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Abstract

Redshift is an important parameter of galaxies, and the use of photometry data for redshift estimation has always been a focus in the field of astronomy. This study explores a redshift estimation method for SDSS photometric data based on the AutoGluon-Mix algorithm, in which the integrated learning methods, including K-nearest neighbors, random forests, XGBoosted trees, LightGBM boosted trees, CatBoost boosted trees, Extremely Randomized Trees, and neural networks, improve the accuracy of redshift estimation through comprehensive strategies. The experimental results show that AutoGluon-Mix performs extremely well compared to other algorithms for both early and late type galaxies.