PoS - Proceedings of Science
Volume 429 - The 6th International Workshop on Deep Learning in Computational Physics (DLCP2022) - Track1. Machine Learning in Particle Astrophysics and High Energy Physics
Deep neural network applications for particle tracking at the BM@N and SPD experiments
D. Rusov, A. Nikolskaia, P.V. Goncharov*, E. Shchavelev and G. Ososkov
Full text: pdf
Pre-published on: November 14, 2022
Published on:
Particle tracking is an essential part of any high-energy physics experiment. Well-known tracking algorithms based on the Kalman filter are not scaling well with the amounts of data being produced in modern experiments. In our work we present a particle tracking approach based on deep neural networks for the BM@N experiment and future SPD experiment. We have already applied similar approaches for BM@N RUN 6 and BES-III Monte-Carlo simulation data. This work is the next step in our ongoing study of tracking with the help of machine learning. Revised algorithms - combination of Recurrent Neural Network (RNN) and Graph Neural Network (GNN) for the BM@N RUN 7 Monte-Carlo simulation data, and GNN for the preliminary SPD Monte-Carlo simulation data are presented. Results of the track efficiency and processing speed for both experiments are demonstrated.
DOI: https://doi.org/10.22323/1.429.0005
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.