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- W4306751446 abstract "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." @default.
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- W4306751446 date "2022-10-19" @default.
- W4306751446 modified "2023-10-11" @default.
- W4306751446 title "Recurrent and Graph Neural Networks for Particle Tracking at the BM@N Experiment" @default.
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- W4306751446 doi "https://doi.org/10.1007/978-3-031-19032-2_32" @default.
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