Publication Date:
2020-08-31
Description:
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 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 =0.0005, a standard deviation of σ(Δznorm) = 0.0322, a median absolute deviation of MAD(Δznorm) = 0.0161, and an outlier fraction of $Pleft(|Delta z_{mathrm{norm}}|〉0.15
ight)=1.89\%$. The catalogue will be made available as a high-level science product via the Mikulski Archive for Space Telescopes.
Print ISSN:
0035-8711
Electronic ISSN:
1365-2966
Topics:
Physics
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