AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913982577639424 |
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| author | Ganguly, S. |
| author_facet | Ganguly, S. |
| contents | The Long Baseline Neutrino Facility (LBNF) at Fermilab will deliver a high-intensity, multi-megawatt neutrino beam to the Deep Underground Neutrino Experiment (DUNE), enabling precision tests of the three-neutrino paradigm, CP violation searches, neutrino mass ordering determination, and supernova neutrino studies. In order to accelerate DUNE's physics reach and ensure robust beam operations, we propose an integrated AI-driven framework with real-time diagnostics and radiation-hardened instrumentation. A physics-informed digital twin is at the heart of this Real-Time Beam Integrity Monitor. By reconstructing pion phase space from muon profiles and exploiting magnetic horn optic linearity, it enables spill-by-spill beam correction and flux stabilization. By using this approach, flux-related systematics could be reduced from 5\% to 1\%, potentially accelerating the discovery of CP violations by four to six years. Complementing this, a US-Japan R\&D effort will deploy a LGAD-based muon monitor in the NuMI beamline. Time of Flight (ToF) measurements can be acquired with picosecond precision using this radiation-hard system, enhancing sensitivity to horn chromatic effects. Simulations confirm strong responses to these effects. Machine learning models can predict beam quality and horn current with sub-percent accuracy. This scalable, AI-enabled strategy improves beam fidelity and reduces systematics, transforming high-power accelerator operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06645 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments Ganguly, S. Accelerator Physics The Long Baseline Neutrino Facility (LBNF) at Fermilab will deliver a high-intensity, multi-megawatt neutrino beam to the Deep Underground Neutrino Experiment (DUNE), enabling precision tests of the three-neutrino paradigm, CP violation searches, neutrino mass ordering determination, and supernova neutrino studies. In order to accelerate DUNE's physics reach and ensure robust beam operations, we propose an integrated AI-driven framework with real-time diagnostics and radiation-hardened instrumentation. A physics-informed digital twin is at the heart of this Real-Time Beam Integrity Monitor. By reconstructing pion phase space from muon profiles and exploiting magnetic horn optic linearity, it enables spill-by-spill beam correction and flux stabilization. By using this approach, flux-related systematics could be reduced from 5\% to 1\%, potentially accelerating the discovery of CP violations by four to six years. Complementing this, a US-Japan R\&D effort will deploy a LGAD-based muon monitor in the NuMI beamline. Time of Flight (ToF) measurements can be acquired with picosecond precision using this radiation-hard system, enhancing sensitivity to horn chromatic effects. Simulations confirm strong responses to these effects. Machine learning models can predict beam quality and horn current with sub-percent accuracy. This scalable, AI-enabled strategy improves beam fidelity and reduces systematics, transforming high-power accelerator operations. |
| title | AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments |
| topic | Accelerator Physics |
| url | https://arxiv.org/abs/2508.06645 |