Bayesian analysis of relativistic nuclear energy density functionals

Nuclear energy-density functionals are among the most powerful theoretical tools for describing atomic nuclei and for connecting laboratory nuclear physics with the properties of dense matter in neutron stars. However, reliable predictions in regions where experimental data are scarce remain limited by uncertainties in model parameters, functional form, and the extrapolation of calibrated interactions to neutron-rich systems.

The central aim of this project is to develop a Gaussian Process emulator for computationally expensive Relativistic Hartree-Bogoliubov and Quasiparticle Random Phase Approximation calculations, whose direct use in Bayesian parameter estimation is currently computationally prohibitive. By providing a fast and statistically controlled surrogate for these calculations, the emulator will enable Bayesian analyses of relativistic nuclear functionals that are presently out of reach.

With the emulator in place, Bayesian inference will be used to constrain nuclear model parameters using finite-nucleus observables, including binding energies, charge radii, giant resonances, and parity-violating electron-scattering data. Particular attention will be paid to the isovector sector of the nuclear interaction, which governs neutron skins, the nuclear symmetry energy, and the equation of state of neutron-rich matter. The resulting posterior distributions will be propagated to neutron-star radii and tidal deformabilities, connecting nuclear-structure experiments with astrophysical observations within a common statistical framework.

By combining relativistic nuclear theory, Bayesian statistics, and statistical emulation, this project will provide a rigorous assessment of the predictive power of modern relativistic nuclear functionals and deliver new insights into the behavior of dense neutron-rich matter.

You will need to meet the minimum entry requirements for our Physics PhD programme.

Open to UK nationals only.

Applicants should have, or expect to obtain, a first or upper second-class UK honours degree (or equivalent) in Physics, Mathematics, or a closely related discipline. A background in quantum mechanics is essential. Experience in nuclear physics, statistical methods, or scientific programming (Python or equivalent) is desirable but not required.