Recurrent and Graph Neural Networks for Particle Tracking at the BM@N Experiment

This work presents a new two-step approach for elementary particle tracking that combines the advantages of both local and global tracking algorithms. On the first stage, where the graph of possible track-candidates is too big to fit into memory, a recurrent neural network model TrackNETv3 is used for building track candidates. On the second stage there is a graph neural network GraphNet needed for clearing the graph from the fake segments. The results of testing the proposed approach on the 3,2 GeV Ar+Pb simulation for the BM@N RUN7 are presented.