Muon Neutrino Reconstruction at SBND using SPINE ML

The Short-Baseline Near Detector (SBND) on the Booster Neutrino Beamline (BNB) at Fermilab is the near detector of the Short-Baseline Neutrino (SBN) Program. The purpose of the SBN Program is to test for the existence of sterile neutrinos and other physics beyond the Standard Model by probing neutrino oscillation. SBND utilizes the liquid argon time projection chamber (LArTPC) technology, a fine-grained imaging detector that images charged particles using ionization charge. As imaging detectors, LArTPCs lend themselves toward the use of machine learning (ML) for image classification tasks relevant for the reconstruction of neutrino interactions. The Scalable Particle Imaging with Neural Embeddings (SPINE) package was recently developed for machine-learning-based reconstruction of neutrino interactions in LArTPC neutrino experiments, including SBND. In this talk, first studies of using SPINE ML for reconstructing muon neutrino events at SBND are presented.