1. Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks
- Author
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Lukas Burget, C. Garrel, Marguerite Pierre, Nicolas Clerc, Norbert Werner, Matej Kosiba, Alina Khalikova, Sarah Kendrew, Miriam E. Ramos-Ceja, Filip Hroch, Mona Molham, M. Lieu, Bruno Altieri, Elias Koulouridis, T. Sadibekova, L. Faccioli, I. Valtchanov, Evelina R. Gaynullina, Faculty of Science [Brno] (SCI / MUNI), Masaryk University [Brno] (MUNI), European Space Astronomy Centre (ESAC), European Space Agency (ESA), Centre for Astronomy and Particle Theory, University of Nottingham, Institut de recherche en astrophysique et planétologie (IRAP), Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Astrophysique Interprétation Modélisation (AIM (UMR_7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Space Telescope Science Institute (STSci), Telespazio, Services par satellites, Ulugh Beg Astronomical Institute, Uzbekistan Academy of Sciences, Eötvös Loránd University (ELTE), Hiroshima University, Faculty of Information Technology [Brno] (FIT / BUT), Brno University of Technology [Brno] (BUT), National Observatory of Athens (NOA), National Research Institute of Astronomy and Geophysics [Helwan] (NRIAG), Max Planck Institute for Extraterrestrial Physics (MPE), Max-Planck-Gesellschaft, Agence Spatiale Européenne = European Space Agency (ESA), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)
- Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO) ,FOS: Physical sciences ,Astrophysics::Cosmology and Extragalactic Astrophysics ,01 natural sciences ,Convolutional neural network ,010305 fluids & plasmas ,0103 physical sciences ,Cluster (physics) ,Instrumentation and Methods for Astrophysics (astro-ph.IM) ,010303 astronomy & astrophysics ,Galaxy cluster ,High Energy Astrophysical Phenomena (astro-ph.HE) ,Physics ,[SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph] ,Contextual image classification ,business.industry ,Astronomy and Astrophysics ,Pattern recognition ,Digitized Sky Survey ,Sample (graphics) ,Data set ,Binary classification ,Space and Planetary Science ,Artificial intelligence ,Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Instrumentation and Methods for Astrophysics ,business ,Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
Galaxy clusters appear as extended sources in XMM-Newton images, but not all extended sources are clusters. So, their proper classification requires visual inspection with optical images, which is a slow process with biases that are almost impossible to model. We tackle this problem with a novel approach, using convolutional neural networks (CNNs), a state-of-the-art image classification tool, for automatic classification of galaxy cluster candidates. We train the networks on combined XMM-Newton X-ray observations with their optical counterparts from the all-sky Digitized Sky Survey. Our data set originates from the X-CLASS survey sample of galaxy cluster candidates, selected by a specially developed pipeline, the XAmin, tailored for extended source detection and characterisation. Our data set contains 1 707 galaxy cluster candidates classified by experts. Additionally, we create an official Zooniverse citizen science project, The Hunt for Galaxy Clusters, to probe whether citizen volunteers could help in a challenging task of galaxy cluster visual confirmation. The project contained 1 600 galaxy cluster candidates in total of which 404 overlap with the expert's sample. The networks were trained on expert and Zooniverse data separately. The CNN test sample contains 85 spectroscopically confirmed clusters and 85 non-clusters that appear in both data sets. Our custom network achieved the best performance in the binary classification of clusters and non-clusters, acquiring accuracy of 90 %, averaged after 10 runs. The results of using CNNs on combined X-ray and optical data for galaxy cluster candidate classification are encouraging and there is a lot of potential for future usage and improvements., 14 pages, 10 tables, 16 figures, accepted for publication in MNRAS
- Published
- 2020