Open AccessApplication of Machine Learning methods for centrality determination in heavy ion reactions at the BM@N and MPD@NICA
Author(s) -
N. Karpushkin,
M. Golubeva,
F. Guber,
A. Ivashkin,
S. V. Morozov
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1690/1/012121
Subject(s) - centrality , calorimeter (particle physics) , physics , hadron , nuclear physics , feature (linguistics) , large hadron collider , heavy ion , relativistic heavy ion collider , particle physics , computer science , ion , optics , statistics , mathematics , quantum mechanics , linguistics , philosophy , detector
Forward hadron calorimeters in heavy ion experiments are used to determine the centrality and orientation of reaction plane in nucleus-nucleus collisions. In BM@N and MPD@NICA experiments hadron calorimeters with a beam hole in the center will be used, which is motivated by high radiation doses at the BM@N and by the design of the MPD collider experiment. This feature makes it impossible to determine centrality from only the total energy deposition in the calorimeters. Therefore, an approach using machine learning methods was developed to solve the centrality problem. This approach uses information on the energy distribution of particles over the calorimeter surface. The report is dedicated to the description of the new approach for centrality determination. The results of applying the approach to the simulation data of the BM@N and MPD@NICA experiments will be shown.