Dynamics and Thermodynamics of Neutron-Rich Nuclear Matter

核科学领域的一个主要的长期努力是了解在极端温度和压力条件下物质的性质，这些极端温度和压力条件存在于奇异的恒星环境中，如中子星、核心坍缩超新星和中子星星合并。强核力在塑造这些高能天体物理系统的结构、演化和可观测到的辐射方面起着至关重要的作用。为了支持这一努力，目前正在开展研究，根据最先进的强力理论，改进热密物质的建模。该研究利用了机器学习领域取得的巨大进展，以促进理论建模。这项研究使得对超新星和中子星星合并产生的电磁波、中微子和引力波信号的预测更加可靠，这些信号可以用天基x射线望远镜、地基中微子探测器和引力波探测器观测到。这些结果也被用来提高我们对中子星、超新星和中子星星合并的天文观测中的强核力的理解。 目前对核心坍缩超新星和中子星星合并的数值模拟主要依赖于从唯象平均场模型建立的核状态方程。虽然现象学核力提供了一个计算高密度物质压力的有效框架，但与物理学缺失相关的系统不确定性可能难以完全评估。另一种计算要求更高的方法是发展基础理论，包括现实的核微观物理和更强大的不确定性量化。这是低能核物理学的当务之急，因为超新星和中子星星合并的准确多维建模依赖于高质量的核理论输入，包括状态方程和中微子反应速率。这项研究利用了新的机器学习方法，使高阶多体微扰理论修正的计算有限温度核状态方程。该研究还开发了一种新的矩阵求逆方法，用于使用高精度手征核力在随机相位近似下计算核物质响应函数。多信使天体物理学时代”，未来NSF投资的十大创意之一。该奖项反映了NSF的法定使命，并通过使用评估被认为值得支持基金会的学术价值和更广泛的影响审查标准。

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.