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We present the first automated spectroscopic search for disk-galaxy lenses, using the Sloan Digital Sky Survey database. We follow up eight gravitational lens candidates, selected among a sample of ~40000 candidate massive disk galaxies,…

Using imaging data from the SDSS survey, we present the g' and r' radial stellar light distribution of a complete sample of ~90 face-on to intermediate inclined, nearby, late-type (Sb-Sdm) spiral galaxies. The surface brightness profiles…

In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep…

We present an automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network which is easy to use and provides good accuracy. In our study we use a sample of 9346 galaxies in the…

An all-sky list of 88 nearby dwarf spheroidal (dSph) galaxies with distances D < 10 Mpc is considered. Most of the objects have recently been found by Karachentseva & Karachentsev based on POSSII/ESO-SERC survey. A hundred more dSph…

Flattened bulges with disk-like properties are considered to be the end product of secular evolution processes at work in the inner regions of galaxies. On the contrary, classical bulges are characterized by rounder shapes and thought to be…

We present the first identification of large-scale structures (LSS) at z $< 1.1$ in the Cosmic Evolution Survey (COSMOS). The structures are identified from adaptive smoothing of galaxy counts in the pseudo-3d space ($\alpha,\delta$,z)…

We present a comprehensive analysis of the evolution of the morphological and structural properties of a large sample of galaxies at z=3-9 using early JWST CEERS NIRCam observations. Our sample consists of 850 galaxies at z>3 detected in…

We investigate the effects of potential sources of systematic error on the angular and photometric redshift, z_phot, distributions of a sample of redshift 0.4 < z < 0.7 massive galaxies whose selection matches that of the Baryon Oscillation…

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…

Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable…

Multiple observations made by several different telescopes have shown asymmetry between the number of spiral galaxies rotating in opposite directions in different parts of the sky. One of the immediate questions regarding the possible…

The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS)…

The presence of a well-defined and narrow dust lane in an edge-on spiral galaxy is the observational signature of a thin and dense molecular disk, in which gravitational collapse has overcome turbulence. Using a sample of galaxies out to…

[abridged] New near-infrared surveys, using the HST, offer an unprecedented opportunity to study rest-frame optical galaxy morphologies at z>1 and to calibrate automated morphological parameters that will play a key role in classifying…

Large-scale structures (LSS) out to z $< 3.0$ are measured in the Cosmic Evolution Survey (COSMOS) using extremely accurate photometric redshifts (photoz). The Ks-band selected sample (from Ultra-Vista) is comprised of 155,954 galaxies. Two…

In order to understand the formation and subsequent evolution of galaxies one must first distinguish between the two main morphological classes of massive systems: spirals and early-type systems. This paper introduces a project, Galaxy Zoo,…

Galaxy morphology is a key parameter in galaxy evolution studies. The enormous number of galaxies which current and future surveys will observe demand of automated methods for morphological classification. Supervised learning techniques…

Deep neural networks (DNNs) with a step-by-step introduction of inputs, which is constructed by imitating the somatosensory system in human body, known as SpinalNet have been implemented in this work on a Galaxy Zoo dataset. The input…

We explore how information in images of nearby galaxies can be used to estimate their distance. We train a convolutional Neural Network (NN) to do this, using galaxy images from the Illustris simulation. We show that if the NN is trained on…