Nicolas G. Burdet | Searcharxiv

arXiv2023

Orbital-selective time-domain signature of nematicity dynamics in the charge-density-wave phase of La$_{1.65}$Eu$_{0.2}$Sr$_{0.15}$CuO$_4$

Understanding the interplay between charge, nematic, and structural ordering tendencies in cuprate superconductors is critical to unraveling their complex phase diagram. Using pump-probe time-resolved resonant x-ray scattering on the (0 0 1) Bragg peak at the Cu $L_3$ and O $K$ resonances, we investigate non-equilibrium dynamics of $Q_a = Q_b = 0$ nematic order and its association with both charge density wave (CDW) order and lattice dynamics in La$_{1.65}$Eu$_{0.2}$Sr$_{0.15}$CuO$_4$. The orbital selectivity of the resonant x-ray scattering cross-section allows nematicity dynamics associated with the planar O 2$p$ and Cu 3$d$ states to be distinguished from the response of anisotropic lattice distortions. A direct time-domain comparison of CDW translational-symmetry breaking and nematic rotational-symmetry breaking reveals that these broken symmetries remain closely linked in the photoexcited state, consistent with the stability of CDW topological defects in the investigated pump fluence regime.

cond-mat.str-el↗

arXiv2022

A machine learning photon detection algorithm for coherent X-ray ultrafast fluctuation analysis

X-ray free electron laser (XFEL) experiments have brought unique capabilities and opened new directions in research, such as creating new states of matter or directly measuring atomic motion. One such area is the ability to use finely spaced sets of coherent x-ray pulses to be compared after scattering from a dynamic system at different times. This enables the study of fluctuations in many-body quantum systems at the level of the ultrafast pulse durations, but this method has been limited to a select number of examples and required complex and advanced analytical tools. By applying a new methodology to this problem, we have made qualitative advances in three separate areas that will likely also find application to new fields. As compared to the `droplet-type' models which typically are used to estimate the photon distributions on pixelated detectors to obtain the coherent X-ray speckle patterns, our algorithm pipeline achieves an order of magnitude speedup on CPU hardware and two orders of magnitude improvement on GPU hardware. We also find that it retains accuracy in low-contrast conditions, which is the typical regime for many experiments in structural dynamics. Finally, it can predict photon distributions in high average-intensity applications, a regime which up until now, has not been accessible. Our AI-assisted algorithm will enable a wider adoption of x-ray coherence spectroscopies, by both automating previously challenging analyses and enabling new experiments that were not otherwise feasible without the developments described in this work.

physics.data-an↗