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Automatic classification of sources in large astronomical catalogs

Published online by Cambridge University Press:  10 June 2020

Agnieszka Pollo ,
Aleksandra Solarz ,
Małgorzata Siudek ,
Katarzyna Małek ,
Maciej Bilicki ,
Tomasz Krakowski ,
Tsutomu Takeuchi  and
the VIPERS team
Agnieszka Pollo
Affiliation:
Astronomical Observatory of the Jagiellonian University, ul. Orla 171, 30-001 Cracow, Poland email: agnieszka.pollo@gmail.com National Center for Nuclear Research,ul. A. Sołtana 7, 05-400 Otwock, Poland
Aleksandra Solarz
Affiliation:
National Center for Nuclear Research,ul. A. Sołtana 7, 05-400 Otwock, Poland
Małgorzata Siudek
Affiliation:
National Center for Nuclear Research,ul. A. Sołtana 7, 05-400 Otwock, Poland IFAE, The Barcelona Institute of Science and Technology, 08193 Bellaterra (Barcelona), Spain Center for Theoretical Physics, PAS, al. Lotników 32/46, 02-668, Warsaw, Poland
Katarzyna Małek
Affiliation:
National Center for Nuclear Research,ul. A. Sołtana 7, 05-400 Otwock, Poland Aix Marseille Univ. CNRS, CNES, LAM Marseille 13388, France
Maciej Bilicki
Affiliation:
Center for Theoretical Physics, PAS, al. Lotników 32/46, 02-668, Warsaw, Poland Leiden Observatory, Leiden University, P.O. Box 9513, NL-2300 RA Leiden, The Netherlands
Tomasz Krakowski
Affiliation:
National Center for Nuclear Research,ul. A. Sołtana 7, 05-400 Otwock, Poland
Tsutomu Takeuchi
Affiliation:
Nagoya University, Furo-Cho, Chikusa-ku, Nagoya 464-8602, Japan
the VIPERS team
Affiliation:
listed at the end of this proceedings
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Abstract

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In this paper we address two questions related to data analysis in large astronomical datasets, and we demonstrate how they can be answered making use of machine learning techniques. The first question is: how to efficiently find previously unknown or rare objects which can be expected to exist in big data samples? Using the largest existing extragalactic all-sky survey, provided by the WISE satellite, we demonstrate that, surprisingly, supervised classification methods can come to aid. The second question is: having a sufficiently large data sample, how can we look for new optimal classification schemes, possibly finding new and previously unknown classes and subclasses of sources? Based on the VIPERS cutting-edge galaxy catalog at redshift z > 0.5, we demonstrate that unsupervised classification methods can give unexpected but physically well-motivated results.

Keywords

surveysgalaxies: statisticsquasars: general
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 15 , Symposium S341: Challenges in Panchromatic Modelling with Next Generation Facilities , November 2019 , pp. 109 - 113
Copyright
© International Astronomical Union 2020

References

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