Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs
This paper demonstrates that transfer learning with Generative Adversarial Networks effectively extrapolates physics information from synthetic neutrino-carbon scattering data to related processes like neutrino-argon and antineutrino-carbon interactions, significantly outperforming models trained from scratch and maintaining high accuracy even with limited statistics.