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Four large-area Sunyaev-Zeldovich (SZ) experiments -- APEX-SZ, SPT, ACT, andPlanck -- promise to detect clusters of galaxies through the distortion ofCosmic Microwave Background photons by hot (> 10^6 K) cluster gas (the SZeffect) over thousands of square degrees. A large observational follow-upeffort to obtain redshifts for these SZ-detected clusters is under way. Giventhe large area covered by these surveys, most of the redshifts will be obtainedvia the photometric redshift (photo-z) technique. Here we demonstrate, in anapplication using ~3000 SDSS stripe 82 galaxies with r<20, how the addition ofGALEX photometry (FUV, NUV) greatly improves the photometric redshifts ofgalaxies obtained with optical griz or ugriz photometry. In the case wherelarge spectroscopic training sets are available, empirical neural-network-basedtechniques (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 ofGALEX data makes possible the use simple maximum likelihood techniques, withoutresorting to Bayesian priors, and obtains $\sigma_z=0.04(1+z)$, accuracy thatapproaches the accuracy obtained using spectroscopic training of neuralnetworks on ugriz observations. This improvement is especially notable for bluegalaxies. To achieve these results, we have developed a new set of highresolution spectral templates based on physical information about the starformation history of galaxies. We envision these templates to be useful for thenext generation of photo-z applications. We make our spectral templates and newphoto-z catalogs available to the community athttp://www.ice.csic.es/personal/jimenez/PHOTOZ .

Improving Photometric Redshifts using GALEX Observations for the SDSS Stripe 82 and the Next Generation of SZ Cluster Surveys