Differentiable Neural Network for Nuclear Scattering
Analysis
This paper introduces a novel application of Bidirectional Liquid Neural Networks (BiLNN) to solve the optical model in nuclear physics. The key contribution is a fully differentiable emulator that maps optical potential parameters to scattering wave functions. This allows for efficient uncertainty quantification and parameter optimization using gradient-based algorithms, which is crucial for modern nuclear data evaluation. The use of phase-space coordinates enables generalization across a wide range of projectile energies and target nuclei. The model's ability to extrapolate to unseen nuclei suggests it has learned the underlying physics, making it a significant advancement in the field.
Key Takeaways
- •Introduces a differentiable neural network emulator for the nuclear optical model.
- •Enables efficient uncertainty quantification and parameter optimization.
- •Generalizes across a wide range of projectile energies and target nuclei.
- •Demonstrates successful extrapolation to unseen nuclei, indicating learned physics.
“The network achieves an overall relative error of 1.2% and extrapolates successfully to nuclei not included in training.”