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

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.