Abstract
We construct efficient emulators for the ab initio computation of the infinite nuclear matter equation of state. These emulators are based on the subspace-projected coupled-cluster method for which we here develop a new algorithm called small-batch voting to eliminate spurious states that might appear when emulating quantum many-body methods based on a non-Hermitian Hamiltonian. The efficiency and accuracy of these emulators facilitate a rigorous statistical analysis within which we explore nuclear matter predictions for >106 different parametrizations of a chiral interaction model with explicit Δ-isobars at next-to-next-to leading order. Constrained by nucleon-nucleon scattering phase shifts and bound-state observables of light nuclei up to He4, we use history matching to identify nonimplausible domains for the low-energy coupling constants of the chiral interaction. Within these domains we perform a Bayesian analysis using sampling and importance resampling with different likelihood calibrations and study correlations between interaction parameters, calibration observables in light nuclei, and nuclear matter saturation properties.
Original language | English |
---|---|
Article number | 064314 |
Journal | Physical Review C |
Volume | 109 |
Issue number | 6 |
DOIs | |
State | Published - Jun 2024 |
Funding
We thank A. Ekstr\u00F6m and T. Papenbrock for useful discussions. This work was supported by the Swedish Research Council (Grants No. 2017-04234 and No. 2021-04507), the European Research Council under the European Unions Horizon 2020 research and innovation program (Grant No. 758027), and the U.S. Department of Energy under Contract No. DE-AC05-00OR22725 with UT-Battelle, LLC (Oak Ridge National Laboratory). The computations and data handling were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Chalmers Centre for Computational Science and Engineering (C3SE), and the National Supercomputer Centre (NSC) partially funded by the Swedish Research Council through Grant No. 2018-05973.