Improving Neutrino Oscillation Measurements through Event Classification

TL;DR

This paper proposes a machine learning-based event classification method to improve neutrino energy reconstruction, reducing systematic uncertainties in oscillation experiments.

Contribution

It introduces a classification strategy that leverages interaction type differences to enhance energy reconstruction accuracy in neutrino experiments.

Findings

Classification improves energy reconstruction accuracy by 10-20%.

Method is robust against microphysics mismodeling.

Enhanced sensitivity in DUNE neutrino analysis.

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 $\nu_\mu$ 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.