X-ray lens figure errors retrieved by deep learning from several beam intensity images
open access.
| Veröffentlicht in: | Journal of synchrotron radiation. - 1994. - 31(2024), Pt 5 vom: 01. Sept., Seite 1001-1009 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | Journal of synchrotron radiation |
| Schlagworte: | Journal Article compound refractive lens machine learning modeling neural network simulation |
| Zusammenfassung: | open access. The phase problem in the context of focusing synchrotron beams with X-ray lenses is addressed. The feasibility of retrieving the surface error of a lens system by using only the intensity of the propagated beam at several distances is demonstrated. A neural network, trained with a few thousand simulations using random errors, can predict accurately the lens error profile that accounts for all aberrations. It demonstrates the feasibility of routinely measuring the aberrations induced by an X-ray lens, or another optical system, using only a few intensity images |
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| Beschreibung: | Date Revised 05.09.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1600-5775 |
| DOI: | 10.1107/S1600577524004958 |