Rejecting noise in Baikal-GVD data with neural networks

被引：1

作者：

Kharuk, I. ^{[1
,2
]}

Rubtsov, G. ^{[1
,3
]}

Safronov, G. ^{[1
,4
]}

机构：

[1] Russian Acad Sci, Inst Nucl Res, 60Th October Anniversary Prospect 7A, Moscow 117312, Russia

[2] Moscow Inst Phys & Technol, Institutsky Lane 9, Dolgoprudnyi 141700, Moscow Region, Russia

[3] Novosibirsk State Univ, Lab Cosmol & Elementary Particle Phys, Novosibirsk 630090, Russia

[4] Joint Inst Nucl Res, Joliot Curie 6, Dubna 141980, Moscow Region, Russia

来源：

JOURNAL OF INSTRUMENTATION | 2023年 / 18卷 / 09期

关键词：

Analysis and statistical methods; Data analysis; Pattern recognition; cluster finding; calibration and fitting methods; Data Processing;

D O I：

10.1088/1748-0221/18/09/P09026

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

Baikal-GVD is a large (similar to 1 km3) 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.

引用

页数：16

共 50 条

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