J/A+A/703/A13 Morphological classification of galaxies (Yin+, 2025)
A robust morphological classification method for galaxies using dual-encoding
contrastive learning and multi-clustering voting on JWST/NIRCam images.
Yin X., Fang G., Lu S., Lin Z., Dai Y., Zhou C.
<Astron. Astrophys. 703, A13 (2025)>
=2025A&A...703A..13Y 2025A&A...703A..13Y (SIMBAD/NED BibCode)
ADC_Keywords: Galaxies ; Morphology ; Infrared
Keywords: methods: data analysis - methods: statistical -
techniques: image processing - galaxies: structure
Abstract:
The two-step galaxy morphology classification framework USmorph
successfully combines unsupervised machine learning (UML) with
supervised machine learning (SML) methods. To enhance the UML step, we
employed a dual-encoder architecture (ConvNeXt and ViT) to effectively
encode images, contrastive learning to accurately extract features,
and principal component analysis to efficiently reduce dimensionality.
Based on this improved framework, a sample of 46176 galaxies at
0<z<4.2, selected in the COSMOS-Web field, is classified
into five types using the JWST near-infrared images: 33% spherical
(SPH), 25% early-type disk (ETD), 25% late-type disk (LTD), 7%
irregular (IRR), and 10% unclassified (UNC) galaxies. We also
performed parametric (Sersic index, n, and effective radius, re) and
nonparametric measurements (Gini coefficient, G, the second-order
moment of light, M20, concentration, C, multiplicity, psi, and three
other parameters from the MID statistics) for massive galaxies
(M*>109M☉) to verify the validity of our galaxy
morphological classification system. The analysis of morphological
parameters is consistent with our classification system: SPH and ETD
galaxies with higher n, G, and C tend to be more bulge-dominated and
more compact compared with other types of galaxies. This demonstrates
the reliability of this classification system, which will be useful
for a forthcoming large-sky survey from the Chinese Space Station
Telescope.
Description:
Various physical parameters of galaxy samples are provided, including
parametric and non-parametric values, along with galaxy types and
label information. Specifically, these include: ID, RA , DEC, M20,
mass, z, re, n, flag1, C, A, S, G, M20, M, I, D, G2, multiplicity,
flag2, type, and label.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
catalog.dat 137 46176 Catalog
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Byte-by-byte Description of file: catalog.dat
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Bytes Format Units Label Explanations
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1- 5 I5 --- ID Sequential number identifier
7- 15 F9.5 deg RAdeg Right ascension (J2000)
17- 23 F7.5 deg DEdeg Declination (J2000)
25- 29 F5.2 [Msun] Mass Stellar mass
31- 34 F4.2 --- z Redshift
36- 44 F9.2 kpc re ?=- Effective radius
46- 51 F6.2 --- n ?=-99 Sersic index
53- 55 I3 --- flag1 [0/71]?=-99 Flag of single Sersic index
fitting, 0 represents a good fitting
57- 62 F6.2 --- C ?=-99 C coefficient
64- 69 F6.2 --- A ?=-99 A coefficient
71- 76 F6.2 --- S ?=-99 S coefficient
78- 83 F6.2 --- G ?=-99 G coefficient
85- 90 F6.2 --- M20 ?=-99 M20 coefficient
92- 97 F6.2 --- M ?=-99 M coefficient
99-104 F6.2 --- I ?=-99 I coefficient
106-111 F6.2 --- D ?=-99 D coefficient
113-118 F6.2 --- G2 ?=-99 G2 coefficient
120-125 F6.2 --- Mult ?=-99 psi coefficient
127-130 I4 --- Flag2 [0/9216]?=-99 Flag of the nonparametric
morphological measurements,
0 represents a good fitting
132-133 I2 --- MType Morphology type
135-137 A3 --- Label Morphology label (1)
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Note (1): Morphology label are:
3,5,6,9,15,20 for SPH;
0,4,10,11,18,21 for ETD;
1,8,13,16,22 for LTD;
17,19,23 for IRR;
2,7,12,14,24 for UNC
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Acknowledgements:
Guanwen Fang, 2932038157(at)qq.com
(End) Patricia Vannier [CDS] 27-Aug-2025