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Cluster method for analysing surface X‐ray diffraction data sets using area detectors
Author(s) -
Leake Steven J.,
ReinleSchmitt Mathilde L.,
Kalichava Irakli,
Pauli Stephan A.,
Willmott Philip R.
Publication year - 2014
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s1600576713030203
Subject(s) - detector , diffraction , context (archaeology) , data reduction , computer science , cluster (spacecraft) , field (mathematics) , algorithm , reliability (semiconductor) , surface (topology) , process (computing) , optics , data mining , physics , mathematics , geometry , power (physics) , geography , archaeology , quantum mechanics , pure mathematics , programming language , operating system
An automated cluster algorithm is described, applicable to any image where a signal is to be analysed. The algorithm is employed in the context of surface X‐ray diffraction data and extended to automate the data reduction process, which at present limits both the lead time to and the reliability of the retrieved structural information. A detailed evaluation of the constraints used to automate surface X‐ray diffraction data analysis is provided. To overcome limitations of the algorithm and the experiment itself in certain geometries, the full field of view of area detectors is exploited to obtain orders of magnitude improvements in data collection. The method extends the surface X‐ray diffraction technique to new systems and highlights the often archaic approach to the analysis of data collected with a two‐dimensional detector.