RESUMEN
This paper describes a procedure for the validation of alpha-particle sources (exempt unsealed sources) to be used in experimental setups with liquefied gases at cryogenic temperatures (down to -196 °C) and high vacuum. These setups are of interest for the development and characterization of neutrino and dark matter detectors based on liquid argon, among others. Due to the high purity requirements, the sources have to withstand high vacuum and cryogenic temperatures for extended periods. The validation procedure has been applied to 241Am sources produced by electrodeposition.
RESUMEN
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 deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.