Speaker
Description
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 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 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.