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 126.96.36.1995.
|Title of host publication||Book of Abstracts of Heavy Flavour Data Mining workshop|
|Publication status||Published - 2016|
Altsybeev, I., & Kovalenko, V. (2016). Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions (abstract for Heavy Flavour Data Mining workshop). In Book of Abstracts of Heavy Flavour Data Mining workshop https://indico.cern.ch/event/433556/abstract-book.pdf