Towards a model-free interpretation of X-ray Thomson scattering signals


Towards a model-free interpretation of X-ray Thomson scattering signals

Dornheim, T.

Matter under extreme densities and temperatures is ubiquitous throughout our universe and naturally occurs in a plethora of astrophysical objects such as giant planet interiors and brown dwarfs. In addition, such warm dense matter (WDM) is of key importance for a number of technological applications, most notably inertial confinement fusion. Yet, the accurate diagnostics of experiments with WDM is rendered challenging by the extreme conditions. Indeed, even basic parameters such as the temperature often cannot be measured directly and have to be inferred from other observations. In this context, X-ray Thomson scattering (XRTS) [1] has emerged as a key diagnostic, but the interpretation of an XRTS signal is often based on de-facto uncontrolled approximations such as the decomposition into bound and free electrons within the popular Chihara model.

In this contribution, I outline how one can get direct access to the physical properties of interest by analyzing the measured signal in the imaginary-time domain [2]. No simulations/models and, therefore, no approximations are required. First and foremost, this allows us to infer the temperature of a given system with high accuracy [3]. Moreover, we can use XRTS to probe electron—electron correlations by utilizing the f-sum rule in the imaginary-time domain [4]. Finally, we show how the idea of imaginary-time correlation functions can be generalized to characterize the degree of nonequilibrium in the probed system [5], with important implications for equation-of-state measurements and the understanding of relaxation times.

[1] S. Glenzer and R. Redmer, Reviews of Modern Physics 81, 1625 (2009)

[2] T. Dornheim et al, arXiv:2209.02254 (submitted)

[3] T. Dornheim et al, Nature Communications 13, 7911 (2022)

[4] T. Dornheim et al, arXiv:2305.15305 (submitted)

[5] J. Vorberger et al, arXiv:2302.11309 (submitted)

  • Lecture (Conference)
    APS DPP, 30.10.-03.11.2023, Denver, USA

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