A Hybrid Neural Architecture: Online Attosecond X-ray Characterization

TL;DR

This paper introduces a hybrid neural network architecture for real-time, high-precision analysis of XFEL diagnostics, enabling sub-attosecond temporal resolution with ultra-low latency suitable for autonomous control in high-repetition-rate x-ray experiments.

Contribution

The paper presents a novel hybrid machine learning framework combining CNNs and LSTMs for fast, accurate XFEL diagnostics analysis with low latency and high throughput, scalable to 14 kHz.

Findings

Achieves over 10 kHz processing throughput.

Provides sub-30 attosecond temporal resolution.

Scalable to 14 kHz with FPGA implementation.

Abstract

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 \SI{168.3}{\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.