Speaker
Description
Accurate, non-invasive diagnostics of laser-interacted plasma targets are essential for optimizing high-intensity laser experiments, including laser plasma acceleration (LPA). We present a novel approach based on computational X-ray holography to image targets such as hydrogen gas jets. This technique captures single-shot X-ray diffraction patterns in an in-line geometry, where phase contrast arises from the interaction of a divergent beam with the sample. These diffraction images are used to reconstruct spatial phase and, thereby, density distributions.
To solve the inverse problem from intensity-only measurements, we employ machine learning techniques capable of retrieving fine-scale features even in noisy, underdetermined conditions. As a proof of concept, we demonstrate reconstructions of laser-irradiated hydrogen jets, revealing hydrodynamic structure relevant for plasma tailoring and injection control. The approach is compatible with both external and compact plasma-based X-ray sources, enabling real-time, in-situ diagnostics in LPA platforms.
An additional application is proposed for inertial confinement fusion (ICF), where X-ray Free Electron Laser (XFEL)-based coherent diffraction imaging can image shock-compressed solid hydrogen. At the European XFEL’s HED-HIBEF instrument, this technique aims to resolve fuel compression from 20 μm down to sub-micron scales, addressing key challenges in high-gain ICF implosion diagnostics.
