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Rejecting noise in Baikal-GVD data with neural networks

arXiv 2210.04653 ↗First posted 10 Oct 2022astro-ph.IM astro-ph.HE cs.LGcited by 0

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Abstract (arXiv)

Baikal-GVD is a large (~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, 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 39 references that are in the map. First posted on arXiv 10 Oct 2022.

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References · 14

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2 more
  1. 2020Point Proposal Network for Reconstructing 3D Particle Endpoints with Sub-Pixel Precision in Liquid Argon Time Projection Chamberscited by 4
  2. 2021Deep learning method for identifying mass composition of ultra-high-energy cosmic rayscited by 2

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