Emulators for scarce and noisy data: application to auxiliary field diffusion Monte Carlo for neutron matter
ORAL
Abstract
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 use parametric matrix models to emulate AFDMC calculations of the neutron-matter EOS to perform the calculations much faster.
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 use parametric matrix models to emulate AFDMC calculations of the neutron-matter EOS to perform the calculations much faster.
*This work was supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project number 20230315ER.
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Presenters
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Cassandra L Armstrong
- Los Alamos National Laboratory (LANL)