femto-PIXAR: a neural network method for reconstruction of femtosecond X-ray free electron laser pulse energy
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909711899557888 |
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| author | Goetzke, Gesa Plumley, Rajan Hartmann, Gregor Maxwell, Tim Decker, Franz-Josef Lutman, Alberto Dunne, Mike Ratner, Daniel Turner, Joshua J. |
| author_facet | Goetzke, Gesa Plumley, Rajan Hartmann, Gregor Maxwell, Tim Decker, Franz-Josef Lutman, Alberto Dunne, Mike Ratner, Daniel Turner, Joshua J. |
| contents | X-ray Free Electron Lasers (X\nobreakdash-FELs) operate in a wide range of lasing configurations for a broad variety of scientific applications at ultrafast time-scales such as structural biology, materials science, and atomic and molecular physics. Shot-by-shot characterization of the X-FEL pulses is crucial for analysis of many experiments as well as tuning the X-FEL performance. However, for the weak pulses found in advanced configurations, e.g. those needed for coherent, two-pulse studies of quantum materials, there is no current method for reliably resolving pulse profiles. Here we show that a physics-based U-net model can reconstruct the individual pulse power profiles for sub-picosecond pulse separation without the need for simulations. Using experimental data from weak X-FEL pulse pairs, we demonstrate we can learn the pulse characteristics on a shot-by-shot basis when conventional methods fail. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16392 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | femto-PIXAR: a neural network method for reconstruction of femtosecond X-ray free electron laser pulse energy Goetzke, Gesa Plumley, Rajan Hartmann, Gregor Maxwell, Tim Decker, Franz-Josef Lutman, Alberto Dunne, Mike Ratner, Daniel Turner, Joshua J. Accelerator Physics Instrumentation and Detectors X-ray Free Electron Lasers (X\nobreakdash-FELs) operate in a wide range of lasing configurations for a broad variety of scientific applications at ultrafast time-scales such as structural biology, materials science, and atomic and molecular physics. Shot-by-shot characterization of the X-FEL pulses is crucial for analysis of many experiments as well as tuning the X-FEL performance. However, for the weak pulses found in advanced configurations, e.g. those needed for coherent, two-pulse studies of quantum materials, there is no current method for reliably resolving pulse profiles. Here we show that a physics-based U-net model can reconstruct the individual pulse power profiles for sub-picosecond pulse separation without the need for simulations. Using experimental data from weak X-FEL pulse pairs, we demonstrate we can learn the pulse characteristics on a shot-by-shot basis when conventional methods fail. |
| title | femto-PIXAR: a neural network method for reconstruction of femtosecond X-ray free electron laser pulse energy |
| topic | Accelerator Physics Instrumentation and Detectors |
| url | https://arxiv.org/abs/2502.16392 |