Techniques for Reducing Catastrophic Outlier Redshift Estimates in Large-Scale Surveys
Abstract
We present results of using individual galaxies' effective redshift probability density information as a method of identifying potential catastrophic outliers in empirical photometric redshift estimation. We also present a method of modification of the redshift distribution of training sets to improve both the accuracy of high redshift estimation and catastrophic outlier mitigation. We find that with appropriate optimization, we can identify a large percentage of catastrophic outlier galaxies while simultaneously incorrectly flagging only a small percentage of non-outlier galaxies as catastrophic outliers. We show also that our training set redshift distribution modification results in a significant decrease in the percentage of high redshift outlier galaxies with only a small increase in the percentage of low redshift outlier galaxies, and in some cases results in a significant decrease in the percentage of incorrectly identified non-outlier galaxies.
- Publication:
-
American Astronomical Society Meeting Abstracts #235
- Pub Date:
- January 2020
- Bibcode:
- 2020AAS...23510922W