Strangeness enhancement at its extremes: multiple (multi-)strange hadron production in pp collisions at $\mathbf{\sqrt{\textit{s}} = 5.02}$ TeV

TL;DR

The paper introduces an event-by-event measurement of the strange-particle multiplicity distribution ${P(n_S)}$ in pp collisions at ${\sqrt{s}=5.02~\text{TeV}}$, using a novel counting method to probe extreme strangeness content at midrapidity. Through Bayesian unfolding and careful corrections, it extracts ${P(n_S)}$ up to high multiplicities for multiple strange species and derives average multiplet yields ${\langle Y_{nS}\rangle}$, revealing nonlinearly growing strangeness enhancement with multiplicity and extending SE studies to ${\Delta S}$ up to 5. The results are contrasted with MC models (PYTHIA 8 Monash, PYTHIA 8 QCD-CR + Ropes, EPOS LHC), where QCD-based color reconnection and rope hadronization improve baryon-related trends, while EPOS LHC captures high-multiplicity behavior but misses low-multiplicity dynamics. The findings provide a new benchmark for hadronization mechanisms and motivate further high-statistics studies in Run 3 to explore simultaneous production of different strange species.

Abstract

The probability to observe a specific number of strange and multi-strange hadrons ($n_s$), denoted as $P(n_s)$, is measured by ALICE at midrapidity ($|y|<0.5$) in $\sqrt{s} = 5.02$ TeV proton-proton (pp) collisions, dividing events into several multiplicity-density classes. Exploiting a novel technique based on counting the number of strange-particle candidates event-by-event, this measurement allows one to extend the study of strangeness production beyond the mean of the distribution. This constitutes a new test bench for production mechanisms, probing events with a large imbalance between strange and non-strange content. The analysis of a large-statistics data sample makes it possible to extract $P(n_s)$ up to a maximum $n_s$ of 7 for K$^{0}_{\rm s}$, 5 for $Λ$ and $\barΛ$, 4 for $Ξ^-$ and $Ξ^+$, and 2 for $Ω^-$ and $Ω^+$. From this, the probability of producing strange hadron multiplets per event is calculated, thereby enabling the extension of the study of strangeness enhancement to extreme situations where several strange quarks hadronize in a single event at midrapidity. Moreover, comparing hadron combinations with different $\it{u}$ and $\it{d}$ quark compositions and equal overall $s$ quark content, the contribution to the enhancement pattern coming from non-strangeness related mechanisms is isolated. The results are compared with state-of-the-art phenomenological models implemented in commonly used Monte Carlo event generators, including PYTHIA 8 Monash 2013, PYTHIA 8 with QCD-based Color Reconnection and Rope Hadronization (QCD-CR + Ropes), and EPOS LHC, which incorporates both partonic interactions and hydrodynamic evolution. These comparisons show that the new approach dramatically enhances the sensitivity to the different underlying physics mechanisms modeled by each generator.

Figures (11)

• Figure 1: Invariant-mass distributions for ${\rm K}^{0}_{\rm{S}}$ (a), $\Lambda$ (b), $\Xi^-$ (c) and $\Omega^-$ (d), at low and high transverse momentum for the ${\rm INEL>0}$ event class. The dashed lines illustrate the fits used for the definition of the signal probability.
• Figure 2: (left) Signal weights at the signal peak position (circles) and at 1$\sigma$ distance from the peak (square markers) for all particles under study as a function of $p_{\rm T}$ in the ${\rm INEL>0}$ event class. Dashed lines are shown to guide the eye. (right) Un-corrected multiplicity distribution $n_{\mathrm{S}}^{reco}$ for all particles under study in the ${\rm INEL>0}$ event class, where statistical uncertainties are evaluated using the sub-samples method Statunc and are smaller than the marker size. Particles correspond to full markers, antiparticles to hollow markers.
• Figure 3: Response matrices showing the number of generated particles per event as a function of the number of reconstructed particles per event, used for the multiplicity measurement of ${\rm K}^{0}_{\rm{S}}$ (a), $\Lambda$ (b), $\Xi^-$ (c) and $\Omega^-$ (d) in pp collisions at $\sqrt{s}~=~5.02$ TeemV for the ${\rm INEL>0}$ event class.
• Figure 4: (Multi-)strange-particle-multiplicity distributions (${\rm P}(n_{\rm S})$) for ${\rm K}^{0}_{\rm{S}}$ (a), $\Lambda$ and $\overline{\Lambda}$ (b), $\Xi^-$ and $\overline{\Xi}^+$ (c) and $\Omega^-$ and $\overline{\Omega}^+$ (d) for several V0M multiplicity classes. Continuous (dashed) lines are the NBD NBDfit fit for particles (antiparticles) to the multiplicity distributions.
• Figure 5: Illustration of the strangeness-induced charged-particle multiplicity bias effect from PYTHIA 8 QCD-CR + Ropes simulation. (left) $\mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta\xspace|_{|\eta|~<~0.5}$ distribution in the ${\rm INEL>0}$, V0M-(I+II+III) and V0M-XII event classes. Open markers are the corresponding $\mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta\xspace|_{|\eta|~<~0.5}$ resulting from the additional request of at least 5 ${\rm K}^{0}_{\rm{S}}$ in the event. Continuous and dashed vertical lines show the averages of the unbiased and biased multiplicity distributions, respectively. (right) Relative shift in the $\langle\mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta\xspace\rangle$ for all the different V0M event classes when requiring at least one (full markers) or more than one (open markers) strange particles in the event. Lines are shown to guide the eye.