AI RESEARCH
From raw data to neutrino candidates: a neural-network pipeline for Baikal-GVD
arXiv CS.LG
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ArXi:2605.11176v1 Announce Type: cross We present a neural-network-based data processing pipeline for Baikal-GVD, designed to improve event reconstruction quality and accelerate neutrino candidates selection. The pipeline comprises three stages: fast suppression of extensive air shower events, suppression of noise optical modules activations, and extraction of high confidence neutrino candidates. All three networks employ a transformer architecture that exploits inter-hit correlations through the attention mechanism.