Determination of photonuclear reaction cross-sections on stable P-shell nuclei by using deep neural networks

Photonuclear reactions are widely used in investigations of nuclear structure. Thus, the determination of the cross-sections are essential for the experimental studies. In the present work, (γ, n) photonuclear reaction cross-sections for stable p-shell nuclei have been estimated by using the neural network method. The main purpose of this study is to find neural network structures that give the best estimations for the cross-sections, and to compare them with the available data. These comparisons indicate the deep neural network structures that are convenient for this task. Through this procedure, we have found that the shallow NN models, tanh activation function is better than the ReLU. However, as our models become deeper, the difference between tanh and ReLU decreases considerably. In this context, we think that the crucial hyperparameters are the size of the hidden layer and neuron numbers of each layer. (author)