Cheng, T-Y;
Sanchez, H Dominguez;
Vega-Ferrero, J;
Conselice, CJ;
Siudek, M;
Aragon-Salamanca, A;
Bernardi, M;
... Scarpine, V; + view all
(2022)
Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks.
Monthly Notices of the Royal Astronomical Society
, 518
(2)
pp. 2794-2809.
10.1093/mnras/stac3228.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/stac3228 |
| Publisher version: | https://doi.org/10.1093/mnras/stac3228 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Physical Sciences, Astronomy & Astrophysics, methods: data analysis, methods: statistical, galaxies: structure, REDSHIFT SURVEY VIPERS, STELLAR MASS, DATA RELEASE, PAU SURVEY, EVOLUTION, ZOO, POPULATIONS, TRANSFORMATIONS, LUMINOSITY, CLUSTERS |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10167131 |
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