2020/04/30 by C. Drischler, R. J. Furnstahl, J. A. Melendez +2 · 5 citations
Mathematics · Physics and Astronomy · #Astrophysics #Atomic and Subatomic Physics Research #Bayesian probability #Computational physics #Equation of state #Mathematics #Neutron #Neutron star #Nuclear physics #Physics #Pulsars and Gravitational Waves Research #Quantum mechanics #Quantum, superfluid, helium dynamics #Statistical physics #Statistics #astro-ph.HE #hep-ph #nucl-ex #nucl-th
paper · pdf · doi:10.1103/physrevlett.125.202702
published as Phys. Rev. Lett. 125, 202702 (2020) · 7 pages, 2 figures, supplemental material; close to the published version; minor corrections and additional discussion of correlations in supplemental material; Jupyter notebooks for reproducing the results and figures can be found at https://buqeye.github.io/software/
openalex publication_date 2020/11/11 · arxiv created 2021/01/07 · arxiv updated 2021/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
We introduce a new framework for quantifying correlated uncertainties of the infinite-matter equation of state derived from chiral effective field theory (χEFT). Bayesian machine learning via Gaussian processes with physics-based hyperparameters allows us to efficiently quantify and propagate theoretical uncertainties of the equation of state, such as χEFT truncation errors, to derived quantities. We apply this framework to state-of-the-art many-body perturbation theory calculations with nucleon-nucleon and three-nucleon interactions up to fourth order in the χEFT expansion. This produces the first statistically robust uncertainty estimates for key quantities of neutron stars. We give results up to twice nuclear saturation density for the energy per particle, pressure, and speed of sound of neutron matter, as well as for the nuclear symmetry energy and its derivative. At nuclear saturation density, the predicted symmetry energy and its slope are consistent with experimental constraints.