Rejecting noise in Baikal-GVD data with neural networks

• URL: http://arxiv.org/abs/2210.04653v2
• Date: Sun, 9 Jul 2023 11:36:25 GMT
• Title: Rejecting noise in Baikal-GVD data with neural networks
• Authors: I. Kharuk, G. Rubtsov, G. Safronov
• Abstract summary: We introduce a neural network for efficient separation of noise hits from the signal ones, stemming from the propagation of relativistic particles through the detector. The network's metrics reach up to 99% signal purity (precision) and 96% survival efficiency (recall) on Monte-Carlo simulated dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Baikal-GVD is a large ($\sim$1 km$^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, stemming 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.

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