X-ray Thomson scattering (XRTS) diagnostics

While XRTS has emerged as a standard method of diagnostics for experiments with warm dense matter, the accuracy of the extracted system parameters such as the temperature has remained unclear.

The XRTS approach has emerged as a key method of diagnostics for matter under extreme temperatures, densities and pressures. While in principle giving one insights into the microphysics of the probed sample, the interpretation of the measured intensity has generally relied on a number of model assumptions. In our group, we develop new concepts for the model-free interpretation of XRTS measurements such as the recent imaginary-time thermometry method that allows us to directly extract the temperature from the experimental data. Moreover, we use both PIMC and DFT simulations to predict XRTS signals, thereby guiding the development of new experimental set-ups.

While warm dense matter is routinely realized in the laboratory using different experimental techniques, the extreme conditions and short time-scales render the rigorous diagnostics of these states a formidable challenge. Over the last years, the X-ray Thomson scattering (XRTS) approach has emerged as a key technique. In principle, it gives one access to the dynamic structure factor of the probed system, which contains a wealth of information about its microphysics. Of particular importance is the determination of system parameters such as the temperature, density, and ionization state, which are important elements of the equation-of-state of the probed material.

In the past, the interpretation of XRTS measurements was generally based on a number of uncontrolled model assumptions such as the decomposition of the total population of electrons into effectively bound and free states. Such chemical models are always based on approximate models for the individual terms, and are expected to break down alltogether at high densities where even the electronic orbitals of supposedly bound electrons begin to overlap. This unsatisfactory situation can be understood as a classical chicken and egg problem: on the one hand, we need theoretical models to interpret experimental data. On the other hand, the theoretical models have been de-facto uncontrolled and require experimental observations as a benchmark.

To break this vicious cycle, we develop novel concepts in our group for the model-free diagnostics of XRTS measurements in the imaginary-time domain. As a first application, we have shown that it is possible to directly extract the temperature of a given system from the XRTS signal without the need for models and approximations. The model-free inference of other properties of the system is the subject of intense ongoing work. These efforts are complemented by the development of a more rigorous theoretical description of XRTS experiments based on ab initio path integral Monte Carlo (PIMC) and time-dependent density functional theory (TD-DFT) simulations.