Related papers: Preparing for Rubin-LSST -- Detecting Brightest Cl…

Upcoming deep optical surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will scan the sky to unprecedented depths, detecting billions of galaxies. However, this amount of detections will lead to the…

Tidal features provide signatures of recent galaxy mergers, offering insights into the role of mergers in galaxy evolution. The Vera C. Rubin Observatory's upcoming Legacy Survey of Space and Time (LSST) will allow for an unprecedented…

We present a detailed analysis of the surface brightness distribution of the brightest cluster galaxy (BCG) in each of 24 galaxy clusters at 0.03<z<0.13. We use two-dimensional profile fitting to model the surface brightness out to r=300…

Low surface brightness galaxies (LSBGs) which are defined as galaxies that are fainter than the night sky, play a crucial role in understanding galaxy evolution and cosmological models. Upcoming large-scale surveys like Rubin Observatory…

We investigate the formation channels of the intracluster light (ICL) and the brightest cluster galaxy (BCG) in clusters at $z=0$. For this, we perform multi-resolution cosmological N-body simulations using the "Galaxy Replacement…

The properties of the brightest galaxies (BCGs) are studied in both compact and loose groups of galaxies in order to better understand the physical mechanisms influencing galaxy evolution in different environments. Samples of BCGs are…

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will start by the end of 2025 and operate for ten years, offering billions of observations of the southern night sky. One of its main science goals is to create an…

We describe the scientific motivation behind, and the methodology of, the Stanford Cluster Search (StaCS), a program to compile a catalog of optically selected clusters of galaxies at intermediate and high (0.3 < z < 1) redshifts. The…

Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supervised machine learning method (GoogLeNet) to automatically…

We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that…

We identify close companions of Brightest Cluster Galaxies (BCGs) for the purpose of quantifying the rate at which these galaxies grow via mergers. By exploiting deep photometric data from the CFHTLS, we probe the number of companions per…

We present the first cluster catalogue extracted from the UKIRT Infrared Deep Sky Survey Early Data Release. The catalogue is created using UKIDSS Ultra Deep Survey infrared J and K data combined with 3.6 micro-m and 4.5 micro-m Spitzer…

Machine learning (ML) techniques, in particular supervised regression algorithms, are a promising new way to use multiple observables to predict a cluster's mass or other key features. To investigate this approach we use the \textsc{MACSIS}…

Identification of high redshift clusters is important for studies of cosmology and cluster evolution. Using photometric redshifts of galaxies, we identify 631 clusters from the Canada-France-Hawaii Telescope (CFHT) Wide field, 202 clusters…

Images from the next generation of telescopes will enable strikingly detailed reconstruction of the dark matter distributions in galaxy cluster cores using strong gravitational lensing analysis. This will provide a key test of Lambda-CDM…

We use high-precision photometry of red-giant-branch (RGB) stars in 57 Galactic globular clusters (GCs), mostly from the `Hubble Space Telescope (HST) UV Legacy Survey of Galactic globular clusters', to identify and characterize their…

Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…

We present an innovative and widely applicable approach for the detection and classification of stellar clusters, developed for the PHANGS-HST Treasury Program, an $NUV$-to-$I$ band imaging campaign of 38 spiral galaxies. Our pipeline first…

As demonstrated with the Sloan Digital Sky Survey (SDSS), Pan-STARRS, and most recently with Gaia data, broadband near-UV to near-IR stellar photometry can be used to estimate distance, metallicity, and interstellar dust extinction along…

Through gravitational lensing, galaxy clusters can magnify supernovae (SNe) and create multiple images of the same SN. This enables measurements of cosmological parameters, which will be increasingly important in light of upcoming…