Quantitative Characterization of Quark-Gluon Plasma Properties with Dynamical Fluctuations

在极端温度和密度下量化物质的相结构，对于理解夸克和胶子-核子的组成部分-如何集体相互作用，以及阐明早期宇宙在大爆炸中创造后的第一微秒内的性质具有深远的影响。原子核的高能碰撞产生了夸克-胶子等离子体(QGP)，这是一种新的物质状态，温度约为2万亿开尔文。QGP的行为就像一种近乎完美的流体，它的流动能力是水的十倍。这个项目将开发一个新的理论框架来模拟QGP流体中的小波纹是如何演变的。通过解码实验测量中的这些波纹，PI将量化QGP的性质，并识别从普通物质到QGP的相变性质是否类似于水到水蒸气的转变。该项目将核物理、高性能计算和先进的机器学习技术交织在一起。该项目的跨学科性质有助于参与的学生在理论物理和计算科学技能方面获得扎实的培训。该项目将开发第一个理论框架，以模拟相对论重离子碰撞中随机涨落的3D动态演变。通过研究碰撞系统中能量、动量和电荷密度的涨落如何消散，人们可以通过实验测量来量化QGP的热、临界和输运性质。通过将所提出的框架与贝叶斯分析相结合，并将它们应用于大范围碰撞能量范围内的重离子碰撞，PI和他的团队将提取对QGP性质的定量唯象约束。在实验数据中搜索临界点签名有望在深度学习技术应用于物理科学方面开辟新的天地。这个框架是指导和解释相对论重离子对撞机束流能量扫描第二阶段实验的必要基础，大型强子对撞机的高光度运行。在理解这种强耦合多体系统的动力学方面的理论进步可以产生与冷原子物理学、宇宙学和中子星/黑色合并的有趣的串扰。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Quantifying the phase structure of matter at extreme temperature and density has profound impacts on understanding how quarks and gluons, the building blocks of nucleons, interact collectively and elucidating the property of the early Universe within the first microsecond after its creation in the Big Bang. High energy collisions of atomic nuclei create the Quark-Gluon Plasma (QGP), a new state of matter at about 2 trillion degrees Kelvin. The QGP behaves like a nearly perfect fluid, which flows ten times better than water. This project will develop a new theoretical framework to model how small ripples evolve in the QGP fluid. By decoding these ripples' traces in the experimental measurements, the PI will quantify the QGP properties and identify whether the nature of phase transition from ordinary matter to the QGP is like water transits to vapor. This project interweaves nuclear physics, high-performance computing, and advanced machine learning techniques. The interdisciplinary nature of the project helps the participating students acquire solid training in theoretical physics as well as computational science skills.This project will develop the first theoretical framework to model the 3D dynamical evolution of stochastic fluctuations in relativistic heavy-ion collisions. By studying how fluctuations of energy, momentum, and charge density dissipate in the collision system, one can quantify the thermal, critical, and transport properties of the QGP using experimental measurements. By integrating the proposed framework with the Bayesian analysis and applying them to heavy-ion collisions over wide ranges of collision energies, the PI and his group will extract quantitative phenomenological constraints on the QGP properties. Searching the critical point signature in experimental data is slated to break new ground in the application of deep learning techniques to physical science. This proposed framework serves as an essential infrastructure to guide and interpret the experiments at Relativistic Heavy Ion Collider Beam Energy Scan phase II, and high luminosity runs at the Large Hadron Collider. The theoretical advancement in understanding the dynamics of such a strongly-coupled many-body system can generate interesting cross-talk with cold atomic physics, cosmology, and neutron star/black mergers.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.