From raw data to neutrino candidates: a neural-network pipeline for Baikal-GVD
For neutrino astronomy, this pipeline accelerates event classification for multi-messenger alerts and diffuse flux measurements, but the improvements are incremental over existing methods.
The paper presents a neural-network pipeline for Baikal-GVD that achieves orders-of-magnitude speedup over standard reconstruction and improves noise suppression accuracy, enabling near-real-time neutrino candidate selection.
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. Applied sequentially, the pipeline achieves orders-of-magnitude speedup over the standard reconstruction chain. Moreover, noise suppression neural network surpasses the accuracy of algorithmic noise suppression algorithms and provides estimate for time residuals of the signal hits, which is crucial for identification of track-like hits. We address the domain shift between Monte Carlo simulations and experimental data by incorporating a domain adaptation technique, demonstrating improved agreement between the two domains. The resulting framework enables near-real-time event classification, with direct applications to multi-messenger alert systems and diffuse neutrino flux measurements.