Machine Learning Neutrino-Nucleus Cross Sections
This addresses the problem of uncertain cross sections for neutrino physicists in long-baseline experiments, but it is an incremental proof-of-principle study.
The paper tackled the challenge of modeling neutrino-nucleus scattering cross sections for neutrino oscillation experiments by demonstrating that a neural network model, incorporating Standard Model symmetries, can accurately learn these cross sections from near-detector data. In a simulated DUNE experiment analysis, the modeled cross section achieved results consistent with using the true cross section, showing potential for data-driven approaches.
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.