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。在FEL光束线上增加新的高重复率、高能激光驱动器，例如欧洲XFEL的高能量密度（HED）终端的偶极子激光器，将允许在控制良好的HED条件下进行大量新的压缩实验。特别是，调整等离子体和压缩固体中原子之间原子间距的能力，使内层电子开始重叠，相互作用和杂交，这是特别有趣的，因为它将为致密等离子体中的新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，并将其应用于世界范围内的FEL设施的实验活动。我们将主要关注通过共振非弹性x射线散射（RIXS）提取HED系统的详细电子结构和激发谱的方法。通过使用RIXS来探索高度压缩系统中状态的时间分辨密度，我们将探索核-电子杂化是否确实在高密度下发生，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和重定位的性质，并研究等离子体中电离能的下降如何随密度的变化而变化。在计算方面，该项目将利用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模拟器和智能优化。这将确保我们能够充分利用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.