Morphological classification of galaxies

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.