Dr Sunny Cheng ting-yun.cheng@durham.ac.uk
Post Doctoral Research Associate
Dr Sunny Cheng ting-yun.cheng@durham.ac.uk
Post Doctoral Research Associate
H Domínguez Sánchez
J Vega-Ferrero
CJ Conselice
M Siudek
A Aragón-Salamanca
M Bernardi
R Cooke
L Ferreira
M Huertas-Company
J Krywult
A Palmese
A Pieres
AA Plazas Malagón
A Carnero Rosell
D Gruen
D Thomas
D Bacon
D Brooks
DJ James
DL 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
LN da Costa
M Gatti
M Raveri
MES Pereira
M Rodriguez-Monroy
M Smith
M Carrasco Kind
M Aguena
MEC Swanson
N Weaverdyck
P Doel
R Miquel
RLC Ogando
RA Gruendl
S Allam
SR Hinton
S Dodelson
S Bocquet
S Desai
S Everett
V Scarpine
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.
Cheng, T., Domínguez Sánchez, H., Vega-Ferrero, J., Conselice, C., Siudek, M., Aragón-Salamanca, A., …Scarpine, V. (2023). Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks. Monthly Notices of the Royal Astronomical Society, 518(2), 2794-2809. https://doi.org/10.1093/mnras/stac3228
Journal Article Type | Article |
---|---|
Acceptance Date | Nov 2, 2022 |
Online Publication Date | Nov 11, 2022 |
Publication Date | 2023-01 |
Deposit Date | Jan 18, 2023 |
Publicly Available Date | Jan 18, 2023 |
Journal | Monthly Notices of the Royal Astronomical Society |
Print ISSN | 0035-8711 |
Electronic ISSN | 1365-2966 |
Publisher | Royal Astronomical Society |
Peer Reviewed | Peer Reviewed |
Volume | 518 |
Issue | 2 |
Pages | 2794-2809 |
DOI | https://doi.org/10.1093/mnras/stac3228 |
Public URL | https://durham-repository.worktribe.com/output/1181362 |
Published Journal Article
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
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