Improving Photometric Redshifts using Galaxy Evolution Explorer Observations for the Sloan Digital Sky Survey Stripe 82 and the Next Generation of Otical and Sunyaev-Zeldovich Cluster Surveys

• Published in: The Astrophysical Journal
• Main Authors: Niemack, Michael D., Jiménez, Raúl, Verde, Licia, Menanteau, Felipe, Panter, Ben, Spergel, David
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Astronomical Society 2009
• Subjects: Catalogues; Cosmology: observations; Galaxies: clusters: general; Galaxies: distances and redshifts; Galaxies: photometry; Methods: data analysis; Blanco; South Pole; Stripe; South pole
• Online Access: http://hdl.handle.net/10261/10792; https://doi.org/10.1088/0004-637X/690/1/89

13 pages, 8 figures.-- Online version published on Dec 1, 2008.-- ArXiv pre-print available at: http://arxiv.org/abs/0803.3221 Four large-area Sunyaev-Zeldovich (SZ) experiments —APEX-SZ, South Pole Telescope, Atacama Cosmology Telescope, and Planck— promise to detect clusters of galaxies through the distortion of cosmic microwave background photons by hot (> 10^6 K) cluster gas (the SZ effect) over thousands of square degrees. A large observational follow-up effort to obtain redshifts for these SZ-detected clusters is under way. In addition, photometric optical surveys such as the Blanco Cosmology Survey, Dark Energy Survey, Panoramic Survey Telescope and Rapid Response System, and Large Synoptic Survey Telescope will detect and attempt to recover redshifts for billions of field galaxies in pursuit of a diverse array of science objectives. Given the large area covered by these surveys, most of the redshifts will be obtained via the photometric redshift (photo-z) technique. Here we demonstrate, in an application using ~3000 Sloan Digital Sky Survey stripe 82 galaxies with r < 20, how the addition of Galaxy Evolution Explorer (GALEX) photometry (F(UV), N(UV)) greatly improves the photometric redshifts of galaxies obtained with optical griz or ugriz photometry. In the case where large spectroscopic training sets are available, empirical neural-network-based techniques (e.g., ANNz) can yield a photo-z error of σz = 0.018(1 + z). If large spectroscopic training sets are not available, the addition of GALEX data makes the use of simple maximum-likelihood techniques possible, without resorting to Bayesian priors, and obtains σz = 0.04(1 + z), an accuracy that approaches that obtained using spectroscopic training of neural networks on ugriz observations. This improvement is especially notable for blue galaxies. To achieve these results, we have developed a new set of high-resolution spectral templates based on physical information about the star-formation history of galaxies. We envision these templates to be ...