Identification of muon-electron elastic events using Graph Neural Networks for precision measurements

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.