PoS - Proceedings of Science
Volume 414 - 41st International Conference on High Energy physics (ICHEP2022) - Neutrino Physics
Dark rate reduction with machine learning techniques for the Hyper-Kamiokande experiment
A. Langella*, L. Nascimento Machado and B. Spisso
Full text: pdf
Pre-published on: November 17, 2022
Published on:
Abstract
The next generation water-Cherenkov detector Hyper-Kamiokande (Hyper-K), is currently under
construction in Japan and it is expected to be ready for data taking in 2027. Thanks to its
huge fiducial volume and high statistics, Hyper-K will contribute to many investigations such as
CP-violation, determination of neutrino mass ordering and potential observations of neutrinos
from astrophysical sources. To increase the sensitivity of the detector, Hyper-K will have a
hybrid configuration of photo-detectors: thousands of 20-inch photo-multipliers tubes (PMTs)
will be combined with modules containing 3-inches PMTs arranged inside a pressure-resistant
vessel, called multi-PMT modules. Many efforts are on-going to reduce the expected dark counts
for a detector geometry which includes both photo-detector modules. Machine learning-based
techniques are being developed to reduce the detector’s overall dark rates, which could have a
significant impact on Hyper-K’s sensitivity to low-energy neutrinos.
DOI: https://doi.org/10.22323/1.414.0601
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