Machine Learning for Uncertainty Quantification in Neutron Matter

Understanding the equation of state (EOS) of pure neutron matter is necessary for interpreting observations of neutron stars. Reliable data analyses of these observations require well-quantified uncertainties for the EOS input, propagating uncertainties from nuclear interactions to the EOS. Then, observations can, in turn, put constraints on nuclear interaction parameters. However, both applications require us to sample millions of nuclear Hamiltonians, solving the nuclear many-body problem for each one. Quantum Monte Carlo methods, such as Auxiliary field diffusion Monte Carlo (AFDMC), provide precise and accurate results for the neutron matter EOS. However, AFDMC is very computationally expensive which makes it unsuitable for any sampling of nuclear Hamiltonians. In this talk, I explain how to develop emulators based on parametric matrix models to emulate AFDMC calculations of the neutron-matter EOS to perform the calculations much faster and provide well-quantified uncertainties.