| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01144 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701144 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701144
A Graph Neural Network Cosmic Muon Trigger for the Mu3e Experiment
Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 226, 69120 Heidelberg
* e-mail: karres@physi.uni-heidelberg.de
Published online: 7 October 2025
The Mu3e experiment at the Paul Scherrer Institute will be searching for the charged lepton flavor-violating decay µ+ → e+e−e+. To reach its ultimate sensitivity to branching ratios in the order of 10−16, an excellent momentum resolution for the reconstructed electrons is required, which in turn necessitates precise detector alignment. To compensate for weak modes in the track-based alignment, which uses electrons and positrons from muon decays, the exploitation of cosmic ray muons is proposed.
The trajectories of cosmic ray muons are very different from the decays of stopped muons in the experiment and cannot be reconstructed using the same method in the online filter farm. For this reason and in view of their comparatively rare occurrence, a special cosmic muon trigger is being developed. A study on the application of graph neural networks to classify events and to identify cosmic muon tracks is presented.
© The Authors, published by EDP Sciences, 2025
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