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:
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
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.