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Robust background modelling in DIALS
Author(s) -
Parkhurst James M.,
Winter Graeme,
Waterman David G.,
Fuentes-Montero Luis,
Gildea Richard J.,
Murshudov Garib N.,
Evans Gwyndaf
Publication year - 2016
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/s1600576716013595
Subject(s) - outlier , reflection (computer programming) , poisson distribution , pixel , computer science , anomaly detection , generalized linear model , algorithm , distribution (mathematics) , mathematics , artificial intelligence , statistics , mathematical analysis , programming language
A method for estimating the background under each reflection during integration that is robust in the presence of pixel outliers is presented. The method uses a generalized linear model approach that is more appropriate for use with Poisson distributed data than traditional approaches to pixel outlier handling in integration programs. The algorithm is most applicable to data with a very low background level where assumptions of a normal distribution are no longer valid as an approximation to the Poisson distribution. It is shown that traditional methods can result in the systematic underestimation of background values. This then results in the reflection intensities being overestimated and gives rise to a change in the overall distribution of reflection intensities in a dataset such that too few weak reflections appear to be recorded. Statistical tests performed during data reduction may mistakenly attribute this to merohedral twinning in the crystal. Application of the robust generalized linear model algorithm is shown to correct for this bias.