EPJ Web Conf.
Volume 226, 2020Mathematical Modeling and Computational Physics 2019 (MMCP 2019)
|Number of page(s)||4|
|Section||Mathematical and Computational Support of the Experiments, Computing Tools, and Software Services|
|Published online||20 January 2020|
BM@N Tracking with Novel Deep Learning Methods
Sukhoi State Technical University of Gomel,
October Ave. 48,
Republic of Belarus
2 St. Petersburg State University, Universitetskaya Emb. 7/9, 199034 Saint Petersburg, Russia
3 Joint Institute for Nuclear Research, Joliot-Curie 6, 141980 Dubna, Moscow region, Russia
★ e-mail: firstname.lastname@example.org
Published online: 20 January 2020
Three deep tracking methods are presented for the BM@N experiment GEM track detector, which differ in their concepts. The first is a two-stage method with data preprocessing by a directional search in the k-d tree to find all possible candidates for tracks, and then use a deep recurrent neural network to classify them by true and ghost tracks. The second end-to-end method used a deep recurrent neural network to extrapolate the initial tracks, similar to the Kalman filter, which learns necessary parameters from the data. The third method implements our new attempt to adapt the neural graph network approach developed in the HEP.TrkX project at CERN to GEM-specific data. The results of applying these three methods to simulated events are presented.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Initial download of the metrics may take a while.