Identifying and Repairing Catastrophic Errors in Galaxy Properties Using
  Dimensionality Reduction
  Our understanding of galaxy evolution is derived from large surveys designed
to maximize efficiency by only observing the minimum amount needed to infer
properties for a typical galaxy. However, for a few percent of galaxies in
every survey, these observations are insufficient and derived properties can be
catastrophically wrong. Further, it is currently difficult or impossible to
determine which objects have failed, so that these contaminate every study of
galaxy properties. We develop a novel method to identify these objects by
combining the astronomical codes which infer galaxy properties with the
dimensionality reduction algorithm t-SNE, which groups similar objects to
determine which inferred properties are out of place. This method provides an
improvement for the COSMOS catalog, which already uses existing techniques for
catastrophic error removal, and therefore should improve the quality of large
catalogs and any studies which are sensitive to large redshift errors.
