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.

doi:10.1093/mnras/stab2142

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.

Authors

Dr Sunny Cheng ting-yun.cheng@durham.ac.uk
Post Doctoral Research Associate

Christopher J. Conselice

Alfonso Aragón-Salamanca

M. Aguena

S. Allam

F. Andrade-Oliveira

J. Annis

A.F.L. Bluck

D. Brooks

D.L. Burke

M. Carrasco Kind

J. Carretero

A. Choi

M. Costanzi

L.N. da Costa

M.E.S. Pereira

J. De Vicente

H.T. Diehl

A. Drlica-Wagner

K. Eckert

S. Everett

A.E. Evrard

I. Ferrero

P. Fosalba

J. Frieman

J. García-Bellido

D.W. Gerdes

T. Giannantonio

D. Gruen

R.A. Gruendl

J. Gschwend

G. Gutierrez

S.R. Hinton

D.L. Hollowood

K. Honscheid

D.J. James

E. Krause

K. Kuehn

N. Kuropatkin

O. Lahav

M.A.G. Maia

M. March

F. Menanteau

R. Miquel

R. Morgan

F. Paz-Chinchón

A. Pieres

A.A. Plazas Malagón

A. Roodman

E. Sanchez

V. Scarpine

S. Serrano

I. Sevilla-Noarbe

M. Smith

M. Soares-Santos

E. Suchyta

M.E.C. Swanson

G. Tarle

D. Thomas

C. To

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., Conselice, C. J., Aragón-Salamanca, A., Aguena, M., Allam, S., Andrade-Oliveira, F., …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

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© The Author(s) 2021. Published by Oxford University Press on behalf of Royal Astronomical Society.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.