Электронный каталог

Статья

Kharuk, I.
Rejecting Noise in Baikal-GVD Data with Neural Networks / I.Kharuk, G.Rubtsov, G.Safronov // Journal of Instrumentation [Electronic resource]. – 2023. – Vol.18, No.9. – P.P09026. – URL: https://doi.org/10.1088/1748-0221/18/09/P09026.

Baikal-GVD is a large (∼ 1 km*3) underwater neutrino telescope installed in the fresh waters of Lake Baikal. The deep lake water environment is pervaded by background light, which is detectable by Baikal-GVD's photosensors. We introduce a neural network for an efficient separation of these noise hits from the signal ones, stemmng from the propagation of relativistic particles through the detector. The model has a U-Net-like architecture and employs temporal (causal) structure of events. The neural network's metrics reach up to 99% signal purity (precision) and 96% survival efficiency (recall) on Monte-Carlo simulated dataset. We compare the developed method with the algorithmic approach to rejecting the noise and discuss other possible architectures of neural networks, including graph-based ones.



ОИЯИ = ОИЯИ (JINR)2023
Спец.(статьи,препринты) = С 344.1х - Методы регистрации нейтрино
Спец.(статьи,препринты) = С 325.1а - Нейронные сети и клеточные автоматы
Спец.(статьи,препринты) = С 17 к - Расчеты по молекулярной динамике. Численное моделирование физических задач
Бюллетени = 12/024