Related papers: Revisiting the SFR-Mass relation at z=0 with detai…

We present the results from adaptive optics (AO) assisted imaging in the Ks band of an area of 15 arcmin^2 for SWAN (Survey of a Wide Area with NACO). We derive the high resolution near-IR morphology of ~400 galaxies up to Ks~23.5 in the…

We present the morphological catalog of galaxies in nearby clusters of the WINGS survey (Fasano et al. 2006). The catalog contains a total number of 39923 galaxies, for which we provide the automatic estimates of the morphological type…

The ``MORPHS'' group has completed the cataloging, parameterization, and morphological classification of ~2000 galaxies in 10 rich clusters from 0.36 < z < 0.56. From a weak lensing analysis using these data, which compares the X-ray…

A non-zero mutual information between morphology of a galaxy and its large-scale environment is known to exist in SDSS upto a few tens of Mpc. It is important to test the statistical significance of these mutual information if any. We…

We examine a general framework for visualizing datasets of high (> 2) dimensionality, and demonstrate it using the morphology of galaxies at moderate redshifts. The distributions of various populations of such galaxies are examined in a…

We report the results of a comprehensive study of the relationship between galaxy size, stellar mass and specific star-formation rate (sSFR) at redshifts 1.3<z<1.5. Based on a mass complete (M_star >= 6x10^10 Msun), spectroscopic sample…

The morphology of galaxies provide us with a unique tool for relating and understanding other physical properties and their changes over the course of cosmic time. It is only recently that we have been afforded access to a wealth of data…

Modern data empower observers to describe galaxies as the spatially and biographically complex objects they are. We illustrate this through case studies of four, $z\sim1.3$ systems based on deep, spatially resolved, 17-band + G102 + G141…

In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. In particular, the CART, the C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable…

We use \sim 2000 galaxies belonging to different environments to show how the fractions of different galaxy morphological types vary with global environment and as function of galaxy stellar mass at low redshift. Considering mass limited…

Galaxy morphology is inextricably linked to environment. The morphology-density relation quantifies this relationship. However, optical morphology is only loosely related to the kinematic structure of galaxies, and about two thirds of…

We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modified versions of the CASGM coefficients (Concentration $C_1$, Asymmetry $A_3$, and…

The two-step galaxy morphology classification framework {\tt USmorph} successfully combines unsupervised machine learning (UML) with supervised machine learning (SML) methods. To enhance the UML step, we employed a dual-encoder architecture…

We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric…

We analyze the dependence of galaxy structure (size and Sersic index) and mode of star formation (\Sigma_SFR and SFR_IR/SFR_UV) on the position of galaxies in the SFR versus Mass diagram. Our sample comprises roughly 640000 galaxies at…

Galaxy morphologies provide valuable insights into their formation processes, tracing the spatial distribution of ongoing star formation and encoding signatures of dynamical interactions. While such information has been extensively…

The morphology of a galaxy stems from secular and environmental processes during its evolutionary history. Thus galaxy morphologies have been a long used tool to gain insights on galaxy evolution. We visually classify morphologies on…

We analyze the optical morphologies of galaxies in the IllustrisTNG simulation at $z\sim0$ with a Convolutional Neural Network trained on visual morphologies in the Sloan Digital Sky Survey. We generate mock SDSS images of a mass complete…

We present measurements of the specific star-formation rate (SSFR)-stellar mass relation for star-forming galaxies. Our deep spectroscopic samples are based on the Redshift One LDSS3 Emission line Survey, ROLES, and European Southern…

We investigate mass-dependent galaxy evolution based on a large sample of (more than 50,000) K-band selected galaxies in a multi-wavelength catalog of the Subaru/XMM-Newton Deep Survey (SXDS) and the UKIRT Infrared Deep Sky Survey…