Lessons Learned from the Two Largest Galaxy Morphological Classification Catalogues built by Convolutional Neural Networks
- Cheng, Ting-Yun
- Sánchez, H. Domínguez
- Vega-Ferrero, J.
- Conselice, C.J.
- Siudek, M.
- Aragón-Salamanca, A.
- Bernardi, M.
- Cooke, R.
- Ferreira, L.
- Huertas-Company, M.
- Krywult, J.
- Palmese, A.
- Pieres, A.
- Plazas Malagón, A.A.
- Carnero Rosell, A.
- Gruen, D.
- Thomas, D.
- Bacon, D.
- Brooks, D.
- James, D.J.
- Hollowood, D.L.
- Friedel, D.
- Suchyta, E.
- Sanchez, E.
- Menanteau, F.
- Paz-Chinchón, F.
- Gutierrez, G.
- Tarle, G.
- Sevilla-Noarbe, I.
- Ferrero, I.
- Annis, J.
- Frieman, J.
- García-Bellido, J.
- Mena-Fernández, J.
- Honscheid, K.
- Kuehn, K.
- da Costa, L.N.
- Gatti, M.
- Raveri, M.
- Pereira, M.E.S.
- Rodriguez-Monroy, M.
- Smith, M.
- Carrasco Kind, M.
- Aguena, M.
- Swanson, M.E.C.
- Weaverdyck, N.
- Doel, P.
- Miquel, R.
- Ogando, R.L.C.
- Gruendl, R.A.
- Allam, S.
- Hinton, S.R.
- Dodelson, S.
- Bocquet, S.
- Desai, S.
- Everett, S.
- Scarpine, V.
DOIAbstract
International audienceWe compare the two largest galaxy morphology catalogues, which separate early and late type galaxies at intermediate redshift. The two catalogues were built by applying supervised deep learning (convolutional neural networks, CNNs) to the Dark Energy Survey data down to a magnitude limit of $\sim$21 mag. The methodologies used for the construction of the catalogues include differences such as the cutout sizes, the labels used for training, and the input to the CNN - monochromatic images versus $gri$-band normalized images. In addition, one catalogue is trained using bright galaxies observed with DES ($i19$, the overall agreement is good ($\sim$95%), but is mostly driven by the large spiral fraction in the two catalogue...
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