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We present a catalogue of low surface brightness (LSB) galaxies in the Coma cluster obtained from deep Subaru/Suprime-Cam V and R-band imaging data within a region of $sim$4 deg$^2$. We increase the number of LSB galaxies presented in Yagi et al. (2016) by a factor of $sim$3 and report the discovery of 29 new ultra-diffuse galaxies (UDGs). We compile the largest sample of ultra-diffuse galaxies with colours and structural parameters in the Coma cluster. While most UDGs lie along the red-sequence relation of the colour-magnitude diagram, $sim$5per cent are outside (bluer or redder) the red-sequence region of Coma cluster galaxies. Our analyses show that there is no special distinction in the basic photometric parameters between UDGs and other LSB galaxies. We investigate the clustercentric colour distribution and find a remarkable transition at a projected radius of $sim$0.6 Mpc. Within this cluster core region, LSB galaxies are, on average, redder than co-spatial higher surface brightness galaxies, highlighting how vulnerable LSB galaxies are to the physical processes at play in the dense central region of the cluster. The position of the transition radius agrees with expectations from recent cosmological simulation of massive galaxy clusters within which ancient infalls are predicted to dominate the LSB galaxy population.
We present a catalog of extended low-surface-brightness galaxies (LSBGs) identified in the Wide layer of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Using the first ${sim}$200 deg$^2$ of the survey, we have uncovered 781 LSBGs, spanning
We increase the sample of ultra diffuse galaxies (UDGs) in lower density environments with characterized globular cluster (GC) populations using new Hubble Space Telescope observations of nine UDGs in group environments. While the bulk of our UDGs ha
This project is the continuation of our study of faint Low Surface Brightness Galaxies (fLSBs) in one of the densest nearby galaxy regions known, the Coma cluster. Our goal is to improve our understanding of the nature of these objects by comparing t
We report an automated morphological classification of galaxies into S-wise spirals, Z-wise spirals, and non-spirals using big image data taken from Subaru/Hyper Suprime-Cam (HSC) Survey and a convolutional neural network(CNN)-based deep learning tec
We first present a catalogue of photometric redshifts for 14.68 million galaxies derived from the 7-band photometric data of Hyper Suprime-Cam Subaru Strategic Program and the Wide-field Infrared Survey Explorer using the nearest-neighbour algorithm.