基于机器学习导出的KiDS光度红移和PDF分布中异常值的拒绝准则
Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning
摘要：
概率密度函数（PDF）提供了测光红移（zphot）预测误差的估计。它对于当前和未来的天空调查至关重要，其特点是对zphot精度，可靠性和完整性有严格的要求。本工作的假设，正确定义的拒绝标准，能够识别和拒绝潜在的离群值，可以提高zphot估计和累积PDF的精度，而不会牺牲太多的样本的完整性。我们提供了一种方法来评估拒绝通过适当的削减PDF的形状描述符，如宽度和高度的最大PDF的峰值。在这项工作中，我们使用从Kilo Degree Survey（KiDS）ESO Data Release 4中提取的光度学测试了这些星系的拒绝标准，证明这种方法可以显著改善zphot质量：例如，对于在精度和完整性之间显示最佳折衷的剪切样本，相对于原始数据集，我们实现了$\simeq 75\%$的离群值分数的减少和NMAD的$\simeq 6\%$的改进，保留了其内容的$\simeq 93\%$。
英文摘要：
The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by strict requirements on the zphot precision, reliability and completeness. The present work stands on the assumption that properly defined rejection criteria, capable of identifying and rejecting potential outliers, can increase the precision of zphot estimates and of their cumulative PDF, without sacrificing much in terms of completeness of the sample. We provide a way to assess rejection through proper cuts on the shape descriptors of a PDF, such as the width and the height of the maximum PDF's peak. In this work we tested these rejection criteria to galaxies with photometry extracted from the Kilo Degree Survey (KiDS) ESO Data Release 4, proving that such approach could lead to significant improvements to the zphot quality: e.g., for the clipped sample showing the best trade-off between precision and completeness, we achieve a reduction in outliers fraction of $\simeq 75\%$ and an improvement of $\simeq 6\%$ for NMAD, with respect to the original data set, preserving the $\simeq 93\%$ of its content.
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