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Centrality, as a geometrical property of the collision, is crucial for thephysical interpretation of nucleus-nucleus and proton-nucleus experimentaldata. 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 relyon a single detector (either on the signal in zero-degree calorimeters or onthe multiplicity in some semi-central rapidity range). In the present work, wemade an attempt to develop an approach for centrality determination that isbased on machine-learning techniques and utilizes information from severaldetector subsystems simultaneously. Different event classifiers are suggestedand evaluated for their selectivity power in terms of the number ofnucleons-participants and the impact parameter of the collision. Finercentrality resolution may allow to reduce impact from so-called volumefluctuations on physical observables being studied in heavy-ion experimentslike ALICE at the LHC and fixed target experiment NA61/SHINE on SPS.

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