大尺度观测中减少突变离群红移估计的离群预测和训练集修正
Outlier Prediction and Training Set Modification to Reduce Catastrophic Outlier Redshift Estimates in Large-Scale Surveys
摘要：
我们提出的结果，使用个别星系的概率分布的红移作为一种方法来识别潜在的灾难性的离群值的经验光度红移估计。在开发这种方法的过程中，我们开发了一种方法的训练集的红移分布的修改，以提高高红移（z>1.5）估计的基线精度以及灾难性的离群缓解。我们使用两个真实的测试数据集和一个模拟的测试数据集跨越了很宽的红移范围（0<z<4）来证明这些。这里介绍的结果通知一个例子“处方”，可以应用作为一个现实的光度红移估计的假设大规模的调查方案。我们发现，通过适当的优化，我们可以识别出相当大比例（>30%）的灾难性异常星系，同时只错误地将一小部分（<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，与未修改的训练集相比，我们的训练集红移分布修改导致z>1.5的离群星系显着（>10）个百分点的减少，z<1.5的离群星系只有一个小（<3）个百分点的增加。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地识别为异常值的星系显著减少（~20）个百分点，而在其他情况下只会导致该度量的小（<1）个百分点增加。
英文摘要：
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) percentage increase in this metric.
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