Deep-learning real-time phase retrieval of imperfect diffraction patterns from X-ray free-electron lasers

TL;DR

This paper presents a deep-learning approach for real-time phase retrieval of imperfect diffraction patterns from X-ray free-electron lasers, enabling faster and more reliable analysis of large datasets in X-ray science.

Contribution

A novel deep-learning-based phase retrieval method that is robust to imperfect data and capable of real-time processing, improving upon previous techniques in speed and reliability.

Findings

Effective on simulated data and weak-signal XFEL data

Significantly reduces data processing time

Enables real-time image reconstruction

Abstract

Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data. Data-driven science is rapidly growing, especially in X-ray methodologies, where advanced light sources and detection technologies accumulate vast amounts of data that exceed meticulous human inspection capabilities. Despite the increasing demands, the full application of machine learning has been hindered by the need for data-specific optimizations. In this study, we introduce a new deep-learning-based phase retrieval method for imperfect diffraction data. This method provides robust phase retrieval for simulated data and performs well on weak-signal single-pulse diffraction data from X-ray free-electron lasers. Moreover, the method significantly reduces data processing time, facilitating real-time image reconstructions that are crucial for high-repetition-rate data acquisition. Thus, this approach offers a reliable solution to the phase problem and is expected to be widely adopted across various research areas.