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arXiv · 2112.07811

Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates

J. Singal·G. Silverman·E. Jones·T. Do·B. Boscoe·Y. Wan
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Abstract

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.

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J. Singal, G. Silverman, E. Jones, T. Do, B. Boscoe, Y. Wan. 2021-12-15. Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates. https://doi.org/10.3847/1538-4357%2Fac53b5

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