Related papers: Galaxy Zoo: Quantitative Visual Morphological Clas…

We present the galaxy-galaxy angular correlations as a function of photometric redshift in a deep-wide galaxy survey centered on the Hubble Deep Field South. Images were obtained with the Big Throughput Camera on the Blanco 4m telescope at…

We have studied the evolution in the morphologies, sizes, stellar-masses, colors, and internal color dispersion (ICD) of galaxies at z=1 and 2.3, using a near-IR, flux-limited catalog for the HDF-N. At z=1 most luminous galaxies have…

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…

Galaxy morphology is one of the most fundamental ways to describe galaxy properties, but the morphology we observe may be affected by wavelength and spatial resolution, which may introduce systematic bias when comparing galaxies at…

We study the clustering of galaxies detected at $i<22.5$ in the Science Verification observations of the Dark Energy Survey (DES). Two-point correlation functions are measured using $2.3\times 10^6$ galaxies over a contiguous 116 deg$^2$…

Morphology is often used to infer the state of relaxation of galaxy clusters. The regularity, symmetry, and degree to which a cluster is centrally concentrated inform quantitative measures of cluster morphology. The Cluster Lensing and…

The populations of both quiescent and actively star-forming galaxies at 1<z<2 are still under-represented in our spectroscopic census of galaxies throughout the history of the Universe. In the light of galaxy formation models, however, the…

Following the study of Darg et al. (2009; hereafter D09a) we explore the environments, optical colours, stellar masses, star formation and AGN activity in a sample of 3003 pairs of merging galaxies drawn from the SDSS using visual…

This paper begins a series in which we examine the structures of distant galaxies to directly determine the history of their formation modes. We start this series by examining the structures of z_F850LP < 27 galaxies in the Hubble…

We present a revaluation of the optical morphology for 549 galaxies from the Catalog of Isolated Galaxies (Karachentseva 1973) that are available in the SDSS (DR6). Both the high resolution and high dynamic range of the SDSS images and our…

We introduce a continuous depth version of the Residual Network (ResNet) called Neural ordinary differential equations (NODE) for the purpose of galaxy morphology classification. We carry out a classification of galaxy images from the…

Edge-on galaxies have many important applications in galactic astrophysics, but they can be difficult to identify in vast amounts of astronomical data. To facilitate the search for them, we have developed a deep learning algorithm designed…

The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation. However, in large sky surveys, even the morphological classification of galaxies into two classes, like late-type (LT) and…

Spectroscopic + photometric redshifts, stellar mass estimates, and rest-frame colors from the 3D-HST survey are combined with structural parameter measurements from CANDELS imaging to determine the galaxy size-mass distribution over the…

The role of major mergers in galaxy evolution remains a key open question. Existing empirical merger identification methods use non-parametric and subjective visual classifications which can pose systematic challenges to constraining merger…

We measure the morphology--density relation (MDR) and morphology-radius relation (MRR) for galaxies in seven z ~ 1 clusters that have been observed with the Advanced Camera for Surveys on board the Hubble Space Telescope. Simulations and…

We have used the Hubble Space Telescope's Advanced Camera for Surveys (Ford et al. 2003) to measure the cumulative mass density in morphologically-selected early-type galaxies over the redshift range 0.8 < z < 1.7. Our imaging data set…

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 classification of galaxy morphology plays a crucial role in understanding galaxy formation and evolution. Traditionally, this process is done manually. The emergence of deep learning techniques has given room for the automation of this…

Classification of galaxies is traditionally associated with their morphologies through visual inspection of images. The amount of data to come renders this task inhuman and Machine Learning (mainly Deep Learning) has been called to the…