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By applying our previously developed two-step scheme for galaxy morphologyclassification, we present a catalog of galaxy morphology for H-band selectedmassive galaxies in the COSMOS-DASH field, which includes 17292 galaxies withstellar mass $M_{\star}>10^{10}~M_{\odot}$ at $0.5<z<2.5$. The classificationscheme is designed to provide a complete morphology classification for galaxiesvia a combination of two machine-learning steps. We first use an unsupervisedmachine learning method (i.e., bagging-based multi-clustering) to clustergalaxies into five categories: spherical (SPH), early-type disk (ETD),late-type disk (LTD), irregular (IRR), and unclassified (UNC). About 48\% ofgalaxies (8258/17292) are successfully clustered during this step. For theremaining sample, we adopt a supervised machine learning method (i.e.,GoogLeNet) to classify them, during which galaxies that are well-classified inthe previous step are taken as our training set. Consequently, we obtain amorphology classification result for the full sample. The t-SNE test shows thatgalaxies in our sample can be well aggregated. We also measure the parametricand nonparametric morphologies of these galaxies. We find that the S\'{e}rsicindex increases from IRR to SPH and the effective radius decreases from IRR toSPH, consistent with the corresponding definitions. Galaxies from differentcategories are separately distributed in the $G$--$M_{20}$ space. Suchconsistencies with other characteristic descriptions of galaxy morphologydemonstrate the reliability of our classification result, ensuring that it canbe used as a basic catalog for further galaxy studies.

The Classification of Galaxy Morphology in H-band of COSMOS-DASH Field: a combination-based machine learning clustering model