Exploring quantum high energy density matter via Resonant Inelastic X-ray Scattering

高亮度第四代自由电子激光（FEL）光源的出现彻底改变了我们以前所未有的精度和控制研究致密等离子体的能力。新的高重复率，高能量的激光驱动器的自由电子激光光束线，如偶极激光器在高能量密度（HED）的欧洲XFEL的终端站，将允许主机的新的压缩实验控制良好的HED条件进行调查。特别是，能够调整等离子体和压缩固体中原子之间的原子间距，使内壳层电子开始重叠、相互作用和杂交，这一点特别令人感兴趣，因为它将为密集等离子体中新的量子前沿提供无与伦比的实验途径。该项目旨在开发诊断此类系统所需的光谱工具，并将其应用于全世界自由电子激光设施的实验活动。我们将主要集中在方法来提取详细的电子结构和激发光谱的HED系统通过共振非弹性X射线散射（RIXS）。通过使用RIXS来探索高度压缩系统中的时间分辨态密度，我们将探索核电子杂化是否在高密度下发生，以及它是否可以在极端条件下导致新的键合形式。我们将进一步研究电子去局域化和再局域化的本质，并研究等离子体中电离能的降低是如何作为密度的函数而变化的。在计算方面，该项目将利用该小组在原子动力学和量子电子结构模拟方面的大量能力，以及大规模数据分析的机器学习方法，包括使用基于快速深度学习的仿真器和智能优化。这将确保我们能够充分利用自由电子激光器上非常宝贵和有限的实验时间，从高重复率，高通量的实验活动中提取最大的信息。

The advent of high-brightness 4th generation free-electron laser (FEL) light sources has revolutionised our ability to study dense plasmas with unprecedented precision and control. The addition of new high-repetition rate, high energy laser drivers to FEL beamlines, such as the Dipole laser at the high-energy-density (HED) endstation of the European XFEL, will allow for a host of new compression experiments in well-controlled HED conditions to be investigated. In particular, the capability to tune the inter-atomic spacing between atoms in plasmas and compressed solids to the point where inner-shell electrons start overlapping, interacting and hybridizing, is of particular interest as it will provide unparalleled experimental access to a new quantum frontier in dense plasmas.This project aims to develop the spectroscopic tools needed to diagnose such systems, and apply them to experimental campaigns at FEL facilities world-wide. We will focus primarily on methods to extract the detailed electronic structure and excitation spectrum of HED systems via resonant inelastic x-ray scattering (RIXS). By using RIXS to explore the time-resolved density of states in highly compressed systems we will explore whether core-electron-hybridization does take place at high densities, and if it can lead to new forms of bonding in extreme conditions. We will further investigate the nature of electron de- and re-localization, and study how the depression of the ionization energy in plasmas changes as a function of density.On the computational side the project will leverage the substantial capabilities present in the group on atomic kinetics and quantum electronic structure simulations, and on machine learning approaches to large-scale data analysis, including the use of fast deep-learning-based emulators and intelligent optimization. This will ensure we will be able to make best use of the highly valuable and limited experimental time on FELs to extract maximum information from high-repetition rate, high-throughput experimental campaigns.