The redshift evolution of the S0 fraction for $z<1$ in COSMOS
Mitchell K. Cavanagh, Kenji Bekki, Brent A. Groves

TL;DR
This study uses deep learning to classify galaxy morphologies in the COSMOS survey, revealing how the fraction of S0 galaxies evolves with redshift and mass, and identifying two distinct S0 populations.
Contribution
It introduces a transfer learning approach for high-redshift galaxy classification, providing new insights into S0 galaxy evolution and bimodal mass distribution.
Findings
S0 fraction increases as redshift decreases.
Two distinct S0 populations identified: high-mass red and low-mass blue.
High-mass S0s evolve earlier than low-mass S0s.
Abstract
Lenticular (S0) galaxies are galaxies that exhibit a bulge and disk component, yet lack any clear spiral features. With features considered intermediary between spirals and ellipticals, S0s have been proposed to be a transitional morphology, however their exact origin and nature is still debated. In this work, we study the redshift evolution of the S0 fraction out to using deep learning to classify F814W (-band) HST-ACS images of 85,378 galaxies in the Cosmological Evolution Survey (COSMOS). We classify galaxies into four morphological categories: elliptical (E), S0, spiral (Sp), and irregular/miscellaneous (IrrM). Our deep learning models, initially trained to classify SDSS images with known morphologies, have been successfully adapted to classify high-redshift COSMOS images via transfer learning and data augmentation, enabling us to classify S0s with superior accuracy.…
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Taxonomy
TopicsSpectroscopy and Chemometric Analyses · Astronomy and Astrophysical Research
