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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
