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In liquid argon time projection chambers exposed to neutrino beams andrunning on or near surface levels, cosmic muons and other cosmic particles areincident on the detectors while a single neutrino-induced event is beingrecorded. In practice, this means that data from surface liquid argon timeprojection chambers will be dominated by cosmic particles, both as a source ofevent triggers and as the majority of the particle count in trueneutrino-triggered events. In this work, we demonstrate a novel application ofdeep learning techniques to remove these background particles by applyingsemantic segmentation on full detector images from the SBND detector, the neardetector in the Fermilab Short-Baseline Neutrino Program. We use this techniqueto identify, at single image-pixel level, whether recorded activity originatedfrom cosmic particles or neutrino interactions.

Cosmic Background Removal with Deep Neural Networks in SBND