Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks

Autor: T-Y Cheng, H Domínguez Sánchez, J Vega-Ferrero, C J Conselice, M Siudek, A Aragón-Salamanca, M Bernardi, R Cooke, L Ferreira, M Huertas-Company, J Krywult, A Palmese, A Pieres, A A Plazas Malagón, A Carnero Rosell, D Gruen, D Thomas, D Bacon, D Brooks, D J James, D L Hollowood, D Friedel, E Suchyta, E Sanchez, F Menanteau, F Paz-Chinchón, G Gutierrez, G Tarle, I Sevilla-Noarbe, I Ferrero, J Annis, J Frieman, J García-Bellido, J Mena-Fernández, K Honscheid, K Kuehn, L N da Costa, M Gatti, M Raveri, M E S Pereira, M Rodriguez-Monroy, M Smith, M Carrasco Kind, M Aguena, M E C Swanson, N Weaverdyck, P Doel, R Miquel, R L C Ogando, R A Gruendl, S Allam, S R Hinton, S Dodelson, S Bocquet, S Desai, S Everett, V Scarpine
Přispěvatelé: UAM. Departamento de Física Teórica
Jazyk: angličtina
Rok vydání: 2022
Předmět:
ISSN: 0035-8711
DOI: 10.1093/mnras/stac3228
Popis: Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, los autores pertenecientes a la UAM y el nombre del grupo de colaboración, si lo hubiere
This is an electronic version of an article published in Monthly Notices of the Royal Astronomical Society. T. Y. Cheng, H. Domínguez Sánchez, J. Vega-Ferrero, C. J. Conselice, M. Siudek, A. Aragón-Salamanca, M. Bernardi, R. Cooke, L. Ferreira, M. Huertas-Company , J. Krywult, A. Palmese , A. Pieres , A. A. Plazas Malagón, A. Carnero Rosell , D. Gruen, D. Thomas , D. Bacon, D. Brooks, D. J. James, D. L. Hollowood, D. Friedel, E. Suchyta, E. Sánchez, F. Menanteau, F. Paz-Chinchón, G. Gutiérrez, G. Tarle, I. Sevilla-Noarbe, I. Ferrero, J. Annis, J. Frieman, J. García-Bellido, J. Mena-Fernández, K. Honscheid, K. Kuehn, L. N. da Costa, M. Gatti, M. Raveri, M. E. S. Pereira, M. Rodríguez-Monroy, M. Smith, M. Carrasco Kind, M. Aguena, M. E. C. Swanson, N. Weaverdyck, P. Doel, R. Miquel, R. L. C. Ogando, R. A. Gruendl, S. Allam, S. R. Hinton, S. Dodelson, S. Bocquet, S. Desai, S. Everett and V. Scarpine in “Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks”. Monthly Notices of the Royal Astronomical Society 518.2 (2023): 2794-2809
We 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 ∼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 (i < 18), while the other is trained with bright galaxies (r < 17.5) and ‘emulated’ galaxies up to r-band magnitude 22.5. Despite the different approaches, the agreement between the two catalogues is excellent up to i < 19, demonstrating that CNN predictions are reliable for samples at least one magnitude fainter than the training sample limit. It also shows that morphological classifications based on monochromatic images are comparable to those based on gri-band images, at least in the bright regime. At fainter magnitudes, i > 19, the overall agreement is good (∼95 per cent), but is mostly driven by the large spiral fraction in the two catalogues. In contrast, the agreement within the elliptical population is not as good, especially at faint magnitudes. By studying the mismatched cases, we are able to identify lenticular galaxies (at least up to i < 19), which are difficult to distinguish using standard classification approaches. The synergy of both catalogues provides an unique opportunity to select a population of unusual galaxies
Databáze: OpenAIRE
Externí odkaz: