A Hybrid Neural Architecture: Online Attosecond X-ray Characterization
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918212854087680 |
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| author | Hirschman, Jack Mencer, Benjamin Obaid, Razib Shackelford, Amanda Coffee, Ryan |
| author_facet | Hirschman, Jack Mencer, Benjamin Obaid, Razib Shackelford, Amanda Coffee, Ryan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Hybrid Neural Architecture: Online Attosecond X-ray Characterization Hirschman, Jack Mencer, Benjamin Obaid, Razib Shackelford, Amanda Coffee, Ryan Applied Physics Accelerator Physics Data Analysis, Statistics and Probability 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. |
| title | A Hybrid Neural Architecture: Online Attosecond X-ray Characterization |
| topic | Applied Physics Accelerator Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2502.16141 |