Outlier Prediction and Training Set Modification to Reduce Catastrophic Outlier Redshift Estimates in Large-scale Surveys

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...

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Bibliographic Details
Published in:Publications of the Astronomical Society of the Pacific 2021-04, Vol.133 (1022), p.44504
Main Authors: Wyatt, M., Singal, J.
Format: Article
Language:English
Subjects:
Galaxies
Mitigation
Optimization
Polls & surveys
Probability distribution
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Summary: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 ( 1.5 with only a small (
ISSN:0004-6280
1538-3873
DOI:10.1088/1538-3873/abe5fb