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We present the SWIRE Photometric Redshift Catalogue, 1025119 redshifts of unprecedented reliability and accuracy. Our method is based on fixed galaxy and QSO templates applied to data at 0.36-4.5 mu, and on a set of 4 infrared emission templates fitted to infrared excess data at 3.6-170 mu. The code involves two passes through the data, to try to optimize recognition of AGN dust tori. A few carefully justified priors are used and are the key to supression of outliers. Extinction, A_V, is allowed as a free parameter. We use a set of 5982 spectroscopic redshifts, taken from the literature and from our own spectroscopic surveys, to analyze the performance of our method as a function of the number of photometric bands used in the solution and the reduced chi^2. For 7 photometric bands the rms value of (z_{phot}-z_{spec})/(1+z_{spec}) is 3.5%, and the percentage of catastrophic outliers is ~1%. We discuss the redshift distributions at 3.6 and 24 mu. In individual fields, structure in the redshift distribution corresponds to clusters which can be seen in the spectroscopic redshift distribution. 10% of sources in the SWIRE photometric redshift catalogue have z >2, and 4% have z>3, so this catalogue is a huge resource for high redshift galaxies. A key parameter for understanding the evolutionary status of infrared galaxies is L_{ir}/L_{opt}, which can be interpreted as the specific star-formation rate for starbursts. For dust tori around Type 1 AGN, L_{tor}/L_{opt} is a measure of the torus covering factor and we deduce a mean covering factor of 40%.
We have revised the SWIRE Photometric Redshift Catalogue to take account of new optical photometry in several of the SWIRE areas, and incorporating 2MASS and UKIDSS near infrared data. Aperture matching is an important issue for combining near infrar
Accurate photometric redshifts are calculated for nearly 200,000 galaxies to a 4.5 micron flux limit of ~13 uJy in the 8.5 deg^2 Spitzer/IRAC Shallow survey. Using a hybrid photometric redshift algorithm incorporating both neural-net and template-fit
We present a robust method to estimate the redshift of galaxies using Pan-STARRS1 photometric data. Our method is an adaptation of the one proposed by Beck et al. (2016) for the SDSS Data Release 12. It uses a training set of 2313724 galaxies for whi
In this paper we present and characterize a nearest-neighbors color-matching photometric redshift estimator that features a direct relationship between the precision and accuracy of the input magnitudes and the output photometric redshifts. This aspe
We study the performance of the hybrid template-machine-learning photometric redshift (photo-$z$) algorithm Delight, which uses Gaussian processes, on a subset of the early data release of the Physics of the Accelerating Universe Survey (PAUS). We ca