Speaker
Description
The T2K experiment is a long-baseline neutrino oscillation experiment located in Japan that has major goal to find hints of CP-violation in the leptonic sector. To further improve its sensitivity, it is crucial to precisely measure electron neutrino cross section at the near detector. This measurement has been challenging, as the T2K beam composition is mostly muon neutrinos, requiring a strong background suppression in electron neutrino event selections.
The near detector complex has been equipped with new detectors in 2024 to advance its neutrino cross section measurements. The core of the upgrade is the SuperFGD detector, an active target and a scintillation tracker which consists of 2 million 1cm^3 plastic scintillator cubes.
The study presented in this talk aims to distinguish electromagnetic showers generated from electrons from background events. To fully exploit the potential of the SuperFGD, we develop a point cloud based machine learning technique to analyze the collections of 3D hits from electron candidates. In addition to individual 3D hits, we incorporate macroscopic features that characterize the shower shape, along with complementary information from detectors outside the SuperFGD.
