Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions

TL;DR

This paper introduces machine learning classifiers that combine multiple detector signals to improve the accuracy of centrality determination in proton-nucleus and nucleus-nucleus collisions, enhancing the analysis of heavy-ion collision data.

Contribution

It develops and evaluates new machine learning-based methods for centrality estimation that utilize multiple detector subsystems simultaneously, offering finer resolution than traditional single-detector approaches.

Findings

Machine learning classifiers improve centrality resolution.

Multi-detector approach reduces volume fluctuation effects.

Enhanced centrality determination benefits heavy-ion physics analyses.

Abstract

Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of nucleus-nucleus and proton-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Common methods for centrality estimation in A-A and p-A collisions usually rely on a single detector (either on the signal in zero-degree calorimeters or on the multiplicity in some semi-central rapidity range). In the present work, we made an attempt to develop an approach for centrality determination that is based on machine-learning techniques and utilizes information from several detector subsystems simultaneously. Different event classifiers are suggested and evaluated for their selectivity power in terms of the number of nucleons-participants and the impact parameter of the collision. Finer centrality resolution may allow to reduce impact from so-called volume fluctuations on physical observables being studied in heavy-ion experiments like ALICE at the LHC and fixed target experiment NA61/SHINE on SPS.