F. Nicolas-Arnaldos

ORCID: 0009-0004-7390-303X
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About
Contact & Profiles
Research Areas
  • Neutrino Physics Research
  • Dark Matter and Cosmic Phenomena
  • Astrophysics and Cosmic Phenomena
  • Particle physics theoretical and experimental studies
  • Radiation Detection and Scintillator Technologies
  • Particle Detector Development and Performance
  • Atomic and Subatomic Physics Research
  • Particle accelerators and beam dynamics
  • Nuclear Physics and Applications
  • Quantum, superfluid, helium dynamics
  • Cold Atom Physics and Bose-Einstein Condensates

Universidad de Granada
2020-2024

Abstract The Deep Underground Neutrino Experiment (DUNE) is a next generation experiment aimed to study neutrino oscillation. Its long-baseline configuration will exploit Near Detector (ND) and Far (FD) located at distance of ∼1300 km. FD consist four Liquid Argon Time Projection Chamber (LAr TPC) modules. A Photon Detection System (PDS) be used detect the scintillation light produced inside detector after interactions. PDS based on collectors coupled Silicon Photomultipliers (SiPMs)....

10.1088/1748-0221/19/01/t01007 article EN cc-by Journal of Instrumentation 2024-01-01

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, other particles are incident the detectors while a single neutrino-induced event is being recorded. practice, this means that data from will be dominated by particles, both as source of triggers majority particle count in true neutrino-triggered events. work, we demonstrate novel application deep learning techniques remove these background applying full detector images SBND...

10.3389/frai.2021.649917 article EN cc-by Frontiers in Artificial Intelligence 2021-08-24

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons other particles are incident the detectors while a single neutrino-induced event is being recorded. practice, this means that data from will be dominated by particles, both as source of triggers majority particle count in true neutrino-triggered events. work, we demonstrate novel application deep learning techniques remove these background applying semantic segmentation full...

10.48550/arxiv.2012.01301 preprint EN other-oa arXiv (Cornell University) 2020-01-01
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