DOI
10.3847/1538-4357/ae317b
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.
Document Type
Article
Publication Date
2-13-2026
Publisher Statement
Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Recommended Citation
Yoo, J.; Gyure, C.; Agarwal, V.; Singal, Jack E.; and Silverman, G., "Leveraging Redshift Probabilities and Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates" (2026). Physics Faculty Publications. 179.
https://scholarship.richmond.edu/physics-faculty-publications/179
