We present results of using individual galaxies’ probability distribution over redshift as a method of identifying potential catastrophic outliers in empirical photometric redshift estimation. In the course of developing this approach we develop a method of modification of the redshift distribution of training sets to improve both the baseline accuracy of high redshift (z > 1.5) estimation as well as catastrophic outlier mitigation. We demonstrate these using two real test data sets and one simulated test data set spanning a wide redshift range (0 < z < 4). Results presented here inform an example “prescription” that can be applied as a realistic photometric redshift estimation scenario for a hypothetical large-scale survey. We find that with appropriate optimization, we can identify a significant percentage (>30%) of catastrophic outlier galaxies while simultaneously incorrectly flagging only a small percentage (<7% and in many cases <3%) of non-outlier galaxies as catastrophic outliers. We find also that our training set redshift distribution modification results in a significant (>10) percentage point decrease of outlier galaxies for z > 1.5 with only a small (<3) percentage point increase of outlier galaxies for z < 1.5 compared to the unmodified training set. In addition, we find that this modification can in some cases cause a significant (∼20) percentage point decrease of galaxies which are non-outliers but which have been incorrectly identified as outliers, while in other cases cause only a small (<1) increase in this metric.

我们展示了使用单个星系在红移上的概率分布作为在经验光度红移估计中识别潜在灾难性异常值的方法的结果。在开发这种方法的过程中，我们开发了一种修改训练集红移分布的方法，以提高高红移 ( z > 1.5) 估计的基线精度以及灾难性异常值缓解。我们使用两个真实的测试数据集和一个跨越宽红移范围 (0 < z< 4）。此处呈现的结果提供了一个示例“处方”，可用作假设的大规模调查的真实光度红移估计场景。我们发现，通过适当的优化，我们可以识别出很大比例（>30%）的灾难性异常星系，同时错误地将一小部分（<7%，在许多情况下<3%）的非异常星系标记为灾难性异常星系. 我们还发现，我们的训练集红移分布修改导致异常星系的显著（> 10）个百分点下降为Z ^ > 1.5，只有一小（<3％）点离群星系的增加ž< 1.5 与未修改的训练集相比。此外，我们发现这种修改在某些情况下会导致非异常值但被错误识别为异常值的星系显着减少（~20）个百分点，而在其他情况下仅导致很小（<1）这个指标的增加。