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ABSTRACT The Pan-STARRS1 (PS1) 3π survey is a comprehensive optical imaging survey of three quarters of the sky in the grizy broad-band photometric filters. We present the methodology used in assembling the source classification and photometric redshift (photo-z) catalogue for PS1 3π Data Release 1, titled Pan-STARRS1 Source Types and Redshifts with Machine learning (PS1-STRM). For both main data products, we use neural network architectures, trained on a compilation of public spectroscopic measurements that has been cross-matched with PS1 sources. We quantify the parameter space coverage of our training data set, and flag extrapolation using self-organizing maps. We perform a Monte Carlo sampling of the photometry to estimate photo-z uncertainty. The final catalogue contains 2902 054 648 objects. On our validation data set, for non-extrapolated sources, we achieve an overall classification accuracy of 98. 1{\ per\ cent} for galaxies, 97. 8{\ per\ cent} for stars, and 96. 6{\ per\ cent} for quasars. Regarding the galaxy photo-z estimation, we attain an overall bias of 〈Δznorm〉 = 0. 0005, a standard deviation of σ (Δznorm) = 0. 0322, a median absolute deviation of MAD (Δznorm) = 0. 0161, and an outlier fraction of P (| z₍₎ₑ₌| 0. 15) =1. 89{\ per\ cent}. The catalogue will be made available as a high-level science product via the Mikulski Archive for Space Telescopes.
Beck et al. (Wed,) studied this question.