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News/Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment
arxiv
PublishedJune 1, 2026 at 4:00 AM
▲bullish

Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

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 detail

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Discussion
Mentioned models
03
  • 01
    autoencoder
  • 02
    Manifold Projection--Diffusion Recovery (MPDR)
  • 03
    custom neural-network supervised classifiers
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#particle-physics#machine-learning#real-time-processing

No replies yet. Be first.

Mentioned models
03
  • 01
    autoencoder
  • 02
    Manifold Projection--Diffusion Recovery (MPDR)
  • 03
    custom neural-network supervised classifiers
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#particle-physics#machine-learning#real-time-processing

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