Offset Finding of Beamline Parameters on the METRIXS Beamline at BESSY II Using Machine Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913951849119744 |
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| author | Meier, David Zeschke, Thomas Feuer-Forson, Peter Sick, Bernhard Viefhaus, Jens Hartmann, Gregor |
| author_facet | Meier, David Zeschke, Thomas Feuer-Forson, Peter Sick, Bernhard Viefhaus, Jens Hartmann, Gregor |
| contents | Beamline alignment is challenging as the beamline components must be set up ideally so that the rays follow the desired optical path. Automated methods using a digital twin allow for faster diagnostics and improved beam properties compared to manual tuning. We introduce an automated method of finding the offsets to improve this digital twin model. These offsets represent the unknown but constant differences between the beamline parameter positions as set up at the physical beamline and the corresponding parameter positions of its digital twin. Our method assumes the capability to execute precise relative movements with a known step size for these parameters, although the absolute position information is unknown. By combining the surrogate model with a global optimizer, we successfully determine offsets for 34 beamline parameters on a simulated METRIXS beamline at the BESSY II synchrotron radiation source in Berlin. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_17396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Offset Finding of Beamline Parameters on the METRIXS Beamline at BESSY II Using Machine Learning Meier, David Zeschke, Thomas Feuer-Forson, Peter Sick, Bernhard Viefhaus, Jens Hartmann, Gregor Accelerator Physics Data Analysis, Statistics and Probability Beamline alignment is challenging as the beamline components must be set up ideally so that the rays follow the desired optical path. Automated methods using a digital twin allow for faster diagnostics and improved beam properties compared to manual tuning. We introduce an automated method of finding the offsets to improve this digital twin model. These offsets represent the unknown but constant differences between the beamline parameter positions as set up at the physical beamline and the corresponding parameter positions of its digital twin. Our method assumes the capability to execute precise relative movements with a known step size for these parameters, although the absolute position information is unknown. By combining the surrogate model with a global optimizer, we successfully determine offsets for 34 beamline parameters on a simulated METRIXS beamline at the BESSY II synchrotron radiation source in Berlin. |
| title | Offset Finding of Beamline Parameters on the METRIXS Beamline at BESSY II Using Machine Learning |
| topic | Accelerator Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2503.17396 |