The Korean Physical Society 06130 22, Teheran-ro 7-gil, Gangnam-gu, Seoul, Republic of Korea 610 Representation : Tae Won NOH TEL: 02-556-4737 FAX: 02-554-1643 E-mail : Copyright(C) KPS, All rights reserved.
30 May 2022 to 4 June 2022
Virtual Seoul
Asia/Seoul timezone

Dark rate reduction with machine learning techniques for the Hyper-Kamiokande experiment

Not scheduled
Virtual Seoul

Virtual Seoul

Poster New neutrino technologies Poster


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. For 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 optimize the sensitivity of the detector, Hyper-K will have a hybrid configuration of photodetectors: thousands of 20-inch photomultipler tubes (PMTs) will be combined with modules containing smaller PMTs arranged inside a pressure vessel, called multi-PMT modules. Many efforts are on-going to reduce the expected dark counts from both photodetectors. In this poster, we report the details and performances of multivariate analysis techniques such as Boosted Decision Tree that are currently being applied to simulated events to reduce the overall dark rates of the detector, which is significantly important for Hyper-K's sensitivity to low energy neutrinos.

Collaboration Hyper-Kamiokande Collaboration

Primary authors

Dr Lucas Nascimento Machado (University of Naples/INFN Naples) Bernardino Spisso (INFN) Aurora Langella (INFN)

Presentation Materials