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Future radio surveys will generate catalogues of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, spectroscopic data will be available for only a small fraction of these sources, and in most cases even the optical and infrared photometry will be of limited quality. Furthermore, radio sources tend to be at higher redshift than most optical sources and so a significant fraction of radio sources hosts differ from those for which most photometric redshift templates are designed. We therefore need to develop new techniques for estimating the redshifts of radio sources. As a starting point in this process, we evaluate a number of machine-learning techniques for estimating redshift, together with a conventional template-fitting technique. We pay special attention to how the performance is affected by the incompleteness of the training sample and by sparseness of the parameter space or by limited availability of ancillary multi-wavelength data. As expected, we find that the quality of the photometric-redshift degrades as the quality of the photometry decreases, but that even with the limited quality of photometry available for all sky-surveys, useful redshift information is available for the majority of sources, particularly at low redshift. We find that a template-fitting technique performs best with high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately. When we reduced the quality of photometry to match that available for the EMU all-sky radio survey, the quality of the template-fitting degraded and became comparable to some of the machine learning methods. Machine learning techniques currently perform better at low redshift than at high redshift, because of incompleteness of the currently available training data at high redshifts., Comment: Submitted to PASP

The SIRTF Wide-area InfraRed Extragalactic survey (SWIRE), the largest SIRTF Legacy program, is a wide-area, imaging survey to trace the evolution of dusty, star-forming galaxies, evolved stellar populations, and AGN as a function of environment, from redshifts z~3 to the current epoch. SWIRE will survey 7 high-latitude fields, totaling 60 - 65 sq. deg. in all 7 SIRTF bands: IRAC 3.6, 4.5, 5.6, 8 microns and MIPS 24, 70, 160 microns. The Legacy Extragalactic Catalog may contain in excess of 2 million IR-selected galaxies, dominated by (1) ~150,000 luminous infrared galaxies (LIRGs: L{FIR}>10^11 L_sun), ~7000 of these with z>2; (2) 1 million early-type galaxies, ~10,000 with z>2; and (3) \~20,000 classical AGN, plus significantly more dust-obscured QSO/AGN among the LIRGs. SWIRE will provide an unprecedented view of the evolution of galaxies, structure, and AGN. The key scientific goals of SWIRE are: (1) to determine the evolution of actively star-forming and passively evolving galaxies in order to understand the history of galaxy formation in the context of cosmic structure formation; (2) to determine the evolution of the spatial distribution and clustering of evolved galaxies, starbursts and AGN in the key redshift range, 0.5, Comment: 65 pages, 11 figures, accepted for publication in the P.A.S.P. Aug. 2003