Machine learning based TOF charged particle identification at BM@N detector of NICA collider

Standard

Machine learning based TOF charged particle identification at BM@N detector of NICA collider. / Roudnev, V. A.; Merts, S. P.; Nemnyugin, S. A.; Stepanova, M. M.

In: Journal of Physics: Conference Series, Vol. 1479, 012043, 26.05.2020.

Research output: Contribution to journal › Conference article › peer-review

BibTeX

@article{6a4eeb4a2ef448eea97efc6c63ae67b8,

title = "Machine learning based TOF charged particle identification at BM@N detector of NICA collider",

abstract = "In the article results of charged particles identification for BMAN 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 alpha-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.",

author = "Roudnev, {V. A.} and Merts, {S. P.} and Nemnyugin, {S. A.} and Stepanova, {M. M.}",

year = "2020",

month = may,

day = "26",

doi = "10.1088/1742-6596/1479/1/012043",

language = "English",

volume = "1479",

journal = "Journal of Physics: Conference Series",

issn = "1742-6588",

publisher = "IOP Publishing Ltd.",

note = "2019 Applied Mathematics, Computational Science and Mechanics: Current Problems ; Conference date: 11-11-2019 Through 13-11-2019",

}

RIS

TY - JOUR

T1 - Machine learning based TOF charged particle identification at BM@N detector of NICA collider

AU - Roudnev, V. A.

AU - Merts, S. P.

AU - Nemnyugin, S. A.

AU - Stepanova, M. M.

PY - 2020/5/26

Y1 - 2020/5/26

N2 - In the article results of charged particles identification for BMAN 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 alpha-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.

AB - In the article results of charged particles identification for BMAN 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 alpha-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.

UR - http://www.scopus.com/inward/record.url?scp=85086307579&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1479/1/012043

DO - 10.1088/1742-6596/1479/1/012043

M3 - Conference article

AN - SCOPUS:85086307579

VL - 1479

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

M1 - 012043

T2 - 2019 Applied Mathematics, Computational Science and Mechanics: Current Problems

Y2 - 11 November 2019 through 13 November 2019

ER -

ID: 60312740