Providing physics guidance in Bayesian neural networks from the input layer: The case of giant dipole resonance predictions
Xiaohang Wang, Long Zhu, Jun Su
Abstract
Background: A Bayesian neural network (BNN) approach has been applied to evaluate and predict the nuclear data. The BNN is a numerical algorithm. When one incorporates this algorithm in nuclear physics analyses, how to maintain the scientific rigor is a key problem and presents new challenges.Purpose: In this paper, a case study on giant dipole resonance (GDR) energy is presented to illustrate the effectiveness and maneuverability of the method to provide physics guidance in the BNN from the input layer.Methods: Pearson's correlation coefficients are applied to assess the statistical dependence between nuclear properties in the ground state and the GDR energies. Then the optimal ground-state properties are employed as variables of the input layer in the BNN to evaluate and predict the GDR energies.Results: Those selected ground-state properties actively contribute to reduce the predicted errors and avoid the overfitting.Conclusions: This paper gives a demonstration to find effects of the GDR energy by using the BNN without the physics motivated model, which may be helpful to discover physics effects from the complex nuclear data.