Identifying catastrophic outlier photometric redshift estimates in the COSMOS field with machine learning methods

Abstract

We present the result of two binary classifier ensembled neural networks to identify catastrophic outliers for photo-z estimates within the COSMOS field utilizing only 8 and 5 photometric band passes, respectively. Our neural networks can correctly classify 55.6% and 33.3% of the true positives with few to no false positives. These methods can be used to reduce the errors caused by the errors in redshift estimates, particularly at high redshift. When applied to a larger data set with only photometric data available, our 8 band pass network increased the number of objects with a photo-z greater than 5 from 0.1% to 1.6%, and our 5 band pass network increased the number of objects with a photo-z greater than 5 from 0.2% to 1.8%.