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.
@article{arxiv.2112.07811,
title = {Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates},
author = {J. Singal and G. Silverman and E. Jones and T. Do and B. Boscoe and Y. Wan},
journal= {arXiv preprint arXiv:2112.07811},
year = {2022}
}
Comments
7 pages, 2 figure blocks. Updated to ApJ accepted version