2019
DOI: 10.1103/physreve.100.043207
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Ab initio simulations and measurements of the free-free opacity in aluminum

Abstract: The free-free opacity in dense systems is a property that both tests our fundamental understanding of correlated many-body systems, and is needed to understand the radiative properties of high energy-density plasmas. Despite its importance, predictive calculations of the free-free opacity remain challenging even in the condensed matter phase for simple metals. Here we show how the freefree opacity can be modelled at finite-temperatures via time-dependent density functional theory, and illustrate the importance… Show more

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Cited by 27 publications
(25 citation statements)
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References 65 publications
(90 reference statements)
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“…The first is electronic, due to thermal broadening of the plasmon peak [13], increases in the many-body screening length and a reduction in the electron degeneracy as the electrons heat [14]. The second is due to the change in the ion structure factor as the ions heat and the system melts [10,13]. The electron contribution is readily observed on femtosecond timescales in our data.…”
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confidence: 58%
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“…The first is electronic, due to thermal broadening of the plasmon peak [13], increases in the many-body screening length and a reduction in the electron degeneracy as the electrons heat [14]. The second is due to the change in the ion structure factor as the ions heat and the system melts [10,13]. The electron contribution is readily observed on femtosecond timescales in our data.…”
mentioning
confidence: 58%
“…3(c), with 1σ (68%) and 2σ (95%) confidence intervals. We also show the theoretical predictions from time-dependent DFT calculations based on the work of Hollebon et al for both an equilibrated system [10] and for a system with ions at 300 K, and from Iglesias [14]. Having understood how a single XUV pulse heats the sample, we now turn our attention to the pump-probe measurements.…”
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confidence: 92%
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“…The measured diffraction profile must be matched to the output from the diffraction modelling code, this requires optimising seven different parameters simultaneously, while allowing for the fact that these parameters are often not independent from each other. The parameters that the code can vary are the presample beam profile, sample width, sample position, XUV transmission and phase shift, source size 8 and sample edge smoothness 9 . It was determined that Bayesian optimisation would be a suitable method for doing so-this is a commonly used method for finding minima of highly non-linear functions that are time-consuming to evaluate.…”
Section: Discussionmentioning
confidence: 99%