Strangeness production in light-ion collisions with ALICE at the LHC

Technical Summary: Strangeness Production in Light-Ion Collisions with ALICE at the LHC

Problem and Motivation
Historically, the enhanced production of strange hadrons in heavy-ion collisions relative to minimum-bias proton-proton ($pp$) collisions has been interpreted as a signature of deconfined quark–gluon plasma (QGP) formation, a phenomenon known as strangeness enhancement. Recent observations by the ALICE Collaboration have revealed that small collision systems ($pp$ and proton–lead, $p$–Pb) also exhibit a smooth increase in strange-hadron-to-pion yield ratios as a function of charged-particle multiplicity, reaching values comparable to peripheral lead–lead (Pb–Pb) collisions. While this suggests that final-state multiplicity is a central driver of strangeness production, the microscopic origin of this enhancement in small systems remains unclear. Current QCD-inspired Monte Carlo generators fail to provide a quantitative description of these trends. Consequently, there is a need for additional experimental constraints to disentangle the role of multiplicity from effects related to collision geometry, system size, and hadronization dynamics.

Methodology
This study utilizes the first oxygen–oxygen (OO) collision data collected by ALICE at a center-of-mass energy per nucleon pair of $\sqrt{s_{NN}} = 5.36$ TeV. OO collisions serve as an intermediate-size system, bridging the gap between small ($pp$, $p$–Pb) and large (Pb–Pb) systems, offering comparable multiplicities to high-multiplicity small-system events but with distinct initial geometry and collision volume.

The analysis focuses on the reconstruction of strange and multi-strange hadrons ($K^0_S$, $\Lambda$, $\Xi$, and $\Omega$) at midrapidity ($|y| < 0.5$) via their characteristic weak-decay topologies:

• Reconstruction: $K^0_S$ and $\Lambda$ candidates are identified through V0 decays, while $\Xi$ and $\Omega$ baryons are reconstructed via cascade decays.
• Tracking and Identification: Charged-particle tracking and identification are performed using the Inner Tracking System (ITS) and the Time Projection Chamber (TPC).
• Yield Extraction: Raw yields are extracted by fitting invariant-mass distributions. Signal peaks are modeled using Gaussian functions for $K^0_S$ and $\Lambda$, and double-sided Crystal Ball functions for $\Xi$ and $\Omega$. Backgrounds are modeled with particle-dependent functions (exponential or polynomials).
• Corrections: Raw yields are corrected for acceptance, reconstruction efficiency, selection efficiency, signal losses due to event selection, and event-normalization effects.
• Event Activity: The analysis is differential in event activity, defined by the signal amplitude measured by the Fast Interaction Trigger (FT0) detectors, specifically using the FT0C amplitude (covering $-3.3 < \eta < -2.1$) to define centrality percentiles.

Key Results
The paper presents the first centrality-differential measurements of strange particle production in OO collisions:

1. Spectral Evolution: The transverse momentum ($p_T$) spectra of $K^0_S$ exhibit a clear evolution with centrality. Moving from peripheral to central collisions, the distributions become harder, showing a less steep fall-off and enhanced relative contributions at intermediate and high $p_T$. This trend is consistent with stronger radial collective effects in higher-multiplicity events.
2. Baryon-to-Meson Ratios: The ratio $(\Lambda + \bar{\Lambda})/(2K^0_S)$ displays a pronounced enhancement at intermediate $p_T$ ($\approx 2–3$ GeV/c), with the peak shifting to higher $p_T$ and becoming more prominent in central collisions. This behavior is qualitatively consistent with collective radial expansion, which affects heavier baryons more strongly. At high $p_T$, the ratios converge across centrality classes, suggesting a transition to fragmentation-dominated processes.
3. Mean Transverse Momentum ($\langle p_T \rangle$): The mean transverse momentum increases with charged-particle multiplicity density ($\langle dN_{ch}/d\eta \rangle$) for all species, with a stronger increase observed for heavier hadrons. Crucially, while OO collisions cover a multiplicity range comparable to small systems, the evolution of $\langle p_T \rangle$ follows the trend observed in Pb–Pb collisions more closely than that of $pp$ or $p$–Pb. This indicates that similar final-state multiplicities do not necessarily imply identical particle-production dynamics.
4. Strangeness Enhancement Scaling: The ratios of integrated yields ($\Lambda$, $\Xi$, $\Omega$) to $2K^0_S$ as a function of multiplicity density in OO collisions follow the smooth evolution observed in $pp$, $p$–Pb, and Pb–Pb systems. This confirms charged-particle multiplicity as an effective scaling variable for relative strange-hadron production across different system sizes.

Model Comparisons
The experimental results are compared with predictions from Monte Carlo generators, specifically Pythia8 Angantyr (for OO and Pb–Pb) and Pythia8 Ropes (for $pp$). The models do not fully reproduce both the magnitude and the multiplicity dependence of the measured $\langle p_T \rangle$ values, highlighting limitations in current modeling of spectral hardening across different collision systems.

Significance and Claims
The paper claims that OO collisions provide a unique "bridge" between small and large collision systems. The results demonstrate that while integrated yield ratios are largely governed by multiplicity, dynamical observables (such as spectral hardening and baryon-to-meson ratios) reveal that the underlying hadronization dynamics differ between systems even at similar multiplicities. Specifically, the OO data allows for testing whether the smooth evolution from $pp$ to Pb–Pb reflects a common underlying mechanism; the findings indicate that while integrated yields scale smoothly, deviations emerge when system size and initial geometry are altered at comparable multiplicities. These findings provide new constraints on the mechanisms of strangeness production and challenge existing theoretical frameworks to account for the interplay between multiplicity, system size, and collective dynamics.