Muon Identification using Neural Networks With the Muon Telescope Detector at STAR

0503101 - ÚJF 2020 RIV NL eng J - Journal Article
Brandenburg, J. D. - Adam, J. - Adamczyk, L. - Adams, J. R. - Adkins, J. K. - Agakishiev, G. - Bielčík, J. - Bielčíková, Jana - Chaloupka, P. - Federič, Pavol - Harlenderová, A. - Holub, L. - Kocan, M. - Kosarzewski, L. K. - Kramárik, L. - Kvapil, J. - Lidrych, J. - Líčeník, R. - Matonoha, O. - Moravcová, Z. - Rusňák, Jan - Rusňáková, O. - Šimko, Miroslav - Šumbera, Michal - Vaněk, Jan … Total 349 authors
Muon Identification using Neural Networks With the Muon Telescope Detector at STAR.
Nuclear Physics. A. Roč. 982, č. 2 (2019), s. 192-194. ISSN 0375-9474. E-ISSN 1873-1554.
[27th International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter 2018). Venice, 13.05.2018-19.05.2018]
Institutional support: RVO:61389005
Keywords : STAR experiment * muon telescope detector * neutral network
OECD category: Nuclear physics
Impact factor: 1.695, year: 2019
Method of publishing: Open access
https://doi.org/10.1016/j.nuclphysa.2018.10.036

The installation of the Muon Telescope Detector (MTD) at STAR allows a measurement of the dimuon (mu(+)mu(-)) production in heavy-ion collisions over a large invariant mass range for the first time. Data has been collected with the MTD from Au+Au collisions at root S-NN = 200 GeV and from p+p collisions at root S = 200 GeV. These two datasets allow for new opportunities to measure the dimuon invariant mass spectra at STAR. Before any dimuon measurements can be made, muons must be identified. This contribution presents muon identification employing deep neural networks (DNN) and compares it with other multi-variate techniques. Applications of the DNN technique for data-driven purity measurements are discussed.
Permanent Link: http://hdl.handle.net/11104/0294921