Data-driven model validation for neutrino-nucleus cross section measurements

TL;DR

This paper discusses data-driven techniques for validating neutrino-nucleus interaction models, aiming to reduce dependence on simulations and improve the accuracy of cross section measurements in neutrino experiments.

Contribution

It introduces and evaluates data-driven validation methods that detect model defects before biasing results, enhancing the robustness of neutrino cross section measurements.

Findings

Data-driven validation effectively detects model mis-modeling.

Comparison shows data-driven methods outperform traditional validation.

Fake data studies confirm the reliability of proposed techniques.

Abstract

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mis-modeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.