Tests of Catastrophic Outlier Prediction in Empirical Photometric Redshift Estimation with Redshift Probability Distributions




We present results of using individual galaxies' redshift probability information derived from a photometric redshift (photo-z) algorithm, SPIDERz, to identify potential catastrophic outliers in photometric redshift determinations. By using two test data sets comprised of COSMOS multi-band photometry spanning a wide redshift range (0 < z < 4) matched with reliable spectroscopic or other redshift determinations we explore the efficacy of a novel method to flag potential catastrophic outliers (those galaxies where ) in an analysis which relies on accurate photometric redshifts. SPIDERz is a custom support vector machine classification algorithm for photo-z analysis that naturally outputs a distribution of redshift probability information for each galaxy in addition to a discrete most probable photo-z value. By applying an analytic technique with flagging criteria to identify the presence of probability distribution features characteristic of catastrophic outlier photo-z estimates, such as multiple redshift probability peaks separated by substantial redshift distances, we can flag potential catastrophic outliers in photo-z determinations. We find that our proposed method can correctly flag large fractions (>50%) of the catastrophic outlier galaxies, while only flagging a small fraction (<5%) of the total non-outlier galaxies, depending on parameter choices. The fraction of non-outlier galaxies flagged varies significantly with redshift and magnitude, however. We examine the performance of this strategy in photo-z determinations using a range of flagging parameter values. These results could potentially be useful for utilization of photometric redshifts in future large-scale surveys where catastrophic outliers are particularly detrimental to the science goals.

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Copyright © 2020. The Astronomical Society of the Pacific. All rights reserved.
Publications of the Astronomical Society of the Pacific, Volume 132, Number 1008

DOI: https://doi.org/10.1088/1538-3873/ab54ed