A study on nuclear binding energy and beta decay energy using deep neural networks

Abstract

The Artificial Neural Network (ANN) is one of the innovative methods for predicting the structural and dynamic properties of atomic nuclei. In this work, we employed machine learning based on the Deep Neural Network (DNN) technique to find the ground state binding energy and beta decay energy of various nuclei. 3560 experimental nuclear data sets have been used for training and validating the DNN model. Neutron number (N), proton number (Z), the pairing term (δ), a magic number greater than or equal to N and Z, asymmetry factor (a) and promiscuity factor (P) are used as input parameters. The Rectified Linear Unit (ReLU) function and Mean Absolute Error (MAE) are used as activation and Loss functions for the training process. We perform the predictions of ground state binding energy per nucleon and beta decay energies in Zinc and Promethium isotopes using the DNN model, which is in good agreement with experimental results.