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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Publications of the Astronomical Society of the Pacific, Volume 132, Number 1008
Jones, E., and J. Singal. “Tests of Catastrophic Outlier Prediction in Empirical Photometric Redshift Estimation with Redshift Probability Distributions.” Publications of the Astronomical Society of the Pacific 132, no. 1008 (February 2020): 024501. DOI: https://doi.org/10.1088/1538-3873/ab54ed.