Extrapolation to infinite model space of no-core shell model calculations using machine learning

2025/11/07 by Mazur, Aleksandr, Sharypov, Roman, Shirokov, Andrey
Physics and Astronomy · #Nuclear physics research studies #Quantum Chromodynamics and Particle Interactions #High-Energy Particle Collisions Research

paper · doi:10.48550/arxiv.2511.05061

Abstract

An ensemble of neural networks is employed to extrapolate no-core shell model (NCSM) results to infinite model space for light nuclei. We present a review of our neural network extrapolations of the NCSM results obtained with the Daejeon16 NN interaction in different model spaces and with different values of the NCSM basis parameter ℏΩ for energies of nuclear states and root-mean-square (rms) radii of proton, neutron and matter distributions in light nuclei. The method yields convergent predictions with quantifiable uncertainties. Ground-state energies for ⁶Li, ⁶He, and the unbound ⁶Be, as well as the excited (3⁺,0) and (0⁺,1) states of ⁶Li, are obtained within a few hundred keV of experiment. The extrapolated radii of bound states converge well. In contrast, radii of unbound states in ⁶Be and ⁶Li do not stabilize.

Citations

• Benchmarking ANN extrapolations of the ground-state energies and radii of Li isotopes
• Accelerating an Iterative Eigensolver for Nuclear Structure Configuration Interaction Calculations on GPUs using OpenACC
• Cyclical Learning Rates for Training Neural Networks
• Adam: A Method for Stochastic Optimization

Related

• Machine Learning for Extrapolating No-Core Shell Model Results to Infinite Basis
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• Robust extrapolation using physics-related activation functions in neural networks for nuclear masses
• Benchmarking ANN extrapolations of the ground-state energies and radii of Li isotopes
• Hypernuclei with Neural Network Quantum States