Machine learning techniques for optimisation of track selection criteria

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Machine learning techniques for optimisation of track selection criteria. / Altsybeev, Igor ; Andronov, Evgeny ; Prokhorova, Daria.

In: Journal of Physics: Conference Series, Vol. 1690, No. 1, 012119, 16.12.2020.

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

BibTeX

@article{81d6ed1b13ae4f32bd5e112420073a57,

title = "Machine learning techniques for optimisation of track selection criteria",

abstract = "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. ",

author = "Igor Altsybeev and Evgeny Andronov and Daria Prokhorova",

note = "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: {\textcopyright} Published under licence by IOP Publishing Ltd. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 5th International Conference on Particle Physics and Astrophysics, ICPPA 2020 ; Conference date: 05-10-2020 Through 09-10-2020",

year = "2020",

month = dec,

day = "16",

doi = "10.1088/1742-6596/1690/1/012119",

language = "English",

volume = "1690",

journal = "Journal of Physics: Conference Series",

issn = "1742-6588",

publisher = "IOP Publishing Ltd.",

number = "1",

}

RIS

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