http://arxiv.org/abs/2112.07811
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.
J. Singal, G. Silverman, E. Jones, et. al.
Thu, 16 Dec 21
82/83
Comments: 6 pages, 2 figures. Submitted, comments welcome
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