Abstract

Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of proton-nucleus and nucleus-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Contemporary methods of the 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 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. The authors acknowledge Saint-Petersburg State University for a research grant 11.38.242.2015.
Original languageUndefined
Title of host publicationBook of Abstracts of Heavy Flavour Data Mining workshop
Publication statusPublished - 2016

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