May 11 – 12, 2021
Zoom
Europe/Zurich timezone

ML-driven Reconstruction of X-ray Scattering Data

May 12, 2021, 3:50 PM
10m
Online (Zoom)

Online

Zoom

Speaker

Nico Hoffmann (HZDR)

Description

Recent developments in photon science enable the investigation of structures and fundamental dynamics at nanometer and femtosecond scales. The corresponding imaging techniques such as Small Angle X-Ray Scattering (SAXS) at Grazing Incidence (GI-SAXS) or Ptychography produce imaging data at unprecedented spatial and temporal resolution. However, the reconstruction of relevant properties from the acquired incomplete X-ray intensities of SAXS, GI-SAXS or Ptychography requires to solve an ill-posed inverse problem which is commonly approached by Iterative reconstruction schemes that are typically time-consuming and require manual tuning of hyperparameters. Additionally, imaging of non-equilibrium processes prone to perturbations due to e.g. non-planar wavefronts hamper the usage of these methods even further and emphasise the need for very fast & automatic feedback systems. In this talk, we are introducing novel data-driven approaches for fast and reliable reconstruction of X-ray scattering data. The approaches can be seen as a combination of traditional data-driven methods, Bayesian statistics and optimisation resulting in reliable means for very fast reconstruction of known structures as well as robust reconstruction methods of previously unknown structures.

Presentation materials