PS1-STRM: Neural network source classification and photometric redshift catalogue for PS1 3πDR1

Abstract

The Pan-STARRS1 (PS1) 3πsurvey 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 3πData 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 (|Δznorm|>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.