Leveraging Redshift Probabilities and Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates
Year: 2026
Type: article
Abstract: We present results of using a basic binary classification neural network model to identify likely catastrophic outlier (CO) photometric redshift estimates of individual galaxies. The classification is based on individual galaxy redshift probability distributions generated by a multiclass classifier neural network and a support vector machine. Importantly, the redshift probability distributions used as input to the binary classifier can be used for photometric redshift estimates generated by a different method. We find that a simple implementation of this classification can identify a very large fraction of galaxies with CO photometric redshift estimates while erroneously categorizing only a much smaller, in some cases negligible, fraction of nonoutliers. These methods can reduce the errors introduced into science analyses by CO photometric redshift estimates.
Source: The Astrophysical Journal
Authors: J. Yoo, C. Gyure, V. Agarwal, J. Singal, G. Silverman
Institution: University of Richmond
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,499
OpenAlex ID: W7128788756