Результаты исследований: Научные публикации в периодических изданиях › статья по материалам конференции › Рецензирование
Machine learning based background rejection for Baikal-GVD neutrino telescope. / Kalashev, O.; Kharuk, I.; Rubtsov, G.
в: Journal of Physics: Conference Series, Том 2438, № 1, 012099, 2023.Результаты исследований: Научные публикации в периодических изданиях › статья по материалам конференции › Рецензирование
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TY - JOUR
T1 - Machine learning based background rejection for Baikal-GVD neutrino telescope
AU - Kalashev, O.
AU - Kharuk, I.
AU - Rubtsov, G.
N1 - Conference code: 20
PY - 2023
Y1 - 2023
N2 - Baikal-GVD is a gigaton-scale underwater neutrino telescope currently under construction in Lake Baikal. Its principal components are optical modules, registering photons propagating through the telescope's working volume. Part of the activations of the optical modules are due to the natural luminescence of the water, and thus appear as noise in the data. We present a neural network, which efficiently rejects this background and reaches 97% signal purity (precision) and 99% survival efficiency (recall) on the Monte-Carlo data. The neural network has a U-net like architecture based on the temporal structure of optical modules activations.
AB - Baikal-GVD is a gigaton-scale underwater neutrino telescope currently under construction in Lake Baikal. Its principal components are optical modules, registering photons propagating through the telescope's working volume. Part of the activations of the optical modules are due to the natural luminescence of the water, and thus appear as noise in the data. We present a neural network, which efficiently rejects this background and reaches 97% signal purity (precision) and 99% survival efficiency (recall) on the Monte-Carlo data. The neural network has a U-net like architecture based on the temporal structure of optical modules activations.
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85149758461&origin=inward&txGid=44896a0e693df50aa0c7c99733b596ac
UR - https://www.mendeley.com/catalogue/3d7b1c80-4a8f-30d5-914d-c4e49f65459e/
U2 - 10.1088/1742-6596/2438/1/012099
DO - 10.1088/1742-6596/2438/1/012099
M3 - Conference article
VL - 2438
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012099
T2 - 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research
Y2 - 29 November 2021 through 3 December 2021
ER -
ID: 56398919