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Four large-area Sunyaev-Zeldovich (SZ) experiments -- APEX-SZ, SPT, ACT, 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. 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 SDSS stripe 82 galaxies with r<20, how the addition of GALEX photometry (FUV, NUV) 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 scatter of $sigma_z = 0.018 (1+z)$. If large spectroscopic training sets are not available, the addition of GALEX data makes possible the use simple maximum likelihood techniques, without resorting to Bayesian priors, and obtains $sigma_z=0.04(1+z)$, accuracy that approaches the accuracy 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 useful for the next generation of photo-z applications. We make our spectral templates and new photo-z catalogs available to the community at http://www.ice.csic.es/personal/jimenez/PHOTOZ .
Machine learning techniques, specifically the k-nearest neighbour algorithm applied to optical band colours, have had some success in predicting photometric redshifts of quasi-stellar objects (QSOs): Although the mean of differences between the spect
We extend the SDSS Stripe 82 Standard Stars Catalog with post-2007 SDSS imaging data. This improved version lists averaged SDSS ugriz photometry for nearly a million stars brighter than r~22 mag. With 2-3x more measurements per star, random errors ar
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We compare Stetsons photometric standards with measurements listed in a standard star catalog constructed using repeated SDSS imaging observations. The SDSS catalog includes over 700,000 candidate standard stars from the equatorial stripe 82 (|Dec|<1
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