A research team from the Shanghai Institute of Applied Physics, Chinese Academy of Sciences, and collaborating institutions has successfully employed Bayesian neural networks (BNNs) to fit photonuclear (γ,n) cross-sections with remarkable reliability. By training a two-hidden-layer network on a consistent experimental dataset, and analyzing absolute and relative errors, the team confirmed efficient data learning without overfitting.
Compared to the traditional TENDL-2021 database, the BNN approach demonstrated superior accuracy in describing low-energy thresholds, GDR peak characteristics, and high-energy tails. Particularly in cases of sparse or systematically biased data, BNNs showed outstanding generalization, providing reliable predictions even for cross-sections that cannot be directly measured.
Moreover, the study highlighted the importance of consistent training data by evaluating sensitivities between different laboratory datasets. The developed approach will play a key role at the SLEGS beamline in Shanghai, supporting future research in nuclear astrophysics, nuclear material science, and radiation protection.
Enhanced Accuracy and Predictive Power for Photonuclear Cross-Sections
The proposed BNN model consistently outperformed traditional evaluations such as TENDL-2021, yielding lower average absolute errors and greater prediction stability across multiple nuclides. The model accurately predicted (γ, n) cross-sections of nuclei not included in the training data, showing great potential in estimating nuclear reaction data for unstable or experimentally inaccessible isotopes — crucial for r-process studies in astrophysics.
Quantifying Systematic Discrepancies Across Laboratories
By comparing datasets from Lawrence Livermore National Laboratory (LLNL) and Saclay, the BNN revealed systematic differences and proved capable of estimating potential biases, offering a tool for nuclear data standardization.
Guidance for Future SLEGS Experiments
The research supports future precision measurements at the Shanghai Laser Electron Gamma Source (SLEGS) beamline, enabling targeted photonuclear experiments and validation of theoretical predictions. The complete study is accessible via DOI: 10.1007/s41365-024-01611-1.
Nuclear Science and Techniques
Experimental study
Not applicable
Enhancing reliability in photonuclear cross-section fitting with Bayesian neural networks
13-Feb-2025