{"ID":2841962,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11938","arxiv_id":"2511.11938","title":"Improving Neutrino Oscillation Measurements through Event Classification","abstract":"Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematically varying amounts of missing energy and therefore yield different reconstruction performance--information that standard calorimetric approaches do not exploit. We introduce a strategy that incorporates this structure by classifying events according to their underlying interaction type prior to energy reconstruction. Using supervised machine-learning techniques trained on labeled generator events, we leverage intrinsic kinematic differences among quasi-elastic scattering, meson-exchange current, resonance production, and deep-inelastic scattering processes. A cross-generator testing framework demonstrates that this classification approach is robust to microphysics mismodeling and, when applied to a simulated DUNE $ν_μ$ disappearance analysis, yields improved accuracy and sensitivity at the 10-20% level. These results highlight a practical path toward reducing reconstruction-driven systematics in future oscillation measurements.","short_abstract":"Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematically varying amounts of...","url_abs":"https://arxiv.org/abs/2511.11938","url_pdf":"https://arxiv.org/pdf/2511.11938v2","authors":"[\"Sebastian A. R. Ellis\",\"Daniel C. Hackett\",\"Shirley Weishi Li\",\"Pedro A. N. Machado\",\"Karla Tame-Narvaez\"]","published":"2025-11-14T23:26:51Z","proceeding":"hep-ph","tasks":"[\"hep-ph\",\"cs.AI\",\"cs.LG\",\"hep-ex\"]","methods":"[]","has_code":false}