Electron-nucleus cross sections from transfer learning
This work addresses cross-section prediction in nuclear physics, but it is incremental as it applies an existing machine learning technique to a new domain.
The authors tackled the problem of predicting electron-nucleus cross sections by applying transfer learning from electron-carbon scattering data, achieving accurate predictions for targets from helium-3 to iron.
Transfer learning (TL) allows a deep neural network (DNN) trained on one type of data to be adapted for new problems with limited information. We propose to use the TL technique in physics. The DNN learns the details of one process, and after fine-tuning, it makes predictions for related processes. We consider the DNNs, trained on inclusive electron-carbon scattering data, and show that after fine-tuning, they accurately predict cross sections for electron interactions with nuclear targets ranging from helium-3 to iron.