Electron-nucleus cross sections from transfer learning

Abstract

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.

4 pages, 2 figures, discussion and results for helium-3 added

References in corpus (6)

• Physics Potential of a Long Baseline Neutrino Oscillation Experiment Using J-PARC Neutrino Beam and Hyper-Kamiokande
• Inclusive quasi-elastic electron-nucleus scattering
• First Measurement of the Ti$(e,e^{\prime })\mathrm{X}$ Cross Section at Jefferson Lab
• Scaling of the F_2 structure function in nuclei and quark distributions at x>1
• Assessing the accuracy of the GENIE event generator with electron-scattering data
• Electron Scattering and Neutrino Physics

Cited by in corpus (3)

• Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs
• Bayesian Reasoning for Physics Informed Neural Networks
• Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data