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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 infrared and optical data, and we have explored a number of methods of doing this. The increased number of photometric bands available for the redshift solution results in improvements both in the rms error and, especially, in the outlier rate. We have also found that incorporating the dust torus emission into the QSO templates improves the performance for QSO redshift estimation. Our revised redshift catalogue contains over 1 million extragalactic objects, of which 26288 are QSOs.
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 fitt
Precision photometric redshifts will be essential for extracting cosmological parameters from the next generation of wide-area imaging surveys. In this paper we introduce a photometric redshift algorithm, ArborZ, based on the machine-learning techniq
Forming a three dimensional view of the Universe is a long-standing goal of astronomical observations, and one that becomes increasingly difficult at high redshift. In this paper we discuss how tomography of the intergalactic medium (IGM) at $zsimeq
Upcoming imaging surveys, such as LSST, will provide an unprecedented view of the Universe, but with limited resolution along the line-of-sight. Common ways to increase resolution in the third dimension, and reduce misclassifications, include observi
Machine learning (ML) is a standard approach for estimating the redshifts of galaxies when only photometric information is available. ML photo-z solutions have traditionally ignored the morphological information available in galaxy images or partly i