CNN photometric redshifts in the SDSS at r20r\leq 20

Abstract

International audienceWe release photometric redshifts, reaching \sim0.7, for \sim14M galaxies at r20r\leq 20 in the 11,500 deg2^2 of the SDSS north and south galactic caps. These estimates were inferred from a convolution neural network (CNN) trained on ugrizugriz stamp images of galaxies labelled with a spectroscopic redshift from the SDSS, GAMA and BOSS surveys. Representative training sets of \sim370k galaxies were constructed from the much larger combined spectroscopic data to limit biases, particularly those arising from the over-representation of Luminous Red Galaxies. The CNN outputs a redshift classification that offers all the benefits of a well-behaved PDF, with a width efficiently signaling unreliable estimates due to poor photometry or stellar sources. The dispersion, mean bias and rate of catastrophic failures of the median point estimate are of order σMAD=0.014\sigma_{\rm MAD}=0.014, =0.0015=0.0015, η(Δznorm>0.05)=4%\eta(|\Delta z_{\rm norm}|>0.05)=4\% on a representative test sample at r<19.8r<19.8, out-performing currently published estimates. The distributions in narrow intervals of magnitudes of the redshifts inferred for the photometric sample are in good agreement with the results of tomographic analyses. The inferred redshifts also match the photometric redshifts of the redMaPPer galaxy clusters for the probable cluster members. The CNN input and output are available at: https://deepdip.iap.fr/treyer+2023

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    Last time updated on 01/11/2023