AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments

TL;DR

This paper presents an AI-driven framework with real-time diagnostics and radiation-hardened sensors to improve beam stability and reduce systematics in next-generation neutrino experiments, significantly accelerating physics discoveries.

Contribution

It introduces a physics-informed digital twin and radiation-hard muon monitors for real-time beam correction and flux stabilization, enhancing neutrino experiment precision.

Findings

Flux systematic errors reduced from 5% to 1%.

Beam correction enables faster CP violation discovery.

Radiation-hard ToF sensors achieve picosecond precision.

Abstract

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.