EPJ Web Conf.
Volume 137, 2017XIIth Quark Confinement and the Hadron Spectrum
|Number of page(s)||10|
|Section||Statistical Methods for Physics Analysis in the XXI Century|
|Published online||22 March 2017|
Classifiers for centrality determination in proton-nucleus and nucleus-nucleus collisions
Saint-Petersburg State University, 7/9 Universitetskaya nab., St. Petersburg, 199034 Russia
Published online: 22 March 2017
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.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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