用深度学习实现飞秒激光下不完美衍射图的实时相位重建
Deep-learning real-time phase retrieval of imperfect diffraction patterns from X-ray free-electron lasers
- 基于深度学习设计新方法，直接从不完整衍射数据恢复相位信息
- 在弱信号单脉冲数据上表现良好，处理速度显著提升
- 适合高重复率实验，可支持实时成像，适用于多种科研场景
机器学习正迅速渗透到几乎所有科学领域，通过分析海量数据并从不完整信息中提取科学知识。数据驱动型科学研究在X射线技术中尤为突出，先进光源与探测技术产生大量数据，远超人工逐帧检查能力。尽管需求日益增长，机器学习的广泛应用仍受限于对特定数据的调优需求。本研究提出一种新的深度学习相位恢复方法，适用于不完美的衍射数据。该方法在模拟数据上表现稳健，并成功应用于X射线自由电子激光器产生的弱信号单脉冲衍射数据。此外，该方法大幅缩短数据处理时间，实现关键的实时图像重建，满足高重复率数据采集需求。因此，该方案为相位问题提供了可靠解决方案，有望在多个研究领域广泛采用。
原文摘要 · Abstract (English)
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.
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