High Energy Physics experiments require robust particle identification and event classification capabilities, often achievable through machine learning techniques. A Graph Neural Network (GNN) technique is employed, tailored to identifying processes occuring when a muon beam interacts with the atomic electrons of thin, low-Z targets in a series of tracking stations of the MUonE experiment 1, which aims to precisely measure the leading hadronic contribution to the muon magnetic moment anomaly. The application of developed technique has been tested in a case study utilizing simulated data from a reduced geometrical configuration of the MUonE experiment, focusing on µ + e− elastic scattering signal and e + e− pair production events. The proposed GNN classifier achieves a classification accuracy of 97 % in distinguishing signal events from pair-production background, thereby laying the groundwork for an even more precise determination of the leading-order hadronic contribution to the muon’s anomalous magnetic moment.
Hess et al. (Wed,) studied this question.