Neutrino oscillation experiments have entered a precision era, requiring increasingly sophisticated analysis techniques to fully exploit upcoming datasets. In this talk, I discuss atmospheric neutrino studies in the Deep Underground Neutrino Experiment (DUNE). Atmospheric neutrinos provide a complementary probe to beam-based measurements, offering broad coverage in energy and baseline that enhances sensitivity to oscillation parameters and potential new physics effects.
A central aspect of this work is the inclusion of a Bayesian analysis framework for oscillation studies. This approach enables a consistent treatment of many systematic uncertainties, incorporation of prior information, and robust parameter inference, particularly in regimes with limited statistics or complex correlations. By combining advanced reconstruction techniques with Bayesian inference methods, this research aims to strengthen DUNE's capability to extract physics from atmospheric neutrinos, contributing to a more comprehensive understanding of neutrino properties and their role in fundamental physics.