2025/02/22 by Hirschman, Jack, Mencer, Benjamin, Obaid, Razib +2
#Accelerator Physics (physics.acc-ph) #Applied Physics (physics.app-ph) #Data Analysis #FOS: Physical sciences #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.2502.16141
The emergence of high-repetition-rate x-ray free-electron lasers, such as SLAC's LCLS-II, serve as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the Deterministic Characterization with an Integrated Parallelizable Hybrid Resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10~kHz throughput with \SI168.3\micro\second inference latency, indicating scalability to 14~kHz with FPGA integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.