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arXiv · 1512.03597

Learning algorithms at the service of WISE survey

Katarzyna Małek·Tomasz Krakowski·Maciej Bilicki·Agnieszka Pollo·Magdalena Krupa·Anieszka Kurcz·Aleksandra Solarz
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Abstract

We have undertaken a dedicated program of automatic source classification in the WISE database merged with SuperCOSMOS scans, comprehensively identifying galaxies, quasars and stars on most of the unconfused sky. We use the Support Vector Machines classifier for that purpose, trained on SDSS spectroscopic data. The classification has been applied to a photometric dataset based on all-sky WISE 3.4 and 4.6 $\mu$m information cross-matched with SuperCOSMOS B and R bands, which provides a reliable sample of $\sim170$ million sources, including galaxies at $z_{\rm med}\sim0.2$, as well as quasars and stars. The results of our classification method show very high purity and completeness (more than 96\%) of the separated sources, and the resultant catalogs can be used for sophisticated analyses, such as generating all-sky photometric redshifts.

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Katarzyna Małek, Tomasz Krakowski, Maciej Bilicki, Agnieszka Pollo, Magdalena Krupa, Anieszka Kurcz, Aleksandra Solarz. 2015-12-11. Learning algorithms at the service of WISE survey. https://arxiv.org/abs/1512.03597

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