A1 Refereed original research article in a scientific journal
Euclid preparation XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning
Authors: Aussel, B.; Kruk, S.; Walmsley, M.; Huertas-Company, M.; Castellano, M.; Conselice, C.J.; Delli Veneri, M.; Domínguez Sánchez, H.; Duc, P.A.; Knapen, J.H.; Kuchner, U.; La Marca, A.; Margalef-Bentabol, B.; Marleau, F.R.; Stevens, G.; Toba, Y.; Tortora, C.; Wang, L.; Aghanim, N.; Altieri, B.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Bender, R.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Cavuoti, S.; Cimatti, A.; Congedo, G.; Conversi, L.; Copin, Y.; Courbin, F.; Courtois, H.M.; Cropper, M.; Da Silva, A.; Degaudenzi, H.; Di Giorgio, A.M.; Dinis, J.; Dubath, F.; Dupac, X.; Dusini, S.; Farina, M.; Farrens, S.; Ferriol, S.; Fotopoulou, S.; Frailis, M.; Franceschi, E.; Franzetti, P.; Fumana, M.; Galeotta, S.; Garilli, B.; Gillis, B.; Giocoli, C.; Grazian, A.; Grupp, F.; Haugan, S.V.H.; Holmes, W.; Hook, I.; Hormuth, F.; Hornstrup, A.; Hudelot, P.; Jahnke, K.; Keihänen, E.; Kermiche, S.; Kiessling, A.; Kilbinger, M.; Kubik, B.; Kümmel, M.; Kunz, M.; Kurki-Suonio, H.; Laureijs, R.; Ligori, S.; Lilje, P.B.; Lindholm, V.; Lloro, I.; Maiorano, E.; Mansutti, O.; Marggraf, O.; Markovic, K.; Martinet, N.; Marulli, F.; Massey, R.; Maurogordato, S.; Medinaceli, E.; Mei, S.; Mellier, Y.; Meneghetti, M.; Merlin, E.; Meylan, G.; Moresco, M.; Moscardini, L.; Munari, E.; Niemi, S.M.; Padilla, C.; Paltani, S.; Pasian, F.; Pedersen, K.; Percival, W.J.; Pettorino, V.; Pires, S.; Polenta, G.; Poncet, M.; Popa, L.A.; Pozzetti, L.; Raison, F.; Rebolo, R.; Renzi, A.; Rhodes, J.; Riccio, G.; Romelli, E.; Roncarelli, M.; Rossetti, E.; Saglia, R.; Sapone, D.; Sartoris, B.; Schirmer, M.; Schneider, P.; Secroun, A.; Seidel, G.; Serrano, S.; Sirignano, C.; Sirri, G.; Stanco, L.; Starck, J.L.; Tallada-Crespí, P.; Taylor, A.N.; Teplitz, H.I.; Tereno, I.; Toledo-Moreo, R.; Torradeflot, F.; Tutusaus, I.; Valentijn, E.A.; Valenziano, L.; Vassallo, T.; Veropalumbo, A.; Wang, Y.; Weller, J.; Zacchei, A.; Zamorani, G.; Zoubian, J.; Zucca, E.; Biviano, A.; Bolzonella, M.; Boucaud, A.; Bozzo, E.; Burigana, C.; Colodro-Conde, C.; Di Ferdinando, D.; Farinelli, R.; Graciá-Carpio, J.; Mainetti, G.; Marcin, S.; Mauri, N.; Neissner, C.; Nucita, A.A.; Sakr, Z.; Scottez, V.; Tenti, M.; Viel, M.; Wiesmann, M.; Akrami, Y.; Allevato, V.; Anselmi, S.; Baccigalupi, C.; Ballardini, M.; Borgani, S.; Borlaff, A.S.; Bretonnière, H.; Bruton, S.; Cabanac, R.; Calabro, A.; Cappi, A.; Carvalho, C.S.; Castignani, G.; Castro, T.; Cañas-Herrera, G.; Chambers, K.C.; Coupon, J.; Cucciati, O.; Davini, S.; De Lucia, G.; Desprez, G.; Di Domizio, S.; Dole, H.; Díaz-Sánchez, A.; Escartin Vigo, J.A.; Escoffier, S.; Ferrero, I.; Finelli, F.; Gabarra, L.; Ganga, K.; García-Bellido, J.; Gaztanaga, E.; George, K.; Giacomini, F.; Gozaliasl, G.; Gregorio, A.; Guinet, D.; Hall, A.; Hildebrandt, H.; Jimenez Muñoz, A.; Kajava, J.J.E.; Kansal, V.; Karagiannis, D.; Kirkpatrick, C.C.; Legrand, L.; Loureiro, A.; Macias-Perez, J.; Magliocchetti, M.; Maoli, R.; Martinelli, M.; Martins, C.J.A.P.; Matthew, S.; Maturi, M.; Maurin, L.; Metcalf, R.B.; Migliaccio, M.; Monaco, P.; Morgante, G.; Nadathur, S.; Walton, N.A.; Peel, A.; Pezzotta, A.; Popa, V.; Porciani, C.; Potter, D.; Pöntinen, M.; Reimberg, P.; Rocci, P.F.; Sánchez, A.G.; Schneider, A.; Sefusatti, E.; Sereno, M.; Simon, P.; Spurio Mancini, A.; Stanford, S.A.; Steinwagner, J.; Testera, G.; Tewes, M.; Teyssier, R.; Toft, S.; Tosi, S.; Troja, A.; Tucci, M.; Valieri, C.; Valiviita, J.; Vergani, D.; Zinchenko, I.A.
Publisher: EDP Sciences
Publication year: 2024
Journal: Astronomy and Astrophysics
Journal name in source: Astronomy and Astrophysics
Article number: A274
Volume: 689
ISSN: 0004-6361
eISSN: 1432-0746
DOI: https://doi.org/10.1051/0004-6361/202449609
Web address : https://doi.org/10.1051/0004-6361/202449609
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/458477393
The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies of galaxies in Euclid using Zoobot, a convolutional neural network pretrained with 450 000 galaxies from the Galaxy Zoo project. We adapted Zoobot for use with emulated Euclid images generated based on Hubble Space Telescope COSMOS images and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We experimented with different numbers of galaxies and various magnitude cuts during the training process. We demonstrate that the trained Zoobot model successfully measures detailed galaxy morphology in emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features, such as spiral arms, clumps, bars, discs, and central bulges. When compared to volunteer classifications, Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy of above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes, such as disc or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. On the other hand, for more detailed structures and complex tasks, such as detecting and counting spiral arms or clumps, the deviations are slightly higher, of namely around 12% with 60 000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowd sourcing. We estimate that, with our model, the detailed morphology of approximately 800 million galaxies of the Euclid Wide Survey could be reliably measured and that approximately 230 million of these galaxies would display features. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images.
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Funding information in the publication:
The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the French Centre National d’Etudes Spatiales, the Deutsches Zentrum für Luft- und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Hungarian Academy of Sciences, the Ministerio de Ciencia, Innovación y Universidades, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Research Council of Finland, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (http://www.euclid-ec.org). The data in this paper are the result of the efforts of the Galaxy Zoo volunteers, without whom none of this work would be possible. Their efforts are individually acknowledged at http://authors.galaxyzoo.org. This publication uses data generated via the Zooniverse.org platform, development of which is funded by generous support, including a Global Impact Award from Google, and by a grant from the Alfred P. Sloan Foundation.
