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We present results of using individual galaxies' redshift probabilityinformation derived from a photometric redshift (photo-z) algorithm, SPIDERz,to identify potential catastrophic outliers in photometric redshiftdeterminations. By using two test data sets comprised of COSMOS multi-bandphotometry spanning a wide redshift range (0<z<4) matched with reliablespectroscopic or other redshift determinations we explore the efficacy of anovel method to flag potential catastrophic outliers in an analysis whichrelies on accurate photometric redshifts. SPIDERz is a custom support vectormachine classification algorithm for photo-z analysis that naturally outputs adistribution of redshift probability information for each galaxy in addition toa discrete most probable photo-z value. By applying an analytic technique withflagging criteria to identify the presence of probability distribution featurescharacteristic of catastrophic outlier photo-z estimates, such as multipleredshift probability peaks separated by substantial redshift distances, we canflag potential catastrophic outliers in photo-z determinations. We find thatour proposed method can correctly flag large fractions (>50%) of thecatastrophic outlier galaxies, while only flagging a small fraction (<5%) ofthe total non-outlier galaxies, depending on parameter choices. The fraction ofnon-outlier galaxies flagged varies significantly with redshift and magnitude,however. We examine the performance of this strategy in photo-z determinationsusing a range of flagging parameter values. These results could potentially beuseful for utilization of photometric redshifts in future large scale surveyswhere catastrophic outliers are particularly detrimental to the science goals.

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