Paper | Title | Page |
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TUPOST046 | Machine Learning Applied for the Calibration of the Hard X-Ray Single-Shot Spectrometer at the European XFEL | 965 |
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Single-crystal monochromators are used in free electron lasers for hard x-ray self-seeding, selecting a very narrow spectral range of the original SASE signal for further amplification. When rotating the crystal around the roll and pitch axes, one can exploit several symmetric and asymmetric reflections as established by Bragg’s law. This work describes the implementation of a machine learning classifier to identify the crystal indices corresponding to a given reflection, and eventually calculate the difference between the photon energy as measured by a single-shot spectrometer and the actual one. The image processing techniques to extract the properties of the crystal reflection are described, as well as how this information is used to calibrate two spectrometer parameters. | ||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST046 | |
About • | Received ※ 07 June 2022 — Revised ※ 14 June 2022 — Accepted ※ 24 June 2022 — Issue date ※ 09 July 2022 | |
Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |