https://doi.org/10.1051/epjconf/202022603009
BM@N Tracking with Novel Deep Learning Methods
1
Sukhoi State Technical University of Gomel,
October Ave. 48,
246746
Gomel,
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: kaliostrogoblin3@gmail.com
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.
