Deep Learning for Nuclear Physics

Abstract

Deep learning is the state-of-the-art pattern recognition method. It is expected to help scientists to discover most relevant features from big amount of complex data. Different categories of deep learning, the best deep neural network architectures for different data structures, the interpretability of black-box models and the uncertainties of model predictions are reviewed in this article. The applications of deep learning in nuclear equation of state, nuclear structure, mass, decay and fissions are also introduced. In the end, a simple neural network is trained to predict the mass of nucleus. We found that the artificial neural network trained on experimental data has low prediction error for experimental data that are held back. Trained with experimental data, the network predictions for light neutron-rich nuclei deviate from Macro-Micro Liquid model, which indicate that there might be new physics missing in the theoretical model and more data are needed to verify this.