Quantum Many-Body Theories for the Nuclear Equation of State

核理论中的一个首要挑战是为原子核和在恒星内部和其他致密天体物理环境中发现的扩展核物质开发一个预测框架，从组成核子之间的基本相互作用开始，并解决描述其动力学的量子力学运动方程。该项目的重点是开发新的理论技术，以预测在广泛的条件下，如在恒星内部发现的扩展核物质的行为。绘制出核物质的行为作为不对称性的函数（即，不同比例的质子和中子）、密度和温度被称为构建核状态方程。该项目将把以前用于描述原子核在零温度下的性质的技术推广到扩展核物质的问题（即，在有限的温度下，由无限多个核子组成的均匀系统），以构建核状态方程。该项目开发和应用针对致密核物质的微观多体方法，并扩展到访问有限温度和动态响应特性。研究是建立在现代多体方法，如耦合集团理论和介质相似重整化群。 除了将核物质计算扩展到有限温度和动态响应特性之外，还使用机器学习和动力系统的强大技术来开发多体计算的快速准确仿真器，这对于计算密集型不确定性量化研究是必要的。此外，重整化群规模和耦合到外部探针的操作符的方案依赖性进行了研究，以更好地了解如何反应理论和结构理论计算可以在一个一致的框架进行。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

An overarching challenge in nuclear theory is to develop a predictive framework for atomic nuclei and the extended nuclear matter found in the interiors of stars and other dense astrophysical environments, starting from the fundamental interactions between the constituent nucleons and solving the quantum mechanical equations of motion that describe their dynamics. This project focuses on developing new theoretical techniques to predict the behavior of extended nuclear matter under a wide range of conditions such as those found in the interiors of stars. Mapping out the behavior of nuclear matter as a function of asymmetry (i.e., different ratios of protons and neutrons), density, and temperature is referred to as constructing the nuclear equation of state. The project will generalize techniques which have previously been used to describe the properties of atomic nuclei at zero temperature to the problem of extended nuclear matter (i.e., homogenous systems comprised of an infinite number of nucleons) at finite temperatures, in order to construct the nuclear equation of state. The project develops and applies microscopic many-body methods targeted for dense nuclear matter, with extensions to access finite temperature and dynamic response properties. The research is built around modern many-body methods such as coupled cluster theory and the in-medium similarity renormalization group. In addition to extending nuclear matter calculations to finite temperature and dynamic response properties, powerful techniques from machine learning and dynamical systems are used to develop fast and accurate emulators of our many-body calculations, which is necessary for computationally-intensive uncertainty quantification studies. Additionally, renormalization group scale and scheme dependence of operators that couple to external probes are studied to better understand how reaction theory and structure theory calculations can be carried out in a consistent framework.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.