A Data-Driven Geometric Efficiency Correction Technique: Methodology and Application in DUNE

Uncertainties in modeling neutrino-nucleus interactions limit our understanding of neutrinos. To address these challenges, the Deep Underground Neutrino Experiment (DUNE) aims to make precise measurements that require accurate efficiency corrections on neutrino events. Traditional efficiency corrections rely on Monte Carlo simulations, which can introduce significant model dependence. In this talk, I will present our newly developed data-driven event-by-event geometric efficiency correction technique. This method mitigates model dependence by replacing traditional Monte Carlo-based corrections and is broadly applicable, enhancing the accuracy of far detector predictions regardless of the analysis approaches used. Our technique represents a significant step toward more reliable neutrino oscillation measurements with the DUNE Precision Reaction Independent Spectrum Measurement (PRISM) framework.