PS1-STRM: Neural network source classification and photometric redshift catalogue for PS1 $3\pi$ DR1

Abstract

The Pan-STARRS1 (PS1) $3\pi$ 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 $3\pi$ 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 $2,902,054,648$ objects. On our validation data set, for non-extrapolated sources, we achieve an overall classification accuracy of $98.1\%$ for galaxies, $97.8\%$ for stars, and $96.6\%$ for quasars. Regarding the galaxy photo-z estimation, we attain an overall bias of $⟨\Delta z_{\mathrm{norm}}⟩=0.0005$, a standard deviation of $\sigma (\Delta z_{\mathrm{norm}})=0.0322$, a median absolute deviation of $\mathrm{MAD}(\Delta z_{\mathrm{norm}})=0.0161$, and an outlier fraction of $O=1.89\%$. The catalogue will be made available as a high-level science product via the Mikulski Archive for Space Telescopes at https://doi.org/10.17909//t9-rnk7-gr88.

12 pages, 6 figures. Submitted to MNRAS