Recurrent and Graph Neural Networks for Particle Tracking at the BM@N Experiment-Reference-Cited by-同舟云学术

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

Published:2022-10-19 Issue: Volume: Page:305-313
ISSN:1860-949X
Container-title:Studies in Computational Intelligence
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Author:

Rusov Daniil^ORCID,Goncharov Pavel^ORCID,Shchavelev Egor^ORCID,Lubchenkov Leonid,Nikolskaia Anastasiia^ORCID,Rezvaya Ekaterina^ORCID,Ososkov Gennady^ORCID,Zhemchugov Alexey^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-19032-2_32

Reference12 articles.

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2. Nikolskaia, A., et al.: TrackNETv3 with optimized inference for BM@N tracking. In: Proceedings of the 9th International Conference “Distributed Computing and Grid Technologies in Science and Education” (GRID 2021), 5–9 July 2021, vol. 3041, pp. 332–337. Dubna, Russia (2021)

3. Bakina, O., et al.: Global strategy of tracking on the basis of graph neural network for BES-III CGEM inner detector. In: Bakina, O., et al. (eds.) AIP Conference Proceedings, vol. 2377, no. 1, pp. 060001. AIP Publishing LLC (2021)

4. Amrouche, S., et al. (eds.) The Tracking Machine Learning Challenge: Accuracy Phase. In: Escalera, S., Herbrich, R. (eds.) The NeurIPS '18 Competition. SSCML, pp. 231–264. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29135-8_9

5. Ju, X., et al.: Performance of a geometric deep learning pipeline for HL-LHC particle tracking. Eur. Phys. J. C 81(10), 1–14 (2021). https://doi.org/10.1140/epjc/s10052-021-09675-8

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