Machine Learning Neutrino-Nucleus Cross Sections

• URL: http://arxiv.org/abs/2412.16303v2
• Date: Thu, 08 May 2025 18:00:00 GMT
• Title: Machine Learning Neutrino-Nucleus Cross Sections
• Authors: Daniel C. Hackett, Joshua Isaacson, Shirley Weishi Li, Karla Tame-Narvaez, Michael L. Wagman,
• Abstract summary: We show that an accurate neural-network model of the cross section can be learned from near-detector data.<n>We then perform a neutrino oscillation analysis with simulated far-detector events, finding that the modeled cross section achieves results consistent with what could be obtained if the true cross section were known exactly.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy model of the DUNE experiment, we demonstrate that an accurate neural-network model of the cross section -- leveraging Standard Model symmetries -- can be learned from near-detector data. We then perform a neutrino oscillation analysis with simulated far-detector events, finding that the modeled cross section achieves results consistent with what could be obtained if the true cross section were known exactly. This proof-of-principle study highlights the potential of future neutrino near-detector datasets and data-driven cross-section models.

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