A New Technique for Galaxy Photometric Redshifts in the Sloan Digital Sky Survey
arXiv:0707.3443 · doi:10.1086/529127
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.
24 pages, 10 figures, accepted to ApJ; added tables and references
References in corpus (3)
Cited by in corpus (7)
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- Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
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- Photometric Redshifts with Surface Brightness Priors
- SuperNova Acceleration Probe (SNAP): Investigating Photometric Redshift Optimization
- Improving Photometric Redshifts using GALEX Observations for the SDSS Stripe 82 and the Next Generation of SZ Cluster Surveys
- Photometric Redshift Estimation with Galaxy Morphology using Self-Organizing Maps