Machine Learning Classification to Identify Catastrophic Outlier
  Photometric Redshift Estimates
  We present results of using a basic binary classification neural network
model to identify likely catastrophic outlier photometric redshift estimates of
individual galaxies, based only on the galaxies' measured photometric band
magnitude values. We find that a simple implementation of this classification
can identify a significant fraction of galaxies with catastrophic outlier
photometric redshift estimates while falsely categorizing only a much smaller
fraction of non-outliers. These methods have the potential to reduce the errors
introduced into science analyses by catastrophic outlier photometric redshift
estimates.
