J/A+A/703/A13ivo://CDS.VizieR/J/A+A/703/A13doi:10.26093/cds/vizier/bc-nz/lbdCDSYin X.Fang G.Lu S.Lin Z.Dai Y.Zhou C.2026-08-03T06:59:03Z2025-11-18T11:15:24ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frgalaxiesgalaxy-classification-systemsinfrared-astronomyThe 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*>10^9^M_{sun}_) 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.2025A&A...703A..13Yhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/703/A13CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicehttps://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/703/A13https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/703/A13http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/703/A13https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/703/A13https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/703/A13http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/703/A13GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/A+A/703/A13/catalog (Catalog)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/A+A/703/A13/catalog?https://vizier.iucaa.in/viz-bin/conesearch/J/A+A/703/A13/catalog?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/A+A/703/A13/catalog?GETtext/xml+votable180.050000true149.797222.133790.0055555555555555566/27256 27258https://cdsarc.cds.unistra.fr/viz-bin/moc/J/A+A/703/A13?format=asciiOpticalInfrareddefaultJ/A+A/703/A13/catalogCatalogrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintIDSequential number identifiermeta.id;meta.mainintRAJ2000Right ascension (J2000)degpos.eq.ra;meta.mainindexedDEJ2000Declination (J2000)degpos.eq.dec;meta.mainindexedMassStellar masslog(solMass)phys.massfloatzRedshiftsrc.redshiftfloatre? Effective radiuskpcphys.size.radiusdoublenullablen? Sersic indexstat.fit.paramfloatnullableC? C coefficientmeta.notefloatnullableflag1[0/71]? Flag of single Sersic index fitting, 0 represents a good fittingmeta.code.errorintnullableA? A coefficientphys.atmol.transProbfloatnullableS? S coefficientstat.fit.paramfloatnullableG? G coefficientmeta.notefloatnullableM20? M20 coefficientstat.fit.paramfloatnullableM? M coefficientstat.fit.paramfloatnullableI? I coefficientstat.fit.paramfloatnullableD? D coefficientmeta.notefloatnullableG2? G2 coefficientstat.fit.paramfloatnullableMult? psi coefficientstat.fit.paramfloatnullableFlag2[0/9216]? Flag of the nonparametric morphological measurements, 0 represents a good fittingmeta.code.errorintnullableMTypeMorphology typesrc.morph.typeintLabelMorphology label (1)meta.codechar