COSMOS2020 galaxy morphology classif. with USmorph

DOI

Morphological classification conveys abundant information on the formation, evolution, and environment of galaxies. In this work, we refine a two-step galaxy morphological classification framework (USmorph), which employs a combination of unsupervised machine-learning and supervised machine-learning techniques, along with a self-consistent and robust data-preprocessing step. The updated method is applied to galaxies with Imag<25 at 0.2

Identifier
DOI http://doi.org/10.26093/cds/vizier.22720042
Source https://dc.g-vo.org/rr/q/lp/custom/CDS.VizieR/J/ApJS/272/42
Related Identifier https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJS/272/42
Related Identifier https://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJS/272/42
Metadata Access http://dc.g-vo.org/rr/q/pmh/pubreg.xml?verb=GetRecord&metadataPrefix=oai_b2find&identifier=ivo://CDS.VizieR/J/ApJS/272/42
Provenance
Creator Song J.; Fang G.; Ba S.; Lin Z.; Gu Y.; Zhou C.; Wang T.; Hao C.-N.,Liu G.; Zhang H.; Yao Y.; Kong Xu
Publisher CDS
Publication Year 2024
Rights https://cds.unistra.fr/vizier-org/licences_vizier.html
OpenAccess true
Contact CDS support team <cds-question(at)unistra.fr>
Representation
Resource Type Dataset; AstroObjects
Discipline Astrophysics and Astronomy; Cosmology; Galactic and extragalactic Astronomy; Natural Sciences; Observational Astronomy; Physics