Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks

Cheng, Ting-Yun; Conselice, Christopher J.; Aragón-Salamanca, Alfonso; Aguena, M.; Allam, S.; Andrade-Oliveira, F.; Annis, J.; Bluck, A.F.L.; Brooks, D.; Burke, D.L.; Carrasco Kind, M.; Carretero, J.; Choi, A.; Costanzi, M.; da Costa, L.N.; Pereira, M.E.S.; De Vicente, J.; Diehl, H.T.; Drlica-Wagner, A.; Eckert, K.; Everett, S.; Evrard, A.E.; Ferrero, I.; Fosalba, P.; Frieman, J.; García-Bellido, J.; Gerdes, D.W.; Giannantonio, T.; Gruen, D.; Gruendl, R.A.; Gschwend, J.; Gutierrez, G.; Hinton, S.R.; Hollowood, D.L.; Honscheid, K.; James, D.J.; Krause, E.; Kuehn, K.; Kuropatkin, N.; Lahav, O.; Maia, M.A.G.; March, M.; Menanteau, F.; Miquel, R.; Morgan, R.; Paz-Chinchón, F.; Pieres, A.; Plazas Malagón, A.A.; Roodman, A.; Sanchez, E.; Scarpine, V.; Serrano, S.; Sevilla-Noarbe, I.; Smith, M.; Soares-Santos, M.; Suchyta, E.; Swanson, M.E.C.; Tarle, G.; Thomas, D.; To, C.

Abstract

We present in this paper one of the largest galaxy morphological classification catalogues to date, including over 20 million galaxies, using the Dark Energy Survey (DES) Year 3 data based on convolutional neural networks (CNNs). Monochromatic i-band DES images with linear, logarithmic, and gradient scales, matched with debiased visual classifications from the Galaxy Zoo 1 (GZ1) catalogue, are used to train our CNN models. With a training set including bright galaxies (16 ≤ i < 18) at low redshift (z < 0.25), we furthermore investigate the limit of the accuracy of our predictions applied to galaxies at fainter magnitude and at higher redshifts. Our final catalogue covers magnitudes 16 ≤ i < 21, and redshifts z < 1.0, and provides predicted probabilities to two galaxy types – ellipticals and spirals (disc galaxies). Our CNN classifications reveal an accuracy of over 99 per cent for bright galaxies when comparing with the GZ1 classifications (i < 18). For fainter galaxies, the visual classification carried out by three of the co-authors shows that the CNN classifier correctly categorizes discy galaxies with rounder and blurred features, which humans often incorrectly visually classify as ellipticals. As a part of the validation, we carry out one of the largest examinations of non-parametric methods, including ∼100 ,000 galaxies with the same coverage of magnitude and redshift as the training set from our catalogue. We find that the Gini coefficient is the best single parameter discriminator between ellipticals and spirals for this data set.

Citation

Cheng, T.-Y., Conselice, C. J., Aragón-Salamanca, A., Aguena, M., Allam, S., Andrade-Oliveira, F., Annis, J., Bluck, A., Brooks, D., Burke, D., Carrasco Kind, M., Carretero, J., Choi, A., Costanzi, M., da Costa, L., Pereira, M., De Vicente, J., Diehl, H., Drlica-Wagner, A., Eckert, K., …To, C. (2021). Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks. Monthly Notices of the Royal Astronomical Society, 507(3), 4425-4444. https://doi.org/10.1093/mnras/stab2142

• Journal Article Type: Article
• Acceptance Date: Jul 21, 2021
• Online Publication Date: Jul 24, 2021
• Publication Date: 2021-11
• Deposit Date: Jul 21, 2021
• Publicly Available Date: Nov 23, 2021
• Journal: Monthly Notices of Royal Astronomical Society
• Print ISSN: 0035-8711
• Electronic ISSN: 1365-2966
• Publisher: Royal Astronomical Society
• Peer Reviewed: Peer Reviewed
• Volume: 507
• Issue: 3
• Pages: 4425-4444
• DOI: https://doi.org/10.1093/mnras/stab2142
• Public URL: https://durham-repository.worktribe.com/output/1271506