Extrapolation of nuclear structure observables with artificial neural networks

Abstract

Calculations of nuclei are often carried out in finite model spaces. Thus, finite-size corrections enter, and it is necessary to extrapolate the computed observables to infinite model spaces. In this work, we employ extrapolation methods based on artificial neural networks for observables such as the ground-state energy and the point-proton radius. We extrapolate results from no-core shell model and coupled-cluster calculations to very large model spaces and estimate uncertainties. Training the network on different data typically yields extrapolation results that cluster around distinct values. We show that a preprocessing of input data, and the inclusion of correlations among the input data, reduces the problem of multiple solutions and yields more stable extrapolated results and consistent uncertainty estimates. We perform extrapolations for ground-state energies and radii in ${}^4$He, ${}^6$Li, and ${}^{16}$O, and compare the predictions from neural networks with results from infrared extrapolations.

9 pages, 8 figures, details added, accepted by Phys. Rev. C

References in corpus (15)

• Recent developments in no-core shell-model calculations
• Nuclear charge radii: Density functional theory meets Bayesian neural networks
• Convergence in the no-core shell model with low-momentum two-nucleon interactions
• Corrections to nuclear energies and radii in finite oscillator spaces
• Universal properties of infrared oscillator basis extrapolations
• Benchmark calculations for 3H, 4He, 16O and 40Ca with ab-initio coupled-cluster theory
• Bayesian Methods for Parameter Estimation in Effective Field Theories
• An artificial neural network application on nuclear charge radii
• Infrared length scale and extrapolations for the no-core shell model
• Decoding Beta-Decay Systematics: A Global Statistical Model for Beta^- Halflives
• Deep learning: Extrapolation tool for ab initio nuclear theory
• Validating neural-network refinements of nuclear mass models
• Converging sequences in the ab initio no-core shell model
• Ultraviolet extrapolations in finite oscillator bases
• Extrapolation and learning equations

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• Report from the A.I. For Nuclear Physics Workshop
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