184 2 4 6 8 10 0.4 0.6 0.8 1 1.2 1.4 0.1 0.2 0.3 ALICE p p+ 2 4 6 8 10 0.4 0.6 0.8 1 1.2 1.4 0.1 0.2 0.3 Λ+Λ Integrated =1 T p 0 S 1%−: 0 =1 T p 0 S 100%−: 99 =1 T p 0 S PYTHIA 8.2 Monash PYTHIA 8.2 Ropes 2 4 6 8 10 )c (GeV/ T p 0.4 0.6 0.8 1 1.2 1.4 0.1 0.2 0.3 Ξ+Ξ (4x) = 13 TeVs pp > 10 ch N (I), |<0.8η| tracklets N ) + π + - πRatio of yields to (-Integrated =1 T p O Ratio to S 2 4 6 8 10 0.6 0.8 1 1.2 0.2 0.4 0.6 - +K + K 2 4 6 8 10 )c (GeV/ T p 0.6 0.8 1 1.2 0.2 0.4 0.6 s 0 K ) + π + - πRatio of yields to (-Integrated =1 T p O Ratio to S Figure 9. Top panels show hadron-toπ ratios for 0–1% SpT=1 O classes selected for the 0–1% N|η|<0.8 tracklets multiplicity events. Bottom panels present the hadron-toπ double ratios of SpT=1 O classes relative to SpT=1 O integrated high-multiplicity events. Statistical and systematic uncertainties are shown by bars and boxes, respectively. Experimental results are compared with predictions from PYTHIA 8.2 Monash and Ropes. – 30 – JHEP05(2024)184 2 4 6 8 10 0.4 0.6 0.8 1 1.2 1.4 0.2 0.4 0.6 ALICE p p+ 2 4 6 8 10 0.4 0.6 0.8 1 1.2 1.4 0.2 0.4 0.6 Λ+Λ = 13 TeVs pp > 10 ch N (I), |<0.8η| tracklets N 2 4 6 8 10 )c (GeV/ T p 0.4 0.6 0.8 1 1.2 1.4 0.2 0.4 0.6 Integrated =1 T p 0 S 1%−: 0 =1 T p 0 S 100%−: 99 =1 T p 0 S Herwig 7.2 EPOS-LHC Ξ+Ξ (4x) ) + π + - πRatio of yields to (-Integrated =1 T p O Ratio to S 2 4 6 8 10 0.6 0.8 1 1.2 0.2 0.4 0.6 - +K + K 2 4 6 8 10 )c (GeV/ T p 0.6 0.8 1 1.2 0.2 0.4 0.6 s 0 K ) + π + - πRatio of yields to (-Integrated =1 T p O Ratio to S Figure 10. Top panels show hadron-toπ ratios for 0–1% SpT=1 O classes selected for the 0–1% N|η|<0.8 tracklets multiplicity events. Figure 9and figure 10 both contain the same experimental data, but the vertical ranges are modified to accommodate the model predictions. Bottom panels present the hadron-toπ double ratios of SpT=1 O classes relative to SpT=1 O integrated high-multiplicity events. Statistical and systematic uncertainties are shown by bars and boxes, respectively. Experimental results are compared with predictions from Herwig 7.2 and EPOS-LHC. The large fluctuations present in the Herwig 7.2 predictions are due to statistical limitations. – 31 – JHEP05(2024)184 amount of high-multiplicity events that reflect the same rates of strangeness production found in low-multiplicity events. ALICE has previously published studies of differential Λ/K 0 S production in jets relative to the underlying event (UE), where it was found that the ratio in the jet was far below that of the ratio in the UE [ 40 ]. This is qualitatively similar to what we observe for the most extreme jet-like events in this study. One could therefore understand the results obtained here as a generalization to jet-dominated events. The p /π peak present for the 99–100% most isotropic events, as well as in the average 0–1% high-multiplicity events, is significantly suppressed in 0–1% jet-like event sample. In conjunction with strangeness suppression, this hints towards a decrease of QGP-like effects in events with extremely jetlike topologies. Furthermore, the discrepancy between the p /π and K /π ratios at highpT is interesting to note, where the isotropic/jet-like ratios meet for the p /π while diverging for the K /π . The underlying mechanism of this effect is currently not fully understood. In figure 9, the two PYTHIA 8.2 predictions are qualitatively able to describe some of the particle-toπ ratios for the 1% SpT=1 O percentiles, but remarkably underestimate the pT -differential production of Ξbaryons, as well as the isotropic production of both charged and neutral kaons. However, PYTHIA 8.2 is still able to qualitatively describe the interplay between high-multiplicity events, isotropic and jet-like topologies in the DR. Similar to what was observed in figure 8for the broader SpT=1 O selection, EPOS-LHC and Herwig 7.2 are unable to accurately capture the interplay of the DR towards larger pT . EPOS-LHC and Herwig 7.2 are both able to qualitatively describe most of the observed trends for the charged and neutral kaons in figure 10, with a slight underestimation of the absolute production rates. However, both model predictions are unable to describe the observed trends for the presented baryons. In figure 11, baryon-to-meson ratios are presented for p/ π ,Λ/K 0 S and Ξ/ ϕ . These ratios are known to be interesting observables, able to highlight features of radial flow and possible recombination [ 29 ]. While the Ξ/ ϕ ratio is not usually associated to measurements of radial flow, phenomenological models have different views of the effective net-strangeness of the ϕ meson. In Lund-string-like models, such as PYTHIA 8.2, the ϕ meson is produced from the fragmentation of s¯s pairs, making it effectively double strange. On the other hand, statistical thermal models typically treats the ϕ meson as having no strangeness, where the production instead is driven by the hadron mass. The overall trends among the three ratios are qualitatively similar, even as the strangeness content increases from p ( |S| = 0) to Ξ( |S| = 2). Furthermore, one can apply a traditional hypothesis of radial flow in larger collision systems, i.e., that a radial expansion of the system boosts heavier baryons out to high pT , resulting in a depletion of baryons at lowpT . In this context, figure 11 highlights an abundance (suppression) of isotropic (jet-like) protons at intermediate pT in the p/π ratio, without the depletion (enhancement) of isotropic (jet-like) protons at low pT . The origin for the difference in the lowpT behavior is still unclear, but we suspect that there is an interplay between soft radial flow and the hard suppression in the ratios to pions, observed in the jet-like events through figures 7–10. One should keep in mind that the relative systematic uncertainties are smaller than the total systematic uncertainties reported in figure 11, cf., section 6for further discussion. Similar trends are observed in the Λ/K 0 S ratio, although systematic uncertainties in the lower panel does not allow for a clear conclusion on the interplay between jet-like and isotropic – 32 – JHEP05(2024)184 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 πp/ 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 S 0 /KΛ × 3 1 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 ) - K + K→φ/(Ξ × 4 1 0.3 1 2 3 4 5 10 20 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 100%−: 90 =1 T p 0 S 10%−: 0 =1 T p 0 S Monash Ropes 0.3 1 2 3 4 5 10 20 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 = 13 TeVspp, (I) | < 0.8η| tracklets N | < 0.8η 0.15, |≥ T p 10, ≥ ch N 0.3 1 2 3 4 5 10 20 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 ALICE )c (GeV/ T p -int. O =1 T p Ratio to S Particle ratio Figure 11. p/ π ,Λ/ K and Ξ/ ϕ ratios for different SpT=1 O classes are obtained for 0 – 1% events measured by N|η|<0.8 tracklets . Lower panels show the ratio to SpT=1 O -integrated event selection. Statistical and total systematic uncertainties are shown by bars and boxes, respectively. The curves represent different model predictions of the same measurement. Note that for the p/ π , the same data points are presented in 7, with an extended pTrange now covering 10–20 GeV/c. events. The Ξ/ ϕ ratio suggests that there is a constant enhancement of Ξbaryons relative to produced ϕ mesons, within systematic uncertainties. The rope hadronization framework in PYTHIA 8.2 predicts both the single and double ratios reasonably well. It is remarkable that even though PYTHIA 8.2 Monash fails to predict the single ratios, both PYTHIA 8.2 Monash and Rope predictions show no significant deviations for the double ratios. The K 0 S /K ratios for the different multiplicity estimators are presented in figure 12. The ratios highlight a consistency with unity, and showcase no significant SpT=1 O dependence. These results verify that the modified SpT=1 O estimator is robust through a data-driven approach, complementing the studies discussed in section 3. 7.4 Integrated yields as a function of SpT=1 O The integrated double ratio over the full pT range is presented in figure 13, for p, Λ, and Ξ. The fully integrated yields are obtained by extrapolating the measured pT spectra for each particle species. Therefore, the systematic uncertainties shown in figure 13 also account for the added uncertainty due to the extrapolation procedure. This added uncertainty is particle species dependent given the different measured pT ranges for each particle species, particularly affecting the Λyields, which, as shown in table 1, can only be measured down to 1.0 GeV/c after SpT=1 O selection. The results demonstrate that the strange-hadron yield increases as a function of SpT=1 O , with indications of an ordering with strangeness content. In previous ALICE publications – 33 – JHEP05(2024)184 0.5 1 2 3 4 5 6 7 8 910 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 = 13 TeVs, pp, ALICE I | < 0.8η| tracklets N| < 0.8η 0.15, |≥ T p 10, ≥ ch N, -integrated =1 T p 0 S 100%−: 99 =1 T p 0 S 1%−: 0 =1 T p 0 S PYTHIA 8.2 Monash PYTHIA 8.2 Ropes )c (GeV/ T p / 0 S 2x K ± K 0.5 1 2 3 4 5 6 7 8 910 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 = 13 TeVs, pp, ALICE V0M I | < 0.8η 0.15, |≥ T p 10, ≥ ch N, -integrated =1 T p 0 S 100%−: 99 =1 T p 0 S 1%−: 0 =1 T p 0 S PYTHIA 8.2 Monash PYTHIA 8.2 Ropes )c (GeV/ T p / 0 S 2x K ± K Figure 12. The neutral-to-charged K 0 S /K ratios as a function of different multiplicity estimators and SpT=1 O . Statistical and total systematic uncertainties are shown by bars and boxes, respectively. The curves represent PYTHIA 8.2 model predictions of the same measurement. it was observed that in pp collisions at √s = 13 TeV the charge particle density, d Nch/ d η , is a driving quantity for the enhancement of strange hadrons [ 1 ]. For the results presented in figure 13, the d Nch/ d η at midrapidity is restricted (see figure 2), which allows one to directly test if SpT=1 O is sensitive to strangeness enhancement. We find that the strangeness production is suppressed in events with jet-like topologies, and slightly enhanced in softer, isotropic event topologies. In order to make the most precise comparison between the measured data and model predictions, the integrated double ratios are presented as a function of SpT=1 O for the measured pT ranges in figure 14 and figure 15, for p, Λand Ξ. The two figures contain the same data points, but use different ordinate ranges in the ratio to accommodate the MC-generator predictions. The yields for the measured pT ranges are estimated by counting the bin entries in each pT spectra, without the use of the Levy-Tsallis extrapolation. Therefore, the systematic uncertainties for the integrated yields utilizing the measured pT ranges are significantly reduced compared to the extrapolated yields presented in figure 13. Moreover, while the fraction of yields gained from the extrapolation is species dependent, it was tested that the relative increase of yields is consistent across all spherocity classes and the high-multiplicity reference. As the integrated yields presented in figures 13–15 are self-normalized, the relative yields obtained from the extrapolation therefore largely cancels. As such, one obtains the same physics conclusions by utilizing either extrapolated or measured pT ranges, which is reflected in the comparison between figure 13 and figure 14. Figures 14 and 15 highlight that the relative decrease of Ξproduction in the most jet-like events is of order 20%. We estimate, based on ref. [ 1 ], that to obtain a similar effect driven solely by multiplicity, one would have to decrease the multiplicity by approximately 60 to 70%. Given that the difference in multiplicity between the spherocity event classes is roughly 10%, this indicates a substantial lifting of the strangeness suppression due to the event topology selection. – 34 – JHEP05(2024)184 0 0.2 0.4 0.6 0.8 1 =1 T p O S 0.7 0.8 0.9 1 1.1 Ratio to pions / (HM ratio) = 13 TeV, s 10≥ ch N| < 0.8, η (I), | | < 0.8η| tracklets N ALICE π N / p N π N / Λ N π N / Ξ N Figure 13. The double ratios of integrated yields as a function of SpT=1 O for the spectra of top-1% N|η|<0.8 tracklets . The yield is estimated by extrapolating the spectra over the full pT range. Statistical and systematic uncertainties are shown by bars and boxes, respectively. The grey band around unity represents the systematic uncertainty of the pion measurement. This novel feature can help to further elucidate the underlying mechanism(s) that drives the strangeness enhancement. Remarkably, these findings suggest that charged particle production is not driven by a single source, but instead driven by several sources with varying strangeness-toπ production rates, with the jet-like events showcasing a level of strangeness production usually found at lower multiplicities. In combination with the pT -differential ratios from figures 7–10, as well as the baryon-to-meson ratios in figure 11, one can characterize jet-like events as exhibiting a large decrease of relative production at intermediate pT , with an overall high degree of strangeness suppression in the total yields. Isotropic events can be characterized completely opposite to jet-like events, containing a boost of particles at intermediate pT , with enhanced strangeness production in the pT -integrated yields. These findings suggest that one is able to control the degree of QGP-like effects in small systems – 35 – JHEP05(2024)184 0 0.2 0.4 0.6 0.8 1 =1 T p O S 0.7 0.8 0.9 1 1.1 Ratio to pions / (HM ratio) PYTHIA 8.2 Monash PYTHIA 8.2 Ropes = 13 TeV, s 10≥ ch N| < 0.8, η (I), | | < 0.8η| tracklets N ALICE c < 20 GeV/ T p: 0.3 < π N c < 20 GeV/ T p: 0.45 < p N c < 8 GeV/ T p: 1.0 < Λ N c < 6.5 GeV/ T p: 0.6 < Ξ N π N / p N π N / Λ N π N / Ξ N Figure 14. The double ratios of integrated yield as a function of SpT=1 O are represented in the top-1% of N|η|<0.8 tracklets . The yields are integrated in the measured pT ranges for each particle species. Statistical and systematic uncertainties are shown by bars and boxes, respectively. The curves represent different model predictions of the same measurement. The grey band around unity represents the systematic uncertainty of the pion measurement. by categorizing events based on the azimuthal topology. Furthermore, it demonstrates that SpT=1 O -integrated high-multiplicity events are dominated by soft processes, and provides an important input to understanding the ALICE observation of universal scaling of strangeness enhancement with multiplicity [ 1 ]. The PYTHIA 8.2 Rope hadronization framework and EPOS-LHC models, which incorporate two-component phenomenologies, are able to predict the qualitative trend of enhancement and suppression of strange particle production as a function of SpT=1 O , albeit with a different mass-ordering for Λand Ξ. In contrast, both the PYTHIA8 Monash and Herwig 7.2 predictions are unable to describe the reported experimental observation. Surprisingly, Herwig 7.2 predicts the opposite trend; enhancement of all three baryons in jet-like events and a – 36 – JHEP05(2024)184 0 0.2 0.4 0.6 0.8 1 =1 T p O S 0.8 1 1.2 Ratio to pions / (HM ratio) EPOS-LHC Herwig 7.2 = 13 TeV, s 10≥ ch N| < 0.8, η (I), | | < 0.8η| tracklets N ALICE c < 20 GeV/ T p: 0.3 < π N c < 20 GeV/ T p: 0.45 < p N c < 8 GeV/ T p: 1.0 < Λ N c < 6.5 GeV/ T p: 0.6 < Ξ N π N / p N π N / Λ N π N / Ξ N Figure 15. The double ratios of integrated yield as a function of SpT=1 O are represented in the top-1% of N|η|<0.8 tracklets . The yields are integrated in measured pT ranges for each particle species. Statistical and systematic uncertainties are shown by bars and boxes, respectively. Figure 14 and figure 15 both contain the same experimental data, but the vertical ranges are modified to accommodate the model predictions. The curves represent different model predictions of the same measurement. The grey band around unity represents the systematic uncertainty of the pion measurement. suppression in isotropic events. If this is a generic feature of the new strangeness-enhancement process introduced in Herwig 7.2[ 8 ], then the results presented in this article appear to rule out this mechanism. Furthermore, it might seem counterintuitive that there can be large differences between model predictions in figure 14 and figure 15, while those same models had similar trends for the pT -differential double-ratios presented in figures 7–10. However, it is important to note that the integrated double-ratios are weighted by the relative yields in each pT interval. Therefore, the ⟨pT⟩ of each particle species play a major part in the integrated particle yields, see figure 6. – 37 – JHEP05(2024)184 The comparison between model and data suggests that models without a universal hadronization scheme, either through the core-corona in EPOS-LHC, or through the color ropes in PYTHIA 8.2, are able to qualitatively reproduce the observed trends. In contrast, the default PYTHIA 8.2 Monash variation, based on the concept of jet universality, is unable to capture the feature presented in the data. 7.5 SpT=1 Oresults with a broadened multiplicity range In section 7.3, it was shown that the 0–1% topology selection produced the largest effects, seen in figure 9and figure 10. However, the resonance particles had to be excluded in those measurements due to statistical limitations related to the signal extraction. Therefore, in this section, we report on SpT=1 O measurements with a broader multiplicity selection for the different SpT=1 O classes. We implement the broadening of the multiplicity estimation in two different ways: 1. First, we broaden the midrapidity multiplicity estimation to 0–10%, while simultaneously constricting the SpT=1 O event selection to the top 1% quantile. This allows for a broader multiplicity range, while retaining the extreme topology selection, gaining a factor 10 in the number of events. 2. Secondly, we investigate top-1% multiplicity at forward rapidity in 0–10% SpT=1 O quantiles. This is to study the impact of a broader ⟨ d Nπ/ d y⟩ , and to compare midrapidity to forward rapidity multiplicity estimation between roughly similar d Nch/ d η , as is seen in figure 2. Figure 16 illustrates the SpT=1 O -differential ratios to ( π+ + π− )with multiplicity measured at midrapidity, with a simultaneous broadened multiplicity ( N|η|<0.8 tracklets 0–10%) and tightened SpT=1 O selection criteria, compared to the measurement presented in section 7.3. The experimental data is compared with both PYTHIA 8.2 Ropes and EPOS-LHC, which, as shown in previous sections of this article, give the most accurate predictions of the observed trends. The double ratios for K ∗0 suggest that the production in isotropic topologies is similar to that of average high-multiplicity events. In contrast, there is a pronounced structure of the ratio for jet-like topologies, highlighting an overall suppression of K ∗0 production. The ϕ has similar features: suppression in jet-like events (qualitatively the same trend as for Ξ), and consistent with unity for isotropic events. For all presented particle species, an overall narrower SpT=1 O selection highlights a large suppression of the pT -differential yield of strange hadrons relative to pions in events with extreme jet-like topologies. Furthermore, there is a larger deviation among the four different models compared to the more constrained multiplicity quantile, in particular in jet-like topologies for protons, Ξ, and K ∗0 . However, the overall trends are well predicted. Both resonance particles favor production in softer events containing QGP-like features, where the decrease of K ∗0 production in jet-like events could potentially be due to a rescattering effect. While the origin of the suppression of ϕ production in jet-like events is not fully understood, the behavior is consistent with the strange particles measured in this study. The particle ratios to ( π+ + π− )for events with forward rapidity multiplicity estimations are presented in 0–10% SpT=1 O event classes in figure 17. The reported effects of SpT=1 O – 38 – JHEP05(2024)184 2 4 6 8 10 0.6 0.8 1 1.2 1.4 0.2 0.4 0.6 ALICE p p+ 2 4 6 8 10 0.6 0.8 1 1.2 1.4 0.2 0.4 0.6 - K + K→ φ (4x) 2 4 6 8 10 0.6 0.8 1 1.2 1.4 0.2 0.4 0.6 Integrated =1 T p 0 S 1%−: 0 =1 T p 0 S 100%−: 99 =1 T p 0 S EPOS-LHC PYTHIA 8.2 Ropes Λ+Λ 2 4 6 8 10 0.6 0.8 1 1.2 1.4 0.2 0.4 0.6 Ξ+Ξ (4x) = 13 TeVs pp > 10 ch N (III), | < 0.8η| tracklets N )c (GeV/ T p ) + π + - πRatio of yields to (-Integrated =1 T p O Ratio to S 2 4 6 8 10 0.6 0.8 1 1.2 0.2 0.4 0.6 - +K + K 2 4 6 8 10 0.6 0.8 1 1.2 0.2 0.4 0.6 s 0 K 2 4 6 8 10 0.6 0.8 1 1.2 0.2 0.4 0.6 π K→ *0 K )c (GeV/ T p ) + π + - πRatio of yields to (-Integrated =1 T p O Ratio to S Figure 16. Top panels show hadron-toπ ratios for 0–1% SpT=1 O classes selected for the 0–10% N|η|<0.8 tracklets multiplicity events. Bottom panels present the hadron-toπ double-ratios of SpT=1 O classes relative to SpT=1 O integrated high-multiplicity events. Statistical and systematic uncertainties are shown by bars and boxes, respectively. Experimental results are compared with predictions from EPOS-LHC and PYTHIA 8.2 Rope hadronization framework. – 39 – JHEP05(2024)184 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: Czech Science Foundation (grant no. 23-07499S), Czech Republic; European Research Council, Strong 2020 - Horizon 2020 (grant nos. 950692, 824093), European Union; ICSC — Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, European Union — NextGenerationEU; Academy of Finland (Center of Excellence in Quark Matter) (grant nos. 346327, 346328), Finland. Open Access. 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Di Bari 32, – 49 – JHEP05(2024)184 A. Di Mauro 33, B. Diab 131, R.A. Diaz 143,7, T. Dietel 115, Y. Ding 6, J. Ditzel 65, R. Divià 33, D.U. Dixit 19, Ø. Djuvsland21, U. Dmitrieva 142, A. Dobrin 64, B. Dönigus 65, J.M. Dubinski 137, A. Dubla 98, S. Dudi 91, P. Dupieux 128, M. Durkac107, N. Dzalaiova13, T.M. Eder 127, R.J. Ehlers 75, F. Eisenhut 65, R. Ejima93, D. Elia 51, B. Erazmus 104, F. Ercolessi 26, B. Espagnon 132, G. Eulisse 33, D. Evans 101, S. Evdokimov 142, L. Fabbietti 96 , M. Faggin 28 , J. Faivre 74 , F. Fan 6 , W. Fan 75 , A. Fantoni 50 , M. Fasel 88 , A. Feliciello 57, G. Feofilov 142, A. Fernández Téllez 45, L. Ferrandi 111, M.B. Ferrer 33, A. Ferrero 131, C. Ferrero IV,57, A. Ferretti 25, V.J.G. Feuillard 95, V. Filova 36, D. Finogeev 142, F.M. Fionda 53, E. Flatland33, F. Flor 117, A.N. Flores 109, S. Foertsch 69, I. Fokin 95, S. Fokin 142, E. Fragiacomo 58, E. Frajna 47, U. Fuchs 33, N. Funicello 29, C. Furget 74 , A. Furs 142 , T. Fusayasu 99 , J.J. Gaardhøje 84 , M. Gagliardi 25 , A.M. Gago 102 , T. Gahlaut48, C.D. Galvan 110, D.R. Gangadharan 117, P. Ganoti 79, C. Garabatos 98, T. García Chávez 45, E. Garcia-Solis 9, C. Gargiulo 33, P. Gasik 98, A. Gautam 119, M.B. Gay Ducati 67, M. Germain 104, A. Ghimouz126, C. Ghosh136, M. Giacalone 52, G. Gioachin 30, P. Giubellino 98,57, P. Giubilato 28, A.M.C. Glaenzer 131, P. Glässel 95, E. Glimos 123, D.J.Q. Goh77, V. Gonzalez 138, P. Gordeev 142, M. Gorgon 2, K. Goswami 49, S. Gotovac 34 , V. Grabski 68 , L.K. Graczykowski 137 , E. Grecka 87 , A. Grelli 60 , C. Grigoras 33 , V. Grigoriev 142, S. Grigoryan 143,1, F. Grosa 33, J.F. Grosse-Oetringhaus 33, R. Grosso 98, D. Grund 36, N.A. Grunwald95, G.G. Guardiano 112, R. Guernane 74, M. Guilbaud 104, K. Gulbrandsen 84, T. Gündem 65, T. Gunji 125, W. Guo 6, A. Gupta 92, R. Gupta 92, R. Gupta 49, K. Gwizdziel 137, L. Gyulai 47, C. Hadjidakis 132, F.U. Haider 92, S. Haidlova 36 , H. Hamagaki 77 , A. Hamdi 75 , Y. Han 140 , B.G. Hanley 138 , R. Hannigan 109 , J. Hansen 76, M.R. Haque 137, J.W. Harris 139, A. Harton 9, H. Hassan 118, D. Hatzifotiadou 52, P. Hauer 43, L.B. Havener 139, S.T. Heckel 96, E. Hellbär 98, H. Helstrup 35, M. Hemmer 65, T. Herman 36, G. Herrera Corral 8, F. Herrmann127, S. Herrmann 129, K.F. Hetland 35, B. Heybeck 65, H. Hillemanns 33, B. Hippolyte 130, F.W. Hoffmann 71, B. Hofman 60, G.H. Hong 140, M. Horst 96, A. Horzyk 2, Y. Hou 6, P. Hristov 33, C. Hughes 123, P. Huhn65, L.M. Huhta 118, T.J. Humanic 89, A. Hutson 117, D. Hutter 39, R. Ilkaev142, H. Ilyas 14, M. Inaba 126, G.M. Innocenti 33, M. Ippolitov 142, A. Isakov 85,87, T. Isidori 119, M.S. Islam 100, M. Ivanov13, M. Ivanov 98, V. Ivanov 142, K.E. Iversen 76, M. Jablonski 2, B. Jacak 75, N. Jacazio 26, P.M. Jacobs 75, S. Jadlovska107, J. Jadlovsky 107 , S. Jaelani 83 , C. Jahnke 111 , M.J. Jakubowska 137 , M.A. Janik 137 , T. Janson 71 , S. Ji 17, S. Jia 10, A.A.P. Jimenez 66, F. Jonas 88,127, D.M. Jones 120, J.M. Jowett 33,98, J. Jung 65, M. Jung 65, A. Junique 33, A. Jusko 101, J. Kaewjai106, P. Kalinak 61, A.S. Kalteyer 98, A. Kalweit 33, V. Kaplin 142, A. Karasu Uysal V,73, D. Karatovic 90, O. Karavichev 142, T. Karavicheva 142, P. Karczmarczyk 137, E. Karpechev 142, M.J. Karwowska 33,137, U. Kebschull 71, R. Keidel 141, D.L.D. Keijdener60, M. Keil 33, B. Ketzer 43 , S.S. Khade 49 , A.M. Khan 121 , S. Khan 16 , A. Khanzadeev 142 , Y. Kharlov 142 , A. Khatun 119, A. Khuntia 36, B. Kileng 35, B. Kim 105, C. Kim 17, D.J. Kim 118, E.J. Kim 70, J. Kim 140, J.S. Kim 41, J. Kim 59, J. Kim 70, M. Kim 19, S. Kim 18, T. Kim 140, K. Kimura 93, S. Kirsch 65, I. Kisel 39, S. Kiselev 142, A. Kisiel 137, J.P. Kitowski 2, J.L. Klay 5, J. Klein 33, S. Klein 75, C. Klein-Bösing 127, M. Kleiner 65, T. Klemenz 96, A. Kluge 33, A.G. Knospe 117, C. Kobdaj 106, T. Kollegger98, – 50 – JHEP05(2024)184 A. Kondratyev 143 , N. Kondratyeva 142 , E. Kondratyuk 142 , J. Konig 65 , S.A. Konigstorfer 96 , P.J. Konopka 33, G. Kornakov 137, M. Korwieser 96, S.D. Koryciak 2, A. Kotliarov 87, V. Kovalenko 142, M. Kowalski 108, V. Kozhuharov 37, I. Králik 61, A. Kravčáková 38, L. Krcal 33,39, M. Krivda 101,61, F. Krizek 87, K. Krizkova Gajdosova 33, M. Kroesen 95, M. Krüger 65 , D.M. Krupova 36 , E. Kryshen 142 , V. Kučera 59 , C. Kuhn 130 , P.G. Kuijer 85 , T. Kumaoka126, D. Kumar136, L. Kumar 91, N. Kumar91, S. Kumar 32, S. Kundu 33, P. Kurashvili 80, A. Kurepin 142, A.B. Kurepin 142, A. Kuryakin 142, S. Kushpil 87, V. Kuskov 142, M.J. Kweon 59, Y. Kwon 140, S.L. La Pointe 39, P. La Rocca 27, A. Lakrathok106, M. Lamanna 33, A.R. Landou 74,116, R. Langoy 122, P. Larionov 33, E. Laudi 33, L. Lautner 33,96, R. Lavicka 103, R. Lea 135,56, H. Lee 105, I. Legrand 46, G. Legras 127, J. Lehrbach 39, T.M. Lelek2, R.C. Lemmon 86, I. León Monzón 110, M.M. Lesch 96 , E.D. Lesser 19 , P. Lévai 47 , X. Li 10 , J. Lien 122 , R. Lietava 101 , I. Likmeta 117 , B. Lim 25, S.H. Lim 17, V. Lindenstruth 39, A. Lindner46, C. Lippmann 98, D.H. Liu 6, J. Liu 120, G.S.S. Liveraro 112, I.M. Lofnes 21, C. Loizides 88, S. Lokos 108, J. Lömker 60, P. Loncar 34, X. Lopez 128, E. López Torres 7, P. Lu 98,121, F.V. Lugo 68, J.R. Luhder 127, M. Lunardon 28, G. Luparello 58, Y.G. Ma 40, M. Mager 33, A. Maire 130, E.M. Majerz2, M.V. Makariev 37, M. Malaev 142, G. Malfattore 26, N.M. Malik 92, Q.W. Malik20, S.K. Malik 92, L. Malinina I,V III,,143, D. Mallick 132,81, N. Mallick 49, G. Mandaglio 31,54, S.K. Mandal 80, V. Manko 142, F. Manso 128, V. Manzari 51, Y. Mao 6, R.W. Marcjan 2, G.V. Margagliotti 24, A. Margotti 52, A. Marín 98, C. Markert 109, P. Martinengo 33, M.I. Martínez 45 , G. Martínez García 104 , M.P.P. Martins 111 , S. Masciocchi 98 , M. Masera 25 , A. Masoni 53, L. Massacrier 132, O. Massen 60, A. Mastroserio 133,51, O. Matonoha 76, S. Mattiazzo 28, A. Matyja 108, C. Mayer 108, A.L. Mazuecos 33, F. Mazzaschi 25, M. Mazzilli 33, J.E. Mdhluli 124, Y. Melikyan 44, A. Menchaca-Rocha 68, J.E.M. Mendez 66, E. Meninno 103, A.S. Menon 117, M. Meres 13, S. Mhlanga115,69, Y. Miake126, L. Micheletti 33, D.L. Mihaylov 96, K. Mikhaylov 143,142, A.N. Mishra 47, D. Miśkowiec 98, A. Modak 4, B. Mohanty81, M. Mohisin Khan V I,16, M.A. Molander 44, S. Monira 137, C. Mordasini 118, D.A. Moreira De Godoy 127, I. Morozov 142, A. Morsch 33, T. Mrnjavac 33, V. Muccifora 50, S. Muhuri 136, J.D. Mulligan 75, A. Mulliri 23, M.G. Munhoz 111, R.H. Munzer 65, H. Murakami 125 , S. Murray 115 , L. Musa 33 , J. Musinsky 61 , J.W. Myrcha 137 , B. Naik 124 , A.I. Nambrath 19 , B.K. Nandi 48 , R. Nania 52 , E. Nappi 51 , A.F. Nassirpour 18 , A. Nath 95 , C. Nattrass 123, M.N. Naydenov 37, A. Neagu20, A. Negru114, E. Nekrasova142, L. Nellen 66, R. Nepeivoda 76 , S. Nese 20 , G. Neskovic 39 , N. Nicassio 51 , B.S. Nielsen 84 , E.G. Nielsen 84 , S. Nikolaev 142, S. Nikulin 142, V. Nikulin 142, F. Noferini 52, S. Noh 12, P. Nomokonov 143, J. Norman 120, N. Novitzky 88, P. Nowakowski 137, A. Nyanin 142, J. Nystrand 21, M. Ogino 77, S. Oh 18, A. Ohlson 76, V.A. Okorokov 142, J. Oleniacz 137, A.C. Oliveira Da Silva 123, A. Onnerstad 118, C. Oppedisano 57, A. Ortiz Velasquez 66, J. Otwinowski 108 , M. Oya 93 , K. Oyama 77 , Y. Pachmayer 95 , S. Padhan 48 , D. Pagano 135,56 , G. Paić 66, S. Paisano-Guzmán 45, A. Palasciano 51, S. Panebianco 131, H. Park 126, H. Park 105, J. Park 59, J.E. Parkkila 33, Y. Patley 48, R.N. Patra92, B. Paul 23, H. Pei 6, T. Peitzmann 60 , X. Peng 11 , M. Pennisi 25 , S. Perciballi 25 , D. Peresunko 142 , G.M. Perez 7 , Y. Pestov142, V. Petrov 142, M. Petrovici 46, R.P. Pezzi 104,67, S. Piano 58, M. Pikna 13, P. Pillot 104, O. Pinazza 52,33, L. Pinsky117, C. Pinto 96, S. Pisano 50, M. Płoskoń 75, – 51 – JHEP05(2024)184 M. Planinic90, F. Pliquett65, M.G. Poghosyan 88, B. Polichtchouk 142, S. Politano 30, N. Poljak 90, A. Pop 46, S. Porteboeuf-Houssais 128, V. Pozdniakov 143, I.Y. Pozos 45, K.K. Pradhan 49, S.K. Prasad 4, S. Prasad 49, R. Preghenella 52, F. Prino 57, C.A. Pruneau 138, I. Pshenichnov 142, M. Puccio 33, S. Pucillo 25, Z. Pugelova107, S. Qiu 85, L. Quaglia 25, S. Ragoni 15, A. Rai 139, A. Rakotozafindrabe 131, L. Ramello 134,57, F. Rami 130, T.A. Rancien74, M. Rasa 27, S.S. Räsänen 44, R. Rath 52, M.P. Rauch 21, I. Ravasenga 85, K.F. Read 88,123, C. Reckziegel 113, A.R. Redelbach 39, K. Redlich V II,80, C.A. Reetz 98, H.D. Regules-Medel45, A. Rehman21, F. Reidt 33, H.A. Reme-Ness 35, Z. Rescakova38, K. Reygers 95, A. Riabov 142, V. Riabov 142, R. Ricci 29, M. Richter 20, A.A. Riedel 96, W. Riegler 33, A.G. Riffero 25, C. Ristea 64, M.V. Rodriguez 33, M. Rodríguez Cahuantzi 45, S.A. Rodríguez Ramírez 45, K. Røed 20, R. Rogalev 142, E. Rogochaya 143, T.S. Rogoschinski 65, D. Rohr 33, D. Röhrich 21, P.F. Rojas45, S. Rojas Torres 36, P.S. Rokita 137, G. Romanenko 26, F. Ronchetti 50, A. Rosano 31,54, E.D. Rosas66, K. Roslon 137, A. Rossi 55, A. Roy 49, S. Roy 48, N. Rubini 26, D. Ruggiano 137, R. Rui 24, P.G. Russek 2, R. Russo 85, A. Rustamov 82, E. Ryabinkin 142, Y. Ryabov 142, A. Rybicki 108, H. Rytkonen 118, J. Ryu 17, W. Rzesa 137, O.A.M. Saarimaki 44, S. Sadhu 32, S. Sadovsky 142, J. Saetre 21, K. Šafařík 36, P. Saha42, S.K. Saha 4, S. Saha 81, B. Sahoo 48, B. Sahoo 49, R. Sahoo 49, S. Sahoo62, D. Sahu 49, P.K. Sahu 62, J. Saini 136, K. Sajdakova38, S. Sakai 126, M.P. Salvan 98, S. Sambyal 92, D. Samitz 103, I. Sanna 33,96, T.B. Saramela111, P. Sarma 42, V. Sarritzu 23, V.M. Sarti 96, M.H.P. Sas 33, S. Sawan 81, J. Schambach 88, H.S. Scheid 65, C. Schiaua 46, R. Schicker 95, F. Schlepper 95, A. Schmah98, C. Schmidt 98, H.R. Schmidt94, M.O. Schmidt 33, M. Schmidt94, N.V. Schmidt 88, A.R. Schmier 123, R. Schotter 130, A. Schröter 39, J. Schukraft 33, K. Schweda 98, G. Scioli 26, E. Scomparin 57, J.E. Seger 15, Y. Sekiguchi125, D. Sekihata 125, M. Selina 85, I. Selyuzhenkov 98, S. Senyukov 130, J.J. Seo 95,59, D. Serebryakov 142, L. Šerkšnyt˙e 96, A. Sevcenco 64, T.J. Shaba 69, A. Shabetai 104, R. Shahoyan33, A. Shangaraev 142, A. Sharma91, B. Sharma 92, D. Sharma 48, H. Sharma 55, M. Sharma 92, S. Sharma 77, S. Sharma 92, U. Sharma 92, A. Shatat 132, O. Sheibani117, K. Shigaki 93, M. Shimomura78, J. Shin12, S. Shirinkin 142, Q. Shou 40, Y. Sibiriak 142, S. Siddhanta 53, T. Siemiarczuk 80, T.F. Silva 111, D. Silvermyr 76, T. Simantathammakul106, R. Simeonov 37, B. Singh92, B. Singh 96, K. Singh 49, R. Singh 81, R. Singh 92, R. Singh 49, S. Singh 16, V.K. Singh 136, V. Singhal 136, T. Sinha 100, B. Sitar 13, M. Sitta 134,57, T.B. Skaali20, G. Skorodumovs 95, M. Slupecki 44, N. Smirnov 139, R.J.M. Snellings 60, E.H. Solheim 20, J. Song 17, C. Sonnabend 33,98, F. Soramel 28, A.B. Soto-hernandez 89, R. Spijkers 85, I. Sputowska 108, J. Staa 76, J. Stachel 95, I. Stan 64, P.J. Steffanic 123, S.F. Stiefelmaier 95, D. Stocco 104, I. Storehaug 20, P. Stratmann 127, S. Strazzi 26, A. Sturniolo 31,54, C.P. Stylianidis85, A.A.P. Suaide 111, C. Suire 132, M. Sukhanov 142, M. Suljic 33, R. Sultanov 142, V. Sumberia 92, S. Sumowidagdo 83, S. Swain62, I. Szarka 13, M. Szymkowski 137, S.F. Taghavi 96, G. Taillepied 98, J. Takahashi 112, G.J. Tambave 81, S. Tang 6, Z. Tang 121, J.D. Tapia Takaki 119, N. Tapus114, L.A. Tarasovicova 127, M.G. Tarzila 46, G.F. Tassielli 32, A. Tauro 33, A. Tavira García 132, G. Tejeda Muñoz 45, A. Telesca 33, L. Terlizzi 25, C. Terrevoli 117, S. Thakur 4, D. Thomas 109, A. Tikhonov 142, N. Tiltmann 127, A.R. Timmins 117, M. Tkacik107, T. Tkacik 107, A. Toia 65, R. Tokumoto93, – 52 – JHEP05(2024)184 K. Tomohiro93, N. Topilskaya 142, M. Toppi 50, T. Tork 132, V.V. Torres 104, A.G. Torres Ramos 32, A. Trifiró 31,54, A.S. Triolo 33,31,54, S. Tripathy 52, T. Tripathy 48, S. Trogolo 33, V. Trubnikov 3, W.H. Trzaska 118, T.P. Trzcinski 137, A. Tumkin 142, R. Turrisi 55, T.S. Tveter 20, K. Ullaland 21, B. Ulukutlu 96, A. Uras 129, G.L. Usai 23, M. Vala38, N. Valle 22, L.V.R. van Doremalen60, M. van Leeuwen 85, C.A. van Veen 95, R.J.G. van Weelden 85, P. Vande Vyvre 33, D. Varga 47, Z. Varga 47, P. Vargas Torres66, M. Vasileiou 79 , A. Vasiliev 142 , O. Vázquez Doce 50 , O. Vazquez Rueda 117 , V. Vechernin 142 , E. Vercellin 25, S. Vergara Limón45, R. Verma48, L. Vermunt 98, R. Vértesi 47, M. Verweij 60, L. Vickovic34, Z. Vilakazi124, O. Villalobos Baillie 101, A. Villani 24, A. Vinogradov 142, T. Virgili 29, M.M.O. Virta 118, V. Vislavicius76, A. Vodopyanov 143, B. Volkel 33, M.A. Völkl 95 , K. Voloshin 142 , S.A. Voloshin 138 , G. Volpe 32 , B. von Haller 33 , I. Vorobyev 96 , N. Vozniuk 142, J. Vrláková 38, J. Wan40, C. Wang 40, D. Wang40, Y. Wang 40, Y. Wang 6, A. Wegrzynek 33, F.T. Weiglhofer39, S.C. Wenzel 33, J.P. Wessels 127, J. Wiechula 65, J. Wikne 20 , G. Wilk 80 , J. Wilkinson 98 , G.A. Willems 127 , B. Windelband 95 , M. Winn 131 , J.R. Wright 109, W. Wu40, Y. Wu 121, R. Xu 6, A. Yadav 43, A.K. Yadav 136, S. Yalcin 73, Y. Yamaguchi 93, S. Yang21, S. Yano 93, Z. Yin 6, I.-K. Yoo 17, J.H. Yoon 59, H. Yu12, S. Yuan21, A. Yuncu 95, V. Zaccolo 24, C. Zampolli 33, F. Zanone 95, N. Zardoshti 33, A. Zarochentsev 142, P. Závada 63, N. Zaviyalov142, M. Zhalov 142, B. Zhang 6, C. Zhang 131, L. Zhang 40, M. Zhang6, S. Zhang 40, X. Zhang 6, Y. Zhang121, Z. Zhang 6, M. Zhao 10, V. Zherebchevskii 142 , Y. Zhi 10 , D. Zhou 6 , Y. Zhou 84 , J. Zhu 55,6 , Y. Zhu 6 , S.C. Zugravel 57 , N. Zurlo 135,56 1 A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation, Yerevan, Armenia 2AGH University of Krakow, Cracow, Poland 3Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, Kiev, Ukraine 4 Bose 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 China University of Geosciences, Wuhan, China 12 Chungbuk National University, Cheongju, Republic of Korea 13 Comenius University Bratislava, Faculty of Mathematics, Physics and Informatics, Bratislava, Slovak Republic 14 COMSATS University Islamabad, Islamabad, Pakistan 15 Creighton University, Omaha, Nebraska, United States 16 Department of Physics, Aligarh Muslim University, Aligarh, India 17 Department of Physics, Pusan National University, Pusan, Republic of Korea 18 Department of Physics, Sejong University, Seoul, Republic of Korea 19 Department of Physics, University of California, Berkeley, California, United States 20 Department of Physics, University of Oslo, Oslo, Norway 21 Department of Physics and Technology, University of Bergen, Bergen, Norway 22 Dipartimento di Fisica, Università di Pavia, Pavia, Italy 23 Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy 24 Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy 25 Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy – 53 – JHEP05(2024)184 26 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Bologna, Italy 27 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Catania, Italy 28 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN, Padova, Italy 29 Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo Collegato INFN, Salerno, Italy 30 Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy 31 Dipartimento di Scienze MIFT, Università di Messina, Messina, Italy 32 Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari, Italy 33 European Organization for Nuclear Research (CERN), Geneva, Switzerland 34 Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Split, Croatia 35 Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Norway 36 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic 37 Faculty of Physics, Sofia University, Sofia, Bulgaria 38 Faculty of Science, P.J. Šafárik University, Košice, Slovak Republic 39 Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 40 Fudan University, Shanghai, China 41 Gangneung-Wonju National University, Gangneung, Republic of Korea 42 Gauhati University, Department of Physics, Guwahati, India 43 Helmholtz-Institut für Strahlenund Kernphysik, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany 44 Helsinki Institute of Physics (HIP), Helsinki, Finland 45 High Energy Physics Group, Universidad Autónoma de Puebla, Puebla, Mexico 46 Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania 47 HUN-REN Wigner Research Centre for Physics, Budapest, Hungary 48 Indian Institute of Technology Bombay (IIT), Mumbai, India 49 Indian Institute of Technology Indore, Indore, India 50 INFN, Laboratori Nazionali di Frascati, Frascati, Italy 51 INFN, Sezione di Bari, Bari, Italy 52 INFN, Sezione di Bologna, Bologna, Italy 53 INFN, Sezione di Cagliari, Cagliari, Italy 54 INFN, Sezione di Catania, Catania, Italy 55 INFN, Sezione di Padova, Padova, Italy 56 INFN, Sezione di Pavia, Pavia, Italy 57 INFN, Sezione di Torino, Turin, Italy 58 INFN, Sezione di Trieste, Trieste, Italy 59 Inha University, Incheon, Republic of Korea 60 Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University/Nikhef, Utrecht, The Netherlands 61 Institute of Experimental Physics, Slovak Academy of Sciences, Košice, Slovak Republic 62 Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India 63 Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic 64 Institute of Space Science (ISS), Bucharest, Romania 65 Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany 66 Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Mexico City, Mexico 67 Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 68 Instituto de Física, Universidad Nacional Autónoma de México, Mexico City, Mexico 69 iThemba LABS, National Research Foundation, Somerset West, South Africa 70 Jeonbuk National University, Jeonju, Republic of Korea 71 Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik, Fachbereich Informatik und Mathematik, Frankfurt, Germany 72 Korea Institute of Science and Technology Information, Daejeon, Republic of Korea – 54 – JHEP05(2024)184 73 KTO Karatay University, Konya, Turkey 74 Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS-IN2P3, Grenoble, France 75 Lawrence Berkeley National Laboratory, Berkeley, California, United States 76 Lund University Department of Physics, Division of Particle Physics, Lund, Sweden 77 Nagasaki Institute of Applied Science, Nagasaki, Japan 78 Nara Women’s University (NWU), Nara, Japan 79 National and Kapodistrian University of Athens, School of Science, Department of Physics, Athens, Greece 80 National Centre for Nuclear Research, Warsaw, Poland 81 National Institute of Science Education and Research, Homi Bhabha National Institute, Jatni, India 82 National Nuclear Research Center, Baku, Azerbaijan 83 National Research and Innovation Agency - BRIN, Jakarta, Indonesia 84 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark 85 Nikhef, National institute for subatomic physics, Amsterdam, The Netherlands 86 Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom 87 Nuclear Physics Institute of the Czech Academy of Sciences, Husinec-Řež, Czech Republic 88 Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States 89 Ohio State University, Columbus, Ohio, United States 90 Physics department, Faculty of science, University of Zagreb, Zagreb, Croatia 91 Physics Department, Panjab University, Chandigarh, India 92 Physics Department, University of Jammu, Jammu, 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 Sungkyunkwan University, Suwon City, Republic of Korea 106 Suranaree University of Technology, Nakhon Ratchasima, Thailand 107 Technical University of Košice, Košice, Slovak Republic 108 The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Cracow, Poland 109 The University of Texas at Austin, Austin, Texas, United States 110 Universidad Autónoma de Sinaloa, Culiacán, Mexico 111 Universidade de São Paulo (USP), São Paulo, Brazil 112 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil 113 Universidade Federal do ABC, Santo Andre, Brazil 114 Universitatea Nationala de Stiinta si Tehnologie Politehnica Bucuresti, Bucharest, Romania 115 University of Cape Town, Cape Town, South Africa 116 University of Derby, Derby, United Kingdom 117 University of Houston, Houston, Texas, United States 118 University of Jyväskylä, Jyväskylä, Finland 119 University of Kansas, Lawrence, Kansas, United States 120 University of Liverpool, Liverpool, United Kingdom 121 University of Science and Technology of China, Hefei, China 122 University of South-Eastern Norway, Kongsberg, Norway – 55 –