X-ray thermal diffuse scattering as a texture-robust temperature diagnostic for dynamically compressed solids
P. G. Heighway, D. J. Peake, T. Stevens, J. S. Wark, B. Albertazzi, S. J. Ali, L. Antonelli, M. R. Armstrong, C. Baehtz, O. B. Ball, S. Banerjee, A. B. Belonoshko, C. A. Bolme, V. Bouffetier, R. Briggs, K. Buakor, T. Butcher, S. Di Dio Cafiso, V. Cerantola, J. Chantel

TL;DR
This paper develops a texture-aware model of x-ray thermal diffuse scattering (TDS) for cubic polycrystals and demonstrates its robustness as a temperature diagnostic in dynamically compressed solids, regardless of sample texture or deformation.
Contribution
The paper introduces a novel TDS model accounting for texture effects and validates its accuracy against experimental data, showing improved temperature diagnostics in compressed solids.
Findings
Texture-aware TDS model outperforms conventional powder models.
TDS signal remains stable despite sample orientation and texture evolution.
TDS fluctuations are minimal compared to Bragg-peak fluctuations.
Abstract
We present a model of x-ray thermal diffuse scattering (TDS) from a cubic polycrystal with an arbitrary crystallographic texture, based on the classic approach of Warren. We compare the predictions of our model with femtosecond x-ray diffraction patterns obtained from ambient and dynamically compressed rolled copper foils obtained at the High Energy Density (HED) instrument of the European X-Ray Free-Electron Laser (EuXFEL), and find that the texture-aware TDS model yields more accurate results than does the conventional powder model owed to Warren. Nevertheless, we further show that: with sufficient angular detector coverage, the TDS signal is largely unchanged by sample orientation and in all cases strongly resembles the signal from a perfectly random powder; shot-to-shot fluctuations in the TDS signal resulting from grain-sampling statistics are at the percent level, in stark…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
