  The reconstruction of charged particle trajectories in tracking detectors is
a key problem in the analysis of experimental data for high-energy and nuclear
physics. The amount of data in modern experiments is so large that classical
tracking methods, such as the Kalman filter, cannot process them fast enough.
To solve this problem, we developed two neural network algorithms based on deep
learning architectures for track recognition in pixel and strip-based particle
detectors. These are TrackNETv3 for local (track by track) and RDGraphNet for
global (all tracks in an event) tracking. These algorithms were tested using
the GEM tracker of the BM@N experiment at JINR (Dubna) and the cylindrical GEM
inner tracker of the BESIII experiment at IHEP CAS (Beijing). The RDGraphNet
model, based on a reverse directed graph, showed encouraging results: 95%
recall and 74% precision for track finding. The TrackNETv3 model demonstrated a
recall value of 95% and 76% precision. This result can be improved after
further model optimization.
