Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs

Significant experimental efforts have been devoted to studying (anti)neutrino-nucleus interactions [1, 2] in the energy range relevant for next-generation neutrino oscillation experiments, such as Hyper-Kamiokande [3] and DUNE [4]. In parallel, theoretical models describing these interactions have been developed [2]. The outcomes of both experimental and theoretical advances are incorporated into Monte Carlo (MC) event generators, which simulate (anti)neutrino-nucleus collisions under realistic conditions [5-10]. MC generators are often tuned to reproduce experimental observations, relying on adjustable parameters that are fitted using available data [11]. However, this tuning process cannot fully compensate for the fundamental limitations of the underlying models, especially those relying on complex approximations, such as nuclear modeling. Consequently, there is a growing interest in alternative approaches to traditional MC event generation--methods that can learn directly from experimental data and dynamically refine their predictions.