Predicting 28Si projectile fragmentation cross sections with Bayesian neural network method*

Abstract This study utilizes the Bayesian neural network (BNN) method in machine learning to learn and predict the cross-sectional data of 28 Si projectile fragmentation for different targets at different energies and to quantify the uncertainty. The detailed modeling process of the BNN is presented, and its prediction results are compared with those of the Cummings, Nilsen, EPAX2, EPAX3, and FRACS models and experimental measurement values. The results reveal that, compared with other models, the BNN method achieves the smallest root-mean-square error (RMSE) and the highest agreement with the experimental values. Only the BNN method and FRACS model show a significant odd-even staggering effect; however, the results of the BNN method are closer to the experimental values. Furthermore, the BNN method is the only model capable of reproducing data features with low cross-section values at Z = 9, and the average ratio of the predicted to experimental values of the BNN is close to 1.0. These results indicate that the BNN method can accurately reproduce and predict the fragment production cross sections of 28 Si projectile fragmentation and demonstrate its ability to capture key data characteristics.