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Morphology and Interaction of Galaxies using Deep Learning

Published online by Cambridge University Press:  30 May 2017

Fernando Caro ,
Marc Huertas-Company  and
Guillermo Cabrera
Fernando Caro
Affiliation:
GEPI, Observatoire de Paris, PSL Research University, CNRS, Université Paris Diderot, Sorbonne Paris Cité, 61 Avenue de l’Observatoire, 75014 Paris, France email: [email protected]
Marc Huertas-Company
Affiliation:
GEPI, Observatoire de Paris, PSL Research University, CNRS, Université Paris Diderot, Sorbonne Paris Cité, 61 Avenue de l’Observatoire, 75014 Paris, France email: [email protected]
Guillermo Cabrera
Affiliation:
Millenium Institute of Astrophysics, Santiago, Chile Center for Mathematical Modeling, Universidad de Chile, Beaucheff 851, Santiago, Chile
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Abstract

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In order to understand how galaxies form and evolve, the measurement of the parameters related to their morphologies and also to the way they interact is one of the most relevant requirements. Due to the huge amount of data that is generated by surveys, the morphological and interaction analysis of galaxies can no longer rely on visual inspection. For dealing with such issue, new approaches based on machine learning techniques have been proposed in the last years with the aim of automating the classification process. We tested Deep Learning using images of galaxies obtained from CANDELS to study the accuracy achieved by this tool considering two different frameworks. In the first, galaxies were classified in terms of their shapes considering five morphological categories, while in the second, the way in which galaxies interact was employed for defining other five categories. The results achieved in both cases are compared and discussed.

Keywords

methods: data analysisgalaxies: structuregalaxies: interactions
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 205 - 208
Copyright
Copyright © International Astronomical Union 2017 

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