TY  - JOUR
AU  - Gao, Yunyun
AU  - Ginn, Helen
AU  - Thorn, Andrea
TI  - Robust and automatic beamstop shadow outlier rejection: combining crystallographic statistics with modern clustering under a semi-supervised learning strategy
JO  - Acta crystallographica / Section D
VL  - 80
IS  - 10
SN  - 2059-7983
CY  - Bognor Regis
PB  - Wiley
M1  - PUBDB-2025-00076
SP  - 722-732
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:39361355
UR  - <Go to ISI:>//WOS:001329882300002
DO  - DOI:10.1107/S2059798324008519
UR  - https://bib-pubdb1.desy.de/record/620200
ER  -