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We developed a new method combining a hierarchical framework and Bayesian neural networks for nuclear physics calculations. This approach accurately predicts nuclear properties across isotopic chains, with robust uncertainty quantification.

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Area of Science:

  • Nuclear Physics
  • Computational Physics
  • Quantum Many-Body Theory

Background:

  • Ab initio nuclear calculations are computationally intensive.
  • Predicting nuclear properties across isotopic chains requires efficient methods.
  • Bayesian neural networks offer powerful tools for emulation and uncertainty quantification.

Purpose of the Study:

  • To develop a novel emulation scheme for ab initio many-body nuclear calculations.
  • To enable accurate and simultaneous predictions of nuclear properties across isotopic chains.
  • To provide robust uncertainty quantification for nuclear predictions.

Main Methods:

  • Integration of a hierarchical framework with Bayesian neural networks.
  • Application to the oxygen isotopic chain for benchmarking.
  • Global sensitivity analysis of nuclear properties.

Main Results:

  • Accurate predictions for ground-state energies and nuclear charge radii of the oxygen isotopic chain.
  • Robust uncertainty quantification achieved.
  • Successful demonstration of broad applicability across the nuclear chart.

Conclusions:

  • The novel emulation scheme provides an efficient and accurate approach for nuclear structure calculations.
  • The method allows for simultaneous predictions and uncertainty quantification across isotopic chains.
  • This framework facilitates global sensitivity analysis of nuclear forces.