Subaru Hyper Suprime-Cam revisits the large-scale environmental dependence on galaxy morphology over 360 deg$^2$ at z=0.3-0.6
Rhythm Shimakawa, Takumi S. Tanaka, Seiji Toshikage, Masayuki Tanaka

TL;DR
This study uses deep learning on wide-field Subaru data to examine how large-scale environments influence galaxy morphology, revealing moderate density relations and spiral deficits near clusters at redshifts 0.3-0.6.
Contribution
It introduces a transfer learning approach with deep learning for galaxy morphology classification over a large area, enabling new insights into environmental effects on galaxy types.
Findings
Moderate morphology-density relation on 10 Mpc scales.
Spiral galaxy deficits near red sequence clusters.
No significant color-environment dependence observed.
Abstract
This study investigates the role of large-scale environments on the fraction of spiral galaxies at 0.3-0.6 sliced to three redshift bins of . Here, we sample 276220 massive galaxies in a limited stellar mass of solar mass () over 360 deg, as obtained from the Second Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). By combining projected two-dimensional density information (Shimakawa et al. 2021) and the CAMIRA cluster catalog (Oguri et al. 2018), we investigate the spiral fraction across large-scale overdensities and in the vicinity of red sequence clusters. We adopt transfer learning to significantly reduce the cost of labeling spiral galaxies and then perform stacking analysis across the entire field to overcome the limitations of sample size. Here we employ a morphological classification catalog by…
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