Determination of Photonuclear Reaction Cross-Sections on stable p-shell Nuclei by Using Deep Neural Networks

The photonuclear reactions which is induced by high-energetic photon are one of the important type of reactions in the nuclear structure studies. In this reaction, a target material is bombarded by photons with the energies in the range of gamma-ray energy scale and the photons can statistically be absorbed by a nucleus in the target material. In order to get rid of the excess energies of the excited target nuclei, it can first emit protons, neutrons, alphas and light particles according to the separation energy thresholds. After this emitting process, generally an unstable nucleus can be formed. By the investigation of this products forming after photonuclear reactions, nuclear structure information can be obtained. In the present work, ({\gamma}, n) photonuclear reaction cross-sections on stable p-shell nuclei have been estimated by using neural network method. The main purpose of this study is to find neural network structures that give the best estimations on the cross-sections and to compare them with each other and available literature data. According to the results, the method is convenient for this task.