PS1-STRM: neural network source classification and photometric redshift catalogue for PS1 3π DR1
Oct 22, 201912 pages
Published in:
- Mon.Not.Roy.Astron.Soc. 500 (2020) 2, 1633-1644
- Published: Dec 2, 2020
e-Print:
- 1910.10167 [astro-ph.GA]
DOI:
- 10.1093/mnras/staa2587 (publication)
View in:
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Abstract: (Oxford University Press)
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 || for galaxies, || for stars, and || for quasars. Regarding the galaxy photo-z estimation, we attain an overall bias of 〈Δz_norm〉 = 0.0005, a standard deviation of σ(Δz_norm) = 0.0322, a median absolute deviation of MAD(Δz_norm) = 0.0161, and an outlier fraction of ||. The catalogue will be made available as a high-level science product via the Mikulski Archive for Space Telescopes.Note:
- 12 pages, 6 figures. Submitted to MNRAS
- methods: data analysis
- methods: numerical
- catalogues
- large-scale structure of Universe
References(76)
Figures(6)