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MegaZ-LRG: A photometric redshift catalogue of one million SDSS Luminous Red Galaxies
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We describe the construction of MegaZ-LRG, a photometric redshift catalogue of over one million luminous red galaxies (LRGs) in the redshift range 0.4 < z < 0.7 with limiting magnitude i < 20. The catalogue is selected from the imaging data of the Sloan Digital Sky Survey Data Release 4. The 2dF-SDSS LRG and Quasar (2SLAQ) spectroscopic redshift catalogue of 13,000 intermediate-redshift LRGs provides a photometric redshift training set, allowing use of ANNz, a neural network-based photometric-redshift estimator. The rms photometric redshift accuracy obtained for an evaluation set selected from the 2SLAQ sample is sigma_z = 0.049 averaged over all galaxies, and sigma_z = 0.040 for a brighter subsample (i < 19.0). The catalogue is expected to contain ~5 per cent stellar contamination. The ANNz code is used to compute a refined star/galaxy probability based on a range of photometric parameters; this allows the contamination fraction to be reduced to 2 per cent with negligible loss of genuine galaxies. The MegaZ-LRG catalogue is publicly available on the World Wide Web from http://www.2slaq.info .
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Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts
AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.
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