Supervised Classification Methods for Flash X-ray single particle diffraction Imaging

TL;DR

This paper introduces two supervised template-based classification methods for FXI patterns that rapidly identify high-quality single-molecule diffraction images, aiding data reduction and sample heterogeneity management in XFEL experiments.

Contribution

The paper presents novel Eigen-Image and Log-Likelihood classifiers that quickly match diffraction patterns to templates, improving processing speed and efficiency in FXI data analysis.

Findings

Classifiers operate within a few milliseconds per pattern.

Methods are easily parallelizable to match XFEL repetition rates.

Enhances data quality control in FXI experiments.

Abstract

Current Flash X-ray single-particle diffraction Imaging (FXI) experiments, which operate on modern X-ray Free Electron Lasers (XFELs), can record millions of interpretable diffraction patterns from individual biomolecules per day. Due to the stochastic nature of the XFELs, those patterns will to a varying degree include scatterings from contaminated samples. Also, the heterogeneity of the sample biomolecules is unavoidable and complicates data processing. Reducing the data volumes and selecting high-quality single-molecule patterns are therefore critical steps in the experimental set-up. In this paper, we present two supervised template-based learning methods for classifying FXI patterns. Our Eigen-Image and Log-Likelihood classifier can find the best-matched template for a single-molecule pattern within a few milliseconds. It is also straightforward to parallelize them so as to fully match the XFEL repetition rate, thereby enabling processing at site.