Generative adversarial neural networks for simulating neutrino interactions

The first type of 13 interaction plays a pivotal role in the oscillation analyses carried out by the T2K and Hyper-Kamiokande experiments, and the other is important for the DUNE experiment. We consider various kinematic distributions of the charged lepton. The models we present successfully reproduce the peak structure in data distributions. Once these models are developed, they generate events significantly faster than "classical" generators. We also anticipate that these models can be adapted to more realistic scenarios after retraining them on experimental data. Essentially, they can serve as pre-trained models that can be fine-tuned for specific applications. Our study opens the door for future developments, including considering complete event topologies and realistic neutrino fluxes. Furthermore, these deep neural network models can be repurposed to simulate related processes by utilizing advanced deep learning techniques such as transfer learning [15].