TY - JOUR
T1 - Euclid preparation. XLIII. Measuring detailed galaxy morphologies for Euclid with Machine Learning
AU - Euclid Collaboration
AU - Collaboration, Euclid
AU - Aussel, B.
AU - Kruk, S.
AU - Walmsley, M.
AU - Huertas-Company, M.
AU - Castellano, M.
AU - Conselice, C. J.
AU - Veneri, M. Delli
AU - Sánchez, H. Domínguez
AU - Duc, P. -A.
AU - Kuchner, U.
AU - Marca, A. La
AU - Margalef-Bentabol, B.
AU - Marleau, F. R.
AU - Stevens, G.
AU - Toba, Y.
AU - Tortora, C.
AU - Wang, L.
AU - Aghanim, N.
AU - Altieri, B.
AU - Amara, A.
AU - Andreon, S.
AU - Auricchio, N.
AU - Baldi, M.
AU - Bardelli, S.
AU - Bender, R.
AU - Bodendorf, C.
AU - Bonino, D.
AU - Branchini, E.
AU - Brescia, M.
AU - Brinchmann, J.
AU - Camera, S.
AU - Capobianco, V.
AU - Carbone, C.
AU - Carretero, J.
AU - Casas, S.
AU - Cavuoti, S.
AU - Cimatti, A.
AU - Congedo, G.
AU - Conversi, L.
AU - Copin, Y.
AU - Markovic, K.
AU - Percival, W. J.
AU - Taylor, A. N.
AU - Wang, Y.
AU - Weller, J.
AU - Gaztanaga, E.
AU - Kirkpatrick, C. C.
AU - Nadathur, S.
AU - Sánchez, A. G.
N1 - 27 pages, 26 figures, 5 tables, submitted to A&A
PY - 2024/9/19
Y1 - 2024/9/19
N2 - The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy 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 disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 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 crowdsourcing. 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.
AB - The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy 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 disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 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 crowdsourcing. 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.
KW - astro-ph.GA
KW - galaxies: structure
KW - galaxies: evolution
KW - techniques: image processing
KW - methods: data analysis
KW - methods: observational
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=webofscienceportsmouth2022&SrcAuth=WosAPI&KeyUT=WOS:001325214900001&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1051/0004-6361/202449609
DO - 10.1051/0004-6361/202449609
M3 - Article
SN - 0004-6361
VL - 689
JO - Astronomy and Astrophysics
JF - Astronomy and Astrophysics
M1 - A274
ER -