A Bayesian Analysis of Nuclear Deformation Properties with Skyrme Energy Functionals

Abstract

In spite of numerous scientific and practical applications, there is still no comprehensive theoretical description of the nuclear fission process based solely on protons, neutrons and their interactions. The most advanced simulations of fission are currently carried out within nuclear density functional theory (DFT). In spite of being fully quantum-mechanical and rooted in the theory of nuclear forces, DFT still depends on a dozen or so parameters characterizing the energy functional. Calibrating these parameters on experimental data results in uncertainties that must be quantified for applications. This task is very challenging because of the high computational cost of DFT calculations for fission. In this paper, we use Gaussian processes to build emulators of DFT models in order to quantify and propagate statistical uncertainties of theoretical predictions for a range of nuclear deformations relevant to describing the fission process.

arXiv comment18 pages, 7 figures, 4 tables, 46 references

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Cited by in corpus (4)

• Machine Learning in Nuclear Physics
• Theory of Nuclear Fission
• Performing Bayesian analyses with AZURE2 using BRICK: an application to the ${}^7$Be system
• Building Surrogate Models of Nuclear Density Functional Theory with Gaussian Processesand Autoencoders