Speaker
Description
Precision measurements of particle properties, such as the leading hadronic contribution to the muon magnetic moment anomaly, offer critical tests of the Standard Model and probes for new physics. The MUonE experiment aims to achieve this through precise reconstruction of muon-electron elastic scattering events using silicon strip tracking stations and low-Z targets, while accounting for backgrounds like pair production. In this work, we present a Graph Neural Network (GNN) approach for event classification, where graph construction encodes spatial relationships among hits to capture underlying physics. For the first time, we test it on a simulated configuration featuring three tracking
stations.
Significance
The presentation of a PointNet-based custom program for event classification that can be used for event pre-selection in MUonE, where events are classified as one of the following:
●Signal (elastic μ-e scattering)
●Main background = pair production
For the first time, the method has been tested on events with 3 tracking stations
References
https://indico.cern.ch/event/1338689/contributions/6010567/
| Experiment context, if any | Work inspired by the MUonE experiment, performed by a small group of scientists who are also MUonE members |
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