Domagala Slawomir, Nourd Petrick, Diederichs Kay, Henn Julian
DataQ Intelligence, Fichtelgebirgsstrasse 66, 95448 Bayreuth, Germany.
Codivert, Żubrowa 15, 01-978 Warsaw, Poland.
J Appl Crystallogr. 2023 Jul 25;56(Pt 4):1200-1220. doi: 10.1107/S1600576723004764. eCollection 2023 Aug 1.
Contamination with low-energy radiation leads to an increased number of weighted residuals being larger in absolute terms than three standard uncertainties. For a Gaussian distribution, these rare events occur only in 0.27% of all cases, which is a small number for small- to medium-sized data sets. The correct detection of rare events - and an adequate correction procedure - thus relies crucially on correct standard uncertainties, which are often not available [Henn (2019), , 83-156]. It is therefore advisable to use additional, more robust, metrics to complement the established ones. These metrics are developed here and applied to reference data sets from two different publications about low-energy contamination. Other systematic errors were found in the reference data sets. These errors compromise the correction procedures and may lead to under- or overcompensation. This can be demonstrated clearly with the new metrics. Empirical correction procedures generally may be compromised or bound to fail in the presence of other systematic errors. The following systematic errors, which were found in the reference data sets, need to be corrected for prior to application of the low-energy contamination correction procedure: signals of 2λ contamination, extinction, disorder, twinning, and too-large or too-low standard uncertainties (this list may not be complete). All five reference data sets of one publication show a common resolution-dependent systematic error of unknown origin. How this affects the correction procedure can be stated only after elimination of this error. The methodological improvements are verified with data published by other authors.
低能辐射污染会导致加权残差数量增加，其中绝对值大于三个标准不确定度的残差数量增多。对于高斯分布而言，这些罕见事件仅在所有情况的0.27%中出现，对于中小型数据集来说，这是个小数目。因此，对罕见事件的正确检测以及适当的校正程序，关键依赖于正确的标准不确定度，但这些不确定度往往难以获取[亨恩（2019年），第83 - 156页]。所以，明智的做法是使用额外的、更稳健的指标来补充已有的指标。本文开发了这些指标，并将其应用于来自两篇关于低能污染的不同出版物的参考数据集。在参考数据集中还发现了其他系统误差。这些误差会影响校正程序，可能导致补偿不足或过度补偿。这可以通过新指标清楚地证明。在存在其他系统误差的情况下，经验校正程序通常可能会受到影响或注定失败。在应用低能污染校正程序之前，需要对参考数据集中发现的以下系统误差进行校正：2λ污染信号、消光、无序、孪晶以及过大或过小的标准不确定度（此列表可能不完整）。一篇出版物的所有五个参考数据集都显示出一个来源不明的与分辨率相关的共同系统误差。只有消除此误差后，才能说明这对校正程序有何影响。本文通过其他作者发表的数据验证了方法上的改进。
