Emulators for Scarce and Noisy Data: Application to Auxiliary-Field Diffusion Monte Carlo for Neutron Matter
Cassandra L. Armstrong, Pablo Giuliani, Kyle Godbey, Rahul Somasundaram, and Ingo Tews

TL;DR
This paper develops fast emulators for auxiliary-field diffusion Monte Carlo calculations of neutron matter, enabling efficient uncertainty propagation from nuclear interaction parameters to the equation of state, crucial for astrophysical applications.
Contribution
It introduces parametric matrix models as emulators for AFDMC, allowing rapid and accurate uncertainty analysis of neutron matter EOS based on nuclear Hamiltonian parameters.
Findings
Emulators significantly reduce computation time for AFDMC calculations.
Uncertainty propagation from nuclear interactions to EOS is feasible with the developed models.
Robust uncertainty estimates support astrophysical data analysis and constraints.
Abstract
Understanding the equation of state (EOS) of pure neutron matter is necessary for interpreting multimessenger observations of neutron stars. Reliable data analyses of these observations require well-quantified uncertainties for the EOS input, ideally propagating uncertainties from nuclear interactions directly to the EOS. This, however, requires calculations of the EOS for a prohibitively large number 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, but they are very computationally expensive, making them unsuitable for the fast evaluations necessary for uncertainty propagation. Here, we employ parametric matrix models to develop fast emulators for AFDMC calculations of neutron matter and use them to directly…
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Taxonomy
TopicsNuclear Physics and Applications · Nuclear reactor physics and engineering · Atomic and Subatomic Physics Research
