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 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.
