Deep-learning real-time phase retrieval of imperfect diffraction patterns from X-ray free-electron lasers
This provides a reliable solution for researchers in X-ray methodologies, enabling faster data processing for high-repetition-rate experiments, though it appears incremental as it builds on existing deep-learning approaches for phase retrieval.
The paper tackles the challenge of real-time phase retrieval from imperfect X-ray free-electron laser diffraction patterns by introducing a new deep-learning method, which achieves robust performance on simulated and weak-signal data while significantly reducing processing time to enable real-time reconstructions.
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.