| Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
|---|---|---|
| Article Number | 03053 | |
| Number of page(s) | 8 | |
| Section | Offline Computing | |
| DOI | https://doi.org/10.1051/epjconf/202125103053 | |
| Published online | 23 August 2021 | |
https://doi.org/10.1051/epjconf/202125103053
Machine learning for surface prediction in ACTS
1 Department of Physics, University of Regensburg, 93040 Regensburg, Germany
2 CERN, Esplanade des Particules 1, 1211 Meyrin, Switzerland
Published online: 23 August 2021
We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training neural networks for surface prediction and compare their results. This work is carried out in the context of the ACTS tracking toolkit.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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