# Improving Neutrino Oscillation Measurements through Event Classification

arXiv: https://arxiv.org/abs/2511.11938 · HTML: https://mlsanity.com/paper/2511.11938/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Builds on (most cited first, 1 of 1)

- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)

## Abstract (arXiv)

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 nuμ 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.