论文标题
基于儿童光度红移和机器学习得出的PDF分布的拒绝标准
Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning
论文作者
论文摘要
概率密度函数（PDF）提供了对光度红移（ZPHOT）预测误差的估计。这对于当前和未来的天空调查至关重要，其特征是对ZPHOT精度，可靠性和完整性的严格要求。目前的工作基于这样的假设，即能够识别和拒绝潜在异常值的正确定义的排斥标准可以提高ZPHOT估计值及其累积PDF的精度，而无需在样本的完整性方面牺牲很多。我们提供了一种通过对PDF的形状描述符的适当剪切来评估排斥的方法，例如最大PDF峰的宽度和高度。 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关于原始数据集，保留了其内容的$ \ simeq 93 \％$。
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.
