Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование
Machine learning techniques for optimisation of track selection criteria. / Altsybeev, Igor; Andronov, Evgeny; Prokhorova, Daria.
в: Journal of Physics: Conference Series, Том 1690, № 1, 012119, 16.12.2020.Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование
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TY - JOUR
T1 - Machine learning techniques for optimisation of track selection criteria
AU - Altsybeev, Igor
AU - Andronov, Evgeny
AU - Prokhorova, Daria
N1 - Funding Information: This work is supported by the Russian Science Foundation under grant 17-72-20045. We thank the members of the NA61/SHINE Collaboration for their support and help. Publisher Copyright: © Published under licence by IOP Publishing Ltd. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12/16
Y1 - 2020/12/16
N2 - Application of machine learning (ML) algorithms in high-energy physics is evolving rapidly. In particular, they could be used for the optimization of track selection criteria in the analysis of experimental data on hadronic collisions. Using Monte Carlo simulations, one can train ML classifiers to separate correctly reconstructed primary tracks from secondary and fake tracks based on their features such as a number of clusters in TPCs, distance of closest approach to an interaction vertex etc. In this paper, we present the procedure of track selection optimization based on ML techniques and applied to EPOS1.99 simulations of proton-proton interactions obtained via Shine Offline Framework. With this approach, an increase of a fraction of the selected primary tracks and reduced contamination by the secondary tracks is obtained. In case of a complex geometry of an experimental facility like NA61/SHINE, improvement of track selection leads also to a widening of the kinematical acceptance.
AB - Application of machine learning (ML) algorithms in high-energy physics is evolving rapidly. In particular, they could be used for the optimization of track selection criteria in the analysis of experimental data on hadronic collisions. Using Monte Carlo simulations, one can train ML classifiers to separate correctly reconstructed primary tracks from secondary and fake tracks based on their features such as a number of clusters in TPCs, distance of closest approach to an interaction vertex etc. In this paper, we present the procedure of track selection optimization based on ML techniques and applied to EPOS1.99 simulations of proton-proton interactions obtained via Shine Offline Framework. With this approach, an increase of a fraction of the selected primary tracks and reduced contamination by the secondary tracks is obtained. In case of a complex geometry of an experimental facility like NA61/SHINE, improvement of track selection leads also to a widening of the kinematical acceptance.
UR - http://www.scopus.com/inward/record.url?scp=85098320303&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1690/1/012119
DO - 10.1088/1742-6596/1690/1/012119
M3 - Conference article
AN - SCOPUS:85098320303
VL - 1690
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012119
T2 - 5th International Conference on Particle Physics and Astrophysics, ICPPA 2020
Y2 - 5 October 2020 through 9 October 2020
ER -
ID: 75067212