Standard

Machine learning based background rejection for Baikal-GVD neutrino telescope. / Kalashev, O.; Kharuk, I.; Rubtsov, G.

In: Journal of Physics: Conference Series, Vol. 2438, No. 1, 012099, 2023.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Kalashev, O, Kharuk, I & Rubtsov, G 2023, 'Machine learning based background rejection for Baikal-GVD neutrino telescope', Journal of Physics: Conference Series, vol. 2438, no. 1, 012099. https://doi.org/10.1088/1742-6596/2438/1/012099

APA

Kalashev, O., Kharuk, I., & Rubtsov, G. (2023). Machine learning based background rejection for Baikal-GVD neutrino telescope. Journal of Physics: Conference Series, 2438(1), [012099]. https://doi.org/10.1088/1742-6596/2438/1/012099

Vancouver

Kalashev O, Kharuk I, Rubtsov G. Machine learning based background rejection for Baikal-GVD neutrino telescope. Journal of Physics: Conference Series. 2023;2438(1):012099. doi: 10.1088/1742-6596/2438/1/012099

Author

Kalashev, O. ; Kharuk, I. ; Rubtsov, G. / Machine learning based background rejection for Baikal-GVD neutrino telescope. In: Journal of Physics: Conference Series. 2023 ; Vol. 2438, No. 1.

BibTeX

@article{4ba446f8feea44d6836263f457f40feb,
title = "Machine learning based background rejection for Baikal-GVD neutrino telescope",
abstract = "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.",
author = "O. Kalashev and I. Kharuk and G. Rubtsov",
note = "The work is supported by the Ministry of Education of Russian Federation, 075-15-2020-778.; 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2021 ; Conference date: 29-11-2021 Through 03-12-2021",
year = "2023",
doi = "10.1088/1742-6596/2438/1/012099",
language = "English",
volume = "2438",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
publisher = "IOP Publishing Ltd.",
number = "1",

}

RIS

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