  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 .
