Dynamics and Thermodynamics of Neutron-Rich Nuclear Matter

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A major long-term effort within the domain of nuclear science is to understand the properties of matter under extreme conditions of temperature and pressure found in exotic stellar environments such as neutron stars, core-collapse supernovae, and neutron star mergers. The strong nuclear force plays a crucial role in shaping the structure, evolution, and observable emissions of these high-energy astrophysical systems. To support this effort, research to develop improved modeling of hot and dense matter based on state-of-the-art theories of the strong force are under development. The research takes advantage of the tremendous progress that has been achieved in the field of machine learning to facilitate the theoretical modeling. This research enables more reliable predictions for the electromagnetic, neutrino, and gravitational wave signals from supernovae and neutron star mergers that may be observed with space-based x-ray telescopes, ground-based neutrino detectors, and gravitational wave detectors. The results are also being used to improve our understanding of the strong nuclear force from astronomical observations of neutron stars, supernovae, and neutron star mergers. Current numerical simulations of core-collapse supernovae and neutron star mergers largely rely on nuclear equations of state built from phenomenological mean field models. While phenomenological nuclear forces provide a computationally efficient framework for calculating the pressure of hot and dense matter, the systematic uncertainties associated with missing physics can be difficult to fully assess. An alternative but more computationally demanding approach is to develop fundamental theories that include realistic nuclear microphysics and more robust uncertainty quantification. This is an immediate priority in low-energy nuclear physics, given that accurate multi-dimensional modeling of supernovae and neutron star mergers relies on quality nuclear theory inputs, including the equation of state and neutrino reaction rates. This research utilizes new machine learning methods that enable the calculation of high-order many-body perturbation theory corrections to the finite temperature nuclear equation of state. The research is also developing a new matrix inversion method to compute nuclear matter response functions in the random phase approximation using high-precision chiral nuclear forces.This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.