%0 Journal Article
%A Gao, Yunyun
%A Ginn, Helen
%A Thorn, Andrea
%T Robust and automatic beamstop shadow outlier rejection: combining crystallographic statistics with modern clustering under a semi-supervised learning strategy
%J Acta crystallographica / Section D
%V 80
%N 10
%@ 2059-7983
%C Bognor Regis
%I Wiley
%M PUBDB-2025-00076
%P 722-732
%D 2024
%X During the automatic processing of crystallographic diffraction experiments, beamstop shadows are often unaccounted for or only partially masked. As a result of this, outlier reflection intensities are integrated, which is a known issue. Traditional statistical diagnostics have only limited effectiveness in identifying these outliers, here termed Not-Excluded-unMasked-Outliers (NEMOs). The diagnostic tool AUSPEX allows visual inspection of NEMOs, where they form a typical pattern: clusters at the low-resolution end of the AUSPEX plots of intensities or amplitudes versus resolution. To automate NEMO detection, a new algorithm was developed by combining data statistics with a density-based clustering method. This approach demonstrates a promising performance in detecting NEMOs in merged data sets without disrupting existing data-reduction pipelines. Re-refinement results indicate that excluding the identified NEMOs can effectively enhance the quality of subsequent structure-determination steps. This method offers a prospective automated means to assess the efficacy of a beamstop mask, as well as highlighting the potential of modern pattern-recognition techniques for automating outlier exclusion during data processing, facilitating future adaptation to evolving experimental strategies.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:39361355
%U <Go to ISI:>//WOS:001329882300002
%R 10.1107/S2059798324008519
%U https://bib-pubdb1.desy.de/record/620200