Speaker
Description
As we enter the era of precision in neutrino physics, it is essential to better understand and limit systematic uncertainties in experimental setups. With the improvement of statistics and measurement quality in detectors, the uncertainties arising from Monte Carlo (MC) generators, used throughout the data analysis process, become more relevant. These uncertainties stem from limitations in the theoretical descriptions of neutrino interactions with atomic nuclei. We propose an alternative framework to conventional MC generators by employing machine learning algorithms that can learn directly from experimental measurements, even in scenarios with limited data availability. In particular, we demonstrate that Generative Adversarial Networks (GANs) can be implemented to describe the kinematics of the resulting lepton in the muon neutrino Charge Current scattering off nuclei, as a function of the incoming neutrino energy. Furthermore, we show that the physics learned by GANs can be used to improve the training efficiency of a model under different neutrino scattering configurations.
