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.

中文翻译：

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鲁棒且自动的光束停止阴影异常值拒绝：在半监督学习策略下将晶体学统计与现代聚类相结合。


在晶体衍射实验的自动处理过程中，光束阻挡阴影常常无法解释或仅被部分掩盖。因此，异常反射强度被积分，这是一个已知问题。传统的统计诊断在识别这些异常值（此处称为未排除未屏蔽异常值（NEMO））方面效果有限。诊断工具 AUSPEX 允许对 NEMO 进行目视检查，它们形成典型的模式：在 AUSPEX 强度或振幅与分辨率图的低分辨率端形成簇。为了自动化 NEMO 检测，通过将数据统计与基于密度的聚类方法相结合，开发了一种新算法。这种方法在不破坏现有数据缩减管道的情况下检测合并数据集中的 NEMO 具有良好的性能。重新细化结果表明，排除已识别的 NEMO 可以有效提高后续结构确定步骤的质量。该方法提供了一种前瞻性的自动化方法来评估光束阻挡掩模的功效，并强调了现代模式识别技术在数据处理过程中自动排除异常值的潜力，促进未来适应不断发展的实验策略。