Related papers: A robust morphological classification method for g…
We present optical morphologies obtained from deep HST and ground-based images for galaxies selected from the first sub-millimeter survey of the distant Universe. Our sample comprises galaxies detected in deep 850-micron continuum maps of…
We present a comprehensive search and analysis of high-redshift galaxies in a suite of nine public JWST extragalactic fields taken in Cycle 1, covering a total effective search area of $\sim358{\rm arcmin^2}$. Through conservative…
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 a machine learning (ML) pipeline to identify star clusters in the multi{color images of nearby galaxies, from observations obtained with the Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy ExtraGalactic…
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine…
We present the number densities and physical properties of the bright galaxies spectroscopically confirmed at $z\sim7-14$. Our sample is composed of 60 galaxies at $z_\mathrm{spec}\sim7-14$, including recently-confirmed galaxies at…
Identifying merging galaxies is an important - but difficult - step in galaxy evolution studies. We present random forest classifications of galaxy mergers from simulated JWST images based on various standard morphological parameters. We…
We present criteria for separately classifying stars and unresolved background galaxies in photometric catalogs generated with the point spread function (PSF) fitting photometry software DOLPHOT from images taken of Draco II, WLM, and M92…
Galaxy morphology encodes key information about formation and evolution. Large imaging surveys require automated, reproducible methods beyond visual inspection. Non--parametric indices provide an useful framework, but their performance must…
We present the first James Webb Space Telescope/NIRCam-led determination of $7<z<9$ galaxy properties based on broadband imaging from 0.8 to 5~$\mathrm{\mu m}$ as part of the GLASS-JWST Early Release Science program. This is the deepest…
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…
We utilise JWST NIRCam medium-band imaging to search for extreme redshift ($z \geq 9.5$) galaxy candidates in the Hubble Ultra Deep Field (HUDF) and the additional pointing within the GOODS-South field provided by the second NIRCam module.…
Over the past year, JWST has uncovered galaxies at record-breaking distances up to $z \sim 13$. The JWST UNCOVER (ultra-deep NIRSpec and NIRcam observations before the epoch of reionization) program has obtained ultra-deep multiwavelength…
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce unprecedentedly deep and wide photometric catalogs, enabling transformative studies of faint stellar systems such as the research of ultra-faint dwarf…
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…
Aims. We investigate the relationships between three main optical galaxy observables (spectral properties, colours, and morphology), exploiting the data set provided by the COSMOS/zCOSMOS survey. The purpose of this paper is to define a…
Although the optical colour-magnitude diagram of galaxies allows one to select red sequence objects, neither can it be used for galaxy classification without additional observational data such as spectra or high-resolution images, nor to…
The classification of galaxies as spirals or ellipticals is a crucial task in understanding their formation and evolution. With the arrival of large-scale astronomical surveys, such as the Sloan Digital Sky Survey (SDSS), astronomers now…
Bivariate luminosity functions (LFs) are computed for galaxies in the New York Value-Added Galaxy Catalogue, based on the Sloan Digital Sky Survey Data Release 4. The galaxy properties investigated are the morphological type, inverse…
We train three convolutional neural networks (CNNs) to classify galaxies with Galaxy Zoo 2 dataset and extract the activations from the last fully connected layer or the last average pooling layer of CNNs to study the high-dimensional…