4.5 Article

A generalized approach to x-ray data modeling for high-energy-density plasma experiments

Journal

REVIEW OF SCIENTIFIC INSTRUMENTS
Volume 94, Issue 5, Pages -

Publisher

AIP Publishing
DOI: 10.1063/5.0128811

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Accurate understanding of x-ray diagnostic modeling is crucial for interpreting high-energy-density experiments and testing simulations. Past individual treatments have hindered universal diagnostic understanding. A general formula for modeling non-focusing x-ray diagnostics has been derived, accounting naturally for crystal broadening. The new model is recommended for developing a unified picture and providing consistent treatment over multiple x-ray diagnostics.
Accurate understanding of x-ray diagnostics is crucial for both interpreting high-energy-density experiments and testing simulations through quantitative comparisons. X-ray diagnostic models are complex. Past treatments of individual x-ray diagnostics on a case-by-case basis have hindered universal diagnostic understanding. Here, we derive a general formula for modeling the absolute response of non-focusing x-ray diagnostics, such as x-ray imagers, one-dimensional space-resolved spectrometers, and x-ray power diagnostics. The present model is useful for both data modeling and data processing. It naturally accounts for the x-ray crystal broadening. The new model verifies that standard approaches for a crystal response can be good approximations, but they can underestimate the total reflectivity and overestimate spectral resolving power by more than a factor of 2 in some cases near reflectivity edge features. We also find that a frequently used, simplified-crystal-response approximation for processing spectral data can introduce an absolute error of more than an order of magnitude and the relative spectral radiance error of a factor of 3. The present model is derived with straightforward geometric arguments. It is more general and is recommended for developing a unified picture and providing consistent treatment over multiple x-ray diagnostics. Such consistency is crucial for reliable multi-objective data analyses.

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