Toward unsupervised single-shot diffractive imaging of heterogeneous particles using X-ray free-electron lasers

Area of Science:

• X-ray science
• Materials science
• Data science

Background:

• Single-shot diffraction imaging using X-ray free-electron lasers (XFELs) generates vast datasets.
• Current reconstruction methods require manual user guidance, creating significant data processing bottlenecks.

Purpose of the Study:

• To develop automated, unsupervised algorithms for reconstructing real-space contrasts from diffraction patterns.
• To overcome the limitations of user-guided reconstruction processes in XFEL experiments.

Main Methods:

• Implemented a series of algorithmic measures to replace manual user guidance in the reconstruction process.
• Developed unsupervised algorithms for contrast reconstruction from diffraction data.

Main Results:

• Successfully demonstrated the feasibility of automating the reconstruction process.
• Generated hundreds of real-space contrasts from soot particle diffraction experiments.

Conclusions:

• Automated, unsupervised reconstruction significantly enhances data processing efficiency for XFEL diffraction imaging.
• The developed methods pave the way for rapid, large-scale analysis of scattering objects.