内部稳健性：系统检索SN Ia数据中的系统偏倚
Internal Robustness: systematic search for systematic bias in SN Ia data
摘要：
目前，人们正致力于理解、估计和消除宇宙学数据中的系统误差。在Ia型超新星的特殊情况下，系统学开始主导误差预算。在这里，我们提出了一个贝叶斯工具进行系统的搜索系统的污染。这是标准拟合优度检验的延伸，不仅可以交叉检查原始数据或处理后的数据是否存在系统分类，还可以确定最有可能受到污染的数据。我们成功地测试了我们的工具与模拟目录，并得出结论，Union2.1数据不具备大量的系统。最后，我们表明，如果一个包括在Union2.1的超新星，最初没有质量削减，我们的工具信号的存在超过3.8西格玛的置信水平的系统。
英文摘要：
A great deal of effort is currently being devoted to understanding, estimating and removing systematic errors in cosmological data. In the particular case of type Ia supernovae, systematics are starting to dominate the error budget. Here we propose a Bayesian tool for carrying out a systematic search for systematic contamination. This serves as an extension to the standard goodness-of-fit tests and allows not only to cross-check raw or processed data for the presence of systematics but also to pin-point the data that are most likely contaminated. We successfully test our tool with mock catalogues and conclude that the Union2.1 data do not possess a significant amount of systematics. Finally, we show that if one includes in Union2.1 the supernovae that originally failed the quality cuts, our tool signals the presence of systematics at over 3.8-sigma confidence level.
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