Speaker
Igor Altsybeev
(St. Petersburg State University (RU))
Description
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.
Author
Igor Altsybeev
(St. Petersburg State University (RU))
Co-author
Vladimir Kovalenko
(St. Petersburg State University (RU))