Machine Learning Neutrino-Nucleus Cross Sections

Autor:	Hackett, Daniel C., Isaacson, Joshua, Li, Shirley Weishi, Tame-Narvaez, Karla, Wagman, Michael L.
Rok vydání:	2024
Předmět:	High Energy Physics - Phenomenology Computer Science - Machine Learning High Energy Physics - Experiment Nuclear Theory
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 2 figures + 6 pages Supplemental Material
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.16303 Zobrazit plný text záznamu View this record from Arxiv