F. J. Nicolas-Arnaldos

arXiv2025

Cryogenic characterization of FBK NUV-HD-Cryo 3T SiPM sensors for the DUNE photon detection system

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino experiment based in the USA and composed of a Near Detector (ND) complex at Fermi National Laboratory (FNAL), and a Far Detector (FD) complex located at the Sanford Underground Research Facility (SURF) $\sim$1300\,km distant. DUNE will study neutrino oscillations looking for unresolved issues of the Standard Model of particle physics (SM) such as CP violation in the leptonic sector, neutrino mass ordering and others, starting from the early 2030s. The FD, with a mass of $\sim$17\,kt, that will exploit both ionization and scintillation signals to detect neutrino interactions with Argon. Scintillating photons in LAr will be detected by the photon detection system (PDS) based on light collectors coupled to Silicon Photomultipliers (SiPMs). During a test campaign, different laboratories of the collaboration performed an investigation of the best SiPM candidates that fulfill the DUNE FD requirements. We identified two models of SiPM, produced by Hamamatsu Photonics K.K. (HPK) and Fondazione Bruno Kessler (FBK), respectively. In this paper, we focus on the FBK selected model showing its main features. We will describe the characterization protocol, the results at both room and cryogenic temperatures and the photon detection efficiency measurements.

physics.ins-det↗

arXiv2021

Cosmic Background Removal with Deep Neural Networks in SBND

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 semantic segmentation on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, at single image-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

physics.data-an↗