Prediction on X-ray output of free electron laser based on artificial neural networks
Abstract Knowledge of x-ray free electron lasers’ (XFELs) pulse characteristics delivered to a sample is crucial for ensuring high-quality x-rays for scientific experiments. XFELs’ self-amplified spontaneous emission process causes spatial and spectral variations in x-ray pulses entering a sample, w...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-42573-z |
| _version_ | 1797630132579467264 |
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| author | Kenan Li Guanqun Zhou Yanwei Liu Juhao Wu Ming-fu Lin Xinxin Cheng Alberto A. Lutman Matthew Seaberg Howard Smith Pranav A. Kakhandiki Anne Sakdinawat |
| author_facet | Kenan Li Guanqun Zhou Yanwei Liu Juhao Wu Ming-fu Lin Xinxin Cheng Alberto A. Lutman Matthew Seaberg Howard Smith Pranav A. Kakhandiki Anne Sakdinawat |
| author_sort | Kenan Li |
| collection | DOAJ |
| description | Abstract Knowledge of x-ray free electron lasers’ (XFELs) pulse characteristics delivered to a sample is crucial for ensuring high-quality x-rays for scientific experiments. XFELs’ self-amplified spontaneous emission process causes spatial and spectral variations in x-ray pulses entering a sample, which leads to measurement uncertainties for experiments relying on multiple XFEL pulses. Accurate in-situ measurements of x-ray wavefront and energy spectrum incident upon a sample poses challenges. Here we address this by developing a virtual diagnostics framework using an artificial neural network (ANN) to predict x-ray photon beam properties from electron beam properties. We recorded XFEL electron parameters while adjusting the accelerator’s configurations and measured the resulting x-ray wavefront and energy spectrum shot-to-shot. Training the ANN with this data enables effective prediction of single-shot or average x-ray beam output based on XFEL undulator and electron parameters. This demonstrates the potential of utilizing ANNs for virtual diagnostics linking XFEL electron and photon beam properties. |
| first_indexed | 2024-03-11T11:03:42Z |
| format | Article |
| id | doaj.art-d2d6533216124825a96d2273d4a0d118 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-11T11:03:42Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-d2d6533216124825a96d2273d4a0d1182023-11-12T12:23:12ZengNature PortfolioNature Communications2041-17232023-11-011411910.1038/s41467-023-42573-zPrediction on X-ray output of free electron laser based on artificial neural networksKenan Li0Guanqun Zhou1Yanwei Liu2Juhao Wu3Ming-fu Lin4Xinxin Cheng5Alberto A. Lutman6Matthew Seaberg7Howard Smith8Pranav A. Kakhandiki9Anne Sakdinawat10SLAC National Accelerator LabSLAC National Accelerator LabSLAC National Accelerator LabSLAC National Accelerator LabSLAC National Accelerator LabSLAC National Accelerator LabSLAC National Accelerator LabSLAC National Accelerator LabSLAC National Accelerator LabSLAC National Accelerator LabSLAC National Accelerator LabAbstract Knowledge of x-ray free electron lasers’ (XFELs) pulse characteristics delivered to a sample is crucial for ensuring high-quality x-rays for scientific experiments. XFELs’ self-amplified spontaneous emission process causes spatial and spectral variations in x-ray pulses entering a sample, which leads to measurement uncertainties for experiments relying on multiple XFEL pulses. Accurate in-situ measurements of x-ray wavefront and energy spectrum incident upon a sample poses challenges. Here we address this by developing a virtual diagnostics framework using an artificial neural network (ANN) to predict x-ray photon beam properties from electron beam properties. We recorded XFEL electron parameters while adjusting the accelerator’s configurations and measured the resulting x-ray wavefront and energy spectrum shot-to-shot. Training the ANN with this data enables effective prediction of single-shot or average x-ray beam output based on XFEL undulator and electron parameters. This demonstrates the potential of utilizing ANNs for virtual diagnostics linking XFEL electron and photon beam properties.https://doi.org/10.1038/s41467-023-42573-z |
| spellingShingle | Kenan Li Guanqun Zhou Yanwei Liu Juhao Wu Ming-fu Lin Xinxin Cheng Alberto A. Lutman Matthew Seaberg Howard Smith Pranav A. Kakhandiki Anne Sakdinawat Prediction on X-ray output of free electron laser based on artificial neural networks Nature Communications |
| title | Prediction on X-ray output of free electron laser based on artificial neural networks |
| title_full | Prediction on X-ray output of free electron laser based on artificial neural networks |
| title_fullStr | Prediction on X-ray output of free electron laser based on artificial neural networks |
| title_full_unstemmed | Prediction on X-ray output of free electron laser based on artificial neural networks |
| title_short | Prediction on X-ray output of free electron laser based on artificial neural networks |
| title_sort | prediction on x ray output of free electron laser based on artificial neural networks |
| url | https://doi.org/10.1038/s41467-023-42573-z |
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