A New Technique for Galaxy Photometric Redshifts in the Sloan Digital Sky Survey

TL;DR

This paper presents a novel galaxy photometric redshift estimation method that combines multiple galaxy properties and statistical comparison to improve accuracy in SDSS data.

Contribution

The paper introduces a new technique incorporating colors, luminosity, surface brightness, and light profiles, enhancing redshift estimation over traditional color-only methods.

Findings

Achieved rms Delta-z of 0.025 for red galaxies

Achieved rms Delta-z of 0.030 for blue galaxies

Method shows promise for extension to higher redshifts

Abstract

Traditional photometric redshift methods use only color information about the objects in question to estimate their redshifts. This paper introduces a new method utilizing colors, luminosity, surface brightness, and radial light profile to measure the redshifts of galaxies in the Sloan Digital Sky Survey (SDSS). We take a statistical approach: distributions of galaxies from the SDSS Large-Scale Structure (LSS; spectroscopic) sample are constructed at a range of redshifts, and target galaxies are compared to these distributions. An adaptive mesh is implemented to increase the percentage of the parameter space populated by the LSS galaxies. We test the method on a subset of galaxies from the LSS sample, yielding rms Delta-z of 0.025 for red galaxies and 0.030 for blue galaxies (all with z < 0.25). Possible future improvements to this promising technique are described, as is our ongoing work to extend the method to galaxies at higher redshift.