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Instrument-model refinement in normalized reciprocal-vector space for X-ray Laue diffraction.


ABSTRACT: A simple yet efficient instrument-model refinement method for X-ray diffraction data is presented and discussed. The method is based on least-squares minimization of differences between respective normalized (i.e. unit length) reciprocal vectors computed for adjacent frames. The approach was primarily designed to work with synchrotron X-ray Laue diffraction data collected for small-molecule single-crystal samples. The method has been shown to work well on both simulated and experimental data. Tests performed on simulated data sets for small-molecule and protein crystals confirmed the validity of the proposed instrument-model refinement approach. Finally, examination of data sets collected at both BioCARS 14-ID-B (Advanced Photon Source) and ID09 (European Synchrotron Radiation Facility) beamlines indicated that the approach is capable of retrieving goniometer parameters (e.g. detector distance or primary X-ray beam centre) reliably, even when their initial estimates are rather inaccurate.

SUBMITTER: Kaminski R 

PROVIDER: S-EPMC7534537 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Instrument-model refinement in normalized reciprocal-vector space for X-ray Laue diffraction.

Kamiński Radosław R   Szarejko Dariusz D   Pedersen Martin N MN   Hatcher Lauren E LE   Łaski Piotr P   Raithby Paul R PR   Wulff Michael M   Jarzembska Katarzyna N KN  

Journal of applied crystallography 20200929 Pt 5


A simple yet efficient instrument-model refinement method for X-ray diffraction data is presented and discussed. The method is based on least-squares minimization of differences between respective normalized (<i>i.e.</i> unit length) reciprocal vectors computed for adjacent frames. The approach was primarily designed to work with synchrotron X-ray Laue diffraction data collected for small-molecule single-crystal samples. The method has been shown to work well on both simulated and experimental d  ...[more]

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