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 clas...

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Published in:arXiv.org 2022-02
Main Authors: Singal, J, Silverman, G, Jones, E, T Do, Boscoe, B, Wan, Y
Format: Article
Language:English
Subjects:
Classification
Data analysis
Estimates
Galaxies
Machine learning
Neural networks
Outliers (statistics)
Photometry
Red shift
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creator Singal, J
Silverman, G
Jones, E
T Do
Boscoe, B
Wan, Y
description 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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subjects Classification
Data analysis
Estimates
Galaxies
Machine learning
Neural networks
Outliers (statistics)
Photometry
Red shift
title Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates
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