Understanding the interactions between nucleons in dense matter is an important challenge in theoretical physics. Effective field theories have emerged as the dominant approach to address this problem at low energies, with many successful applications to the structure of nuclei and the properties of dense nucleonic matter. However, how far into the interior of neutron stars these interactions can describe dense matter is an open question. Here, we develop a framework that enables the inference of three-nucleon couplings in dense matter directly from astrophysical neutron star observations. We apply this formalism to the LIGO/Virgo gravitational-wave event GW170817 and the X-ray measurements from NASA’s Neutron Star Interior Composition Explorer and establish direct constraints for the coup…

Discussion In this paper, we introduce a framework that allows for the inference of couplings describing microscopic 3N interactions from astrophysical observations of neutron stars. Naively, one would assume that the complex multi-physics calculations required to compute neutron star observables starting from microscopic Hamiltonians are too computationally expensive to allow for a full stochastic sampling of the posterior distribution. We have overcome this challenge by enhancing our Bayesian inference approach with machine learning. In particular, we employ two machine-learning methods—the parametric matrix model and the ensemble neural-network method—that circumvent this challenge by drastically speeding up our likelihood evaluations. The inference of LECs from neutron star observations provides several benefits. While LECs can be adjusted to data on atomic nuclei or scattering, our novel approach makes it possible to constrain interactions using the densest and most neutron-rich system in the cosmos. Furthermore, it is possible that improved nuclear interactions involve not only higher orders in the EFT expansion, but the addition of new degrees of freedom, such as the Δ reson…

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