Speaker
Description
The Deep Underground Neutrino Experiment (DUNE) aims to accomplish precision measurements of neutrino oscillation. DUNE will use the world’s most intense neutrino beam, expecting over 100 neutrino interactions in the near-site Liquid Argon detector per spill. Resolving the overlapping particle signatures in the near detector will be vital for providing precision neutrino oscillation measurements in tandem with the far site’s multiple, 17-kt, detectors. The near site will also have multiple detectors that characterize the unoscillated neutrino beam, including the System for on-Axis Neutrino Detection (SAND), a Liquid Argon Time Projection Chamber (LArTPC), and a solid scintillator muon spectrometer (TMS). This work explores improving the current machine learning reconstruction framework, which already uses input from the LArTPC, by adding input from TMS. This study uses a Graph Neural Network to predict which particle fragments should be matched across the detectors to improve the final state particle and interaction identification.
