2020/12/02 by R. Acciarri, SBND Collaboration, Acciarri, R. +227
Physics and Astronomy · #Astrophysics and Cosmic Phenomena #Data Analysis #FOS: Physical sciences #Neutrino Physics Research #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2012.01301
openalex publication_date 2020/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying semantic segmentation on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, at single image-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.