Galaxy Zoo Morphology and Photometric Redshifts in the Sloan Digital Sky Survey

TL;DR

This paper demonstrates that combining galaxy morphology classifications from Galaxy Zoo with Sloan Digital Sky Survey photometry can significantly improve photometric redshift estimates, especially for elliptical galaxies.

Contribution

It introduces a method to enhance photometric redshift accuracy by integrating Galaxy Zoo morphology data with SDSS photometry using machine learning.

Findings

Root mean square error as low as 0.0118 for ellipticals

Morphology-based redshift estimation shows promising results

Potential to improve redshift estimates for all SDSS galaxies

Abstract

It has recently been demonstrated that one can accurately derive galaxy morphology from particular primary and secondary isophotal shape estimates in the Sloan Digital Sky Survey imaging catalog. This was accomplished by applying Machine Learning techniques to the Galaxy Zoo morphology catalog. Using the broad bandpass photometry of the Sloan Digital Sky Survey in combination with with precise knowledge of galaxy morphology should help in estimating more accurate photometric redshifts for galaxies. Using the Galaxy Zoo separation for spirals and ellipticals in combination with Sloan Digital Sky Survey photometry we attempt to calculate photometric redshifts. In the best case we find that the root mean square error for Luminous Red Galaxies classified as ellipticals is as low as 0.0118. Given these promising results we believe better photometric redshift estimates for all galaxies in the Sloan Digital Sky Survey ($\sim$350 million) will be feasible if researchers can also leverage their derived morphologies via Machine Learning. These initial results look to be promising for those interested in estimating Weak-Lensing, Baryonic Acoustic Oscillation, and other fields dependent upon accurate photometric redshifts.