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.
RESUMEN
The MiniBooNE-DM Collaboration searched for vector-boson mediated production of dark matter using the Fermilab 8-GeV Booster proton beam in a dedicated run with 1.86×10^{20} protons delivered to a steel beam dump. The MiniBooNE detector, 490 m downstream, is sensitive to dark matter via elastic scattering with nucleons in the detector mineral oil. Analysis methods developed for previous MiniBooNE scattering results were employed, and several constraining data sets were simultaneously analyzed to minimize systematic errors from neutrino flux and interaction rates. No excess of events over background was observed, leading to a 90% confidence limit on the dark matter cross section parameter, Y=ε^{2}α_{D}(m_{χ}/m_{V})^{4}â²10^{-8}, for α_{D}=0.5 and for dark matter masses of 0.01
