Electron-nucleus cross sections from transfer learning

2024/08/19 by Graczyk, Krzysztof M., Kowal, Beata E., Ankowski, Artur M. +4 · 1 citation
#FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Machine Learning (cs.LG) #Nuclear Experiment (nucl-ex) #Nuclear Theory (nucl-th)

paper · doi:10.48550/arxiv.2408.09936

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.

Cited by

• Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data

Related

• An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients
• Transfer Learning for Analysis of Collective and Non-Collective Thomson Scattering Spectra
• Empirical fits to inclusive electron-carbon scattering data obtained by deep-learning methods
• Transfer learning of GW-Bethe-Salpeter Equation excitation energies
• Learning nuclear cross sections across the chart of nuclides with graph neural networks