AI RESEARCH
Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs
arXiv CS.LG
•
ArXi:2508.12987v2 Announce Type: replace-cross Transfer learning (TL) is used to extrapolate the physics information encoded in a Generative Adversarial Network (GAN) trained on synthetic neutrino-carbon inclusive scattering data to related processes such as neutrino-argon and antineutrino-carbon interactions. We investigate how much of the underlying lepton-nucleus dynamics is shared across different targets and processes. We also assess the effectiveness of TL when