Neural network study of the nuclear ground-state spin distribution within a random interaction ensemble

The distribution of the nuclear ground-state spin in a two-body random ensemble (TBRE) was studied using a general classification neural network (NN) model with two-body interaction matrix elements as input features and the corresponding ground-state spins as labels or output predictions. The quantum many-body system problem exceeds the capability of our optimized NNs in terms of accurately predicting the ground-state spin of each sample within the TBRE. However, our NN model effectively captured the statistical properties of the ground-state spin because it learned the empirical regularity of the ground-state spin distribution in TBRE, as discovered by physicists.