A Metropolis Monte Carlo Algorithm for Merging Single-Particle Diffraction Intensities

TL;DR

This paper introduces a modified EMC algorithm that uses Metropolis Monte Carlo sampling to efficiently merge single-particle diffraction patterns with missing parameters, improving scalability for complex cases.

Contribution

The paper presents a novel EMC algorithm variant employing Metropolis Monte Carlo sampling, enabling efficient handling of higher-dimensional missing parameter spaces.

Findings

Monte Carlo-based EMC outperforms grid sampling in high-dimensional cases

The method effectively merges diffraction data with unknown orientations and other missing parameters

Simulated data tests show improved computational efficiency and scalability

Abstract

Single-particle imaging with X-ray free-electron lasers depends crucially on algorithms that merge large numbers of weak diffraction patterns despite missing measurements of parameters such as particle orientations. The Expand-Maximize-Compress (EMC) algorithm is highly effective at merging single-particle diffraction patterns with missing orientation values, but most implementations exhaustively sample the space of missing parameters and may become computationally prohibitive as the number of degrees of freedom extend beyond orientation angles. Here we describe how the EMC algorithm can be modified to employ Metropolis Monte Carlo sampling rather than grid sampling, which may be favorable for cases with more than three missing parameters. Using simulated data, this variant is compared to the standard EMC algorithm. Higher dimensional cases of mixed target species and variable x-ray fluence are also explored.