Open AccessMachine learning based TOF charged particle identification at BM@N detector of NICA collider
Author(s) -
Vladimir Roudnev,
S. Merts,
S. A. Nemnyugin,
M. Stepanova
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 - 10.1088/1742-6596/1479/1/012043
Subject(s) - detector , charged particle , collider , muon , identification (biology) , pion , nuclear physics , acceleration , physics , particle identification , particle physics , experimental data , electron , large hadron collider , computer science , artificial neural network , algorithm , artificial intelligence , mathematics , statistics , optics , ion , botany , classical mechanics , quantum mechanics , biology
In the article results of charged particles identification for BM@N experiment being performed at NICA acceleration complex of Joint Institute for Nuclear Research are presented. A standard neural network-based technique of constructing a classificator is applied to the data sets obtained both from modelling of a realistic experimental setup and three synthetic data sets. The carried-out analysis demonstrates that the estimated data accuracy is insufficient to make a clear distinction between electrons, muons and pions, and also between -particles and deutrons. The problem could be solved by using an extra data from the detector or by improving the accuracy of the experimental data by two orders of magnitude.