Using different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation

Jonathan Soriano, Srinath Saikrishnan, Vikram Seenivasan +3

In this work, we explore methods to improve galaxy redshift predictions by combining different ground truths. Traditional machine learning models rely on training sets with known s…