  For current and future neutrino oscillation experiments using large Liquid
Argon Time Projection Chambers (LAr-TPCs), a key challenge is identifying
neutrino interactions from the pervading cosmic-ray background. Rejection of
such background is often possible using traditional cut-based selections, but
this typically requires the prior use of computationally expensive
reconstruction algorithms. This work demonstrates an alternative approach of
using a 3D Submanifold Sparse Convolutional Network trained on low-level
information from the scintillation light signal of interactions inside
LAr-TPCs. This technique is applied to example simulations from ICARUS, the far
detector of the Short Baseline Neutrino (SBN) program at Fermilab. The results
of the network, show that cosmic background is reduced by up to 76.3% whilst
neutrino interaction selection efficiency remains over 98.9%. We further
present a way to mitigate potential biases from imperfect input simulations by
applying Domain Adversarial Neural Networks (DANNs), for which modified
simulated samples are introduced to imitate real data and a small portion of
them are used for adverserial training. A series of mock-data studies are
performed and demonstrate the effectiveness of using DANNs to mitigate biases,
showing neutrino interaction selection efficiency performances significantly
better than that achieved without the adversarial training.
