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

Desai, Shantanu (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). pp. 2794-2809. ISSN 0035-8711

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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 similar to 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 (similar to 95 percent), 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.

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IITH Creators:

IITH Creators	ORCiD
Desai, Shantanu	http://orcid.org/0000-0002-0466-3288

Item Type:

Article

Uncontrolled Keywords:

REDSHIFT SURVEY VIPERS; STELLAR MASS; DATA RELEASE; PAU SURVEY; EVOLUTION; ZOO; POPULATIONS; TRANSFORMATIONS; LUMINOSITY; CLUSTERS; methods: data analysismethods: statisticalgalaxies: structure

Subjects:

Physics
Physics > Classical mechanics
Physics > Modern physics

Divisions:

Department of Physics

Depositing User:

Mr Nigam Prasad Bisoyi

Date Deposited:

18 Aug 2023 08:46

Last Modified:

18 Aug 2023 08:46

URI:

http://raiithold.iith.ac.in/id/eprint/11570