Abstract
Nuclear saturation and the symmetry energy are key properties of low-energy nuclear physics that depend on fine details of the nuclear interaction. The equation of state around saturation is also an important anchor for extrapolations to higher densities and studies of neutron stars. Here we develop a unified statistical framework that uses realistic nuclear forces to link the theoretical modeling of finite nuclei and infinite nuclear matter. We construct fast and accurate emulators for nuclear-matter observables and employ an iterative history-matching approach to explore and reduce the enormous parameter domain of Δ-full chiral interactions. We perform rigorous uncertainty quantification and find that model calibration including O16 observables gives saturation predictions that are more precise than those that only use few-body data.
| Original language | English |
|---|---|
| Article number | L061302 |
| Journal | Physical Review C |
| Volume | 109 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 2024 |
Funding
We thank Andreas Ekström and Thomas 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. Acknowledgments. We thank Andreas Ekström and Thomas 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.