U-Net-based surrogate modeling for attosecond X-ray free-electron lasers
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910067009257472 |
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| author | Wei, Yufei Yan, Bingyang Xu, Chenzhi Yan, Jiawei Deng, Haixiao |
| author_facet | Wei, Yufei Yan, Bingyang Xu, Chenzhi Yan, Jiawei Deng, Haixiao |
| contents | Attosecond X-ray pulse generation in modern X-ray free-electron lasers relies on strongly compressed, precisely tailored electron bunches, making accurate diagnostics and control of the longitudinal phase space (LPS) essential. In the self-chirping scheme, collective effects in the linac generate a strong energy chirp that is converted into high peak current through pre-undulator compression, enabling isolated attosecond pulse generation. Reliable operation of this scheme depends on precise LPS control and fast diagnostics. In this work, we present a U-Net-based neural network surrogate that predicts two-dimensional LPS distributions directly from accelerator settings. The model exhibits excellent agreement with start-to-end simulation results. These results demonstrate the potential of neural network surrogates to facilitate real-time tuning and control in attosecond X-ray pulse generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10898 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | U-Net-based surrogate modeling for attosecond X-ray free-electron lasers Wei, Yufei Yan, Bingyang Xu, Chenzhi Yan, Jiawei Deng, Haixiao Accelerator Physics Attosecond X-ray pulse generation in modern X-ray free-electron lasers relies on strongly compressed, precisely tailored electron bunches, making accurate diagnostics and control of the longitudinal phase space (LPS) essential. In the self-chirping scheme, collective effects in the linac generate a strong energy chirp that is converted into high peak current through pre-undulator compression, enabling isolated attosecond pulse generation. Reliable operation of this scheme depends on precise LPS control and fast diagnostics. In this work, we present a U-Net-based neural network surrogate that predicts two-dimensional LPS distributions directly from accelerator settings. The model exhibits excellent agreement with start-to-end simulation results. These results demonstrate the potential of neural network surrogates to facilitate real-time tuning and control in attosecond X-ray pulse generation. |
| title | U-Net-based surrogate modeling for attosecond X-ray free-electron lasers |
| topic | Accelerator Physics |
| url | https://arxiv.org/abs/2601.10898 |