Dynamics and Thermodynamics of Neutron-Rich Nuclear Matter

富中子核物质的动力学和热力学

• 批准号: 2209318
• 负责人: Jeremy Holt
• 金额: $ 30.11万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209318&HistoricalAwards=false
• 关键词: Dynamics; Thermodynamics; Neutron; Rich; Nuclear

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.

核科学领域的主要长期努力是在极端的温度和压力条件下，在异国情调的恒星环境（例如中子恒星，核心 - 循环超新星和中子恒星合并）中发现物质的性质。强大的核力量在塑造这些高能天体物理系统的结构，进化和可观察到的排放中起着至关重要的作用。为了支持这一努力，正在开发基于强大力量的最先进理论的研究以改进对热和密集物质的建模。该研究利用了机器学习领域取得的巨大进步，以促进理论建模。这项研究可以对来自超新星和中子星星合并的电磁，中微子和重力波信号进行更可靠的预测，这些信号可以与空间基X射线望远镜，地面中微子检测器以及重力波检测器一起观察到。结果也用于提高我们对中子星，超新星和中子星星合并的天文观察的强大核力量的理解。 当前的核心偏转超新星和中子星的数值模拟在很大程度上取决于现象学平均野外模型构建的状态的核方程。尽管现象学核力量为计算热物质的压力提供了一个计算有效的框架，但与缺失物理学相关的系统不确定性可能很难完全评估。一种替代但更高的计算方法的方法是开发包括现实的核微物理学和更健壮的不确定性定量的基本理论。鉴于超新星和中子星的准确多维建模依赖于质量核理论输入，包括状态和中微子反应率的方程，这是低能核物理学的直接优先级。这项研究利用了新的机器学习方法，可以计算高阶多体扰动理论校正国家的有限温度核方程。这项研究还开发了一种新的矩阵反转方法，以使用高精确的手性核力量来计算随机阶段的核物质响应功能。该项目促进了“宇宙中的Windows：Mult-Messenger天体物理学时代”的目标，该项目是通过评估NSF的Internition Internation deem的10大奖项之一。和更广泛的影响审查标准。