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| Journal Article | PUBDB-2026-01341 |
; ; ; ;
2026
Springer
Heidelberg
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Please use a persistent id in citations: doi:10.1140/epjd/s10053-026-01163-6
Report No.: arXiv:2601.10898
Abstract: 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 two-dimensional 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 two-dimensional 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 good agreement with start-to-end simulation results. These results demonstrate the potential of neural-network surrogates to facilitate fast virtual diagnostics and provide a step toward real-time tuning in attosecond XFEL operation.
Keyword(s): Accelerator Physics (physics.acc-ph) ; FOS: Physical sciences
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U-Net-based surrogate modeling for attosecond X-ray free-electron lasers
(2026)
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