arXiv:2605.31391v1 Announce Type: cross Abstract: Modern machine learning techniques have become increasingly important in particle physics because of their powerful pattern-recognition capabilities, including in real-time data acquisition where stringent runtime constraints apply. This paper details the performance of deep-learning-based trigger algorithms for a large water Cherenkov detector such as Hyper-Kamiokande aimed at low-energy neutrino events (below 7 MeV). The performance of custom neural-network supervised classifiers is shown alongside two anomaly-detection approaches trained sol
The increasing maturity of deep learning techniques allows for their application in highly specialized scientific fields with stringent real-time constraints, like particle physics experiments.
This development enhances the ability to detect crucial low-energy events in large-scale physics experiments, potentially leading to new discoveries and pushing the boundaries of scientific understanding.
The adoption of deep-learning-based trigger algorithms improves the efficiency and sensitivity of particle detectors, enabling more precise and comprehensive data acquisition for phenomena like neutrino events.
- · Particle physics research institutions
- · AI/ML model developers
- · High-performance computing providers
- · Scientific instrument manufacturers
- · Traditional algorithmic trigger systems
- · Research groups without AI/ML expertise
Improved detection capabilities for rare low-energy physics events in experiments worldwide.
Accelerated pace of discovery in fundamental physics due to more efficient data analysis and event triggering.
Potential for new AI-driven methods to become standard across various scientific data acquisition and real-time processing challenges, beyond physics.
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Read at arXiv cs.LG