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The study of galaxy morphology is fundamental to understand the physical processes driving the structural evolution of galaxies. It has long been known that dense environments host high fractions of early-type galaxies and low fractions of…
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…
We describe the application of the `shapelet' linear decomposition of galaxy images to multi-wavelength morphological classification using the $u,g,r,i,$ and $z$-band images of 1519 galaxies from the Sloan Digital Sky Survey. We utilize…
We use the TNG100 simulation of the IllustrisTNG project to investigate the stellar specific angular momenta ($j_{\ast}$) of $\sim$12,000 central galaxies at $z=0$ in a full cosmological context, with stellar masses ($M_{\ast}$) ranging…
We explore the nature of low surface brightness galaxies (LSBGs) in the hydrodynamic cosmological simulation TNG100 of the IllustrisTNG project, selecting a sample of LSBGs ($r$-band effective surface brightness $\mu_r > 22.0$ mag…
We study the morphologies and sizes of galaxies at z>5 using high-resolution cosmological zoom-in simulations from the Feedback In Realistic Environments project. The galaxies show a variety of morphologies, from compact to clumpy to…
We present a detailed visual morphological classification for the 4614 MaNGA galaxies in SDSS Data Release 15, using image mosaics generated from a combination of r-band (SDSS and deeper DESI Legacy Surveys) images and their digital…
We study the intrinsic 3D shapes of quiescent galaxies over the last half of cosmic history based on their axial ratio distribution. To this end, we construct a sample of unprecedented size, exploiting multi-wavelength $u$-to-$K_s$…
We train Artificial Neural Networks to classify galaxies based solely on the morphology of the galaxy images as they appear on blue survey plates. The images are reduced and morphological features such as bulge size and the number of arms…
We present a catalog of about 25,000 images of massive ($M_{\star} \ge 10^9 M_{\odot}$) galaxies at redshift $3 \leq z \leq 6$ from the TNG50 cosmological simulation, tailored for observations at multiple wavelengths carried out with JWST.…
We develop a straightforward and quantitative two-step method for spectroscopically classifying galaxies from the low signal-to-noise (S/N) optical spectra typical of galaxy redshift surveys. First, using \chi^2-fitting of characteristic…
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)…
We use rest frame ultraviolet (UV), B, and V band images of five nearby (z<0.02) interacting and/or starbursting galaxies to simulate deep HST observations of peculiar galaxies at medium to high redshifts. In particular, we simulate Hubble…
We carry out a comparative analysis of the relation between the mass of supermassive black holes (BHs) and the stellar mass of their host galaxies at $0.2<z<1.7$ using well-matched observations and multiple state-of-the-art simulations…
The Galform semi-analytic model of galaxy formation is used to explore the mechanisms primarily responsible for the three types of galaxies seen in the local universe: bulge, bulge+disk and disk, identified with the visual morphological…
We use deep learning to explore the nature of observed giant clumps in high-redshift disc galaxies, based on their identification and classification in cosmological simulations. Simulated clumps are detected using the 3D gas and stellar…
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…
Numerous studies have demonstrated the ability of Convolutional Neural Networks (CNNs) to classify large numbers of galaxies in a manner which mimics the expertise of astronomers. Such classifications are not always physically motivated,…
We contrast the gas kinematics and dark matter contents of $z=2$ star-forming galaxies (SFGs) from state-of-the-art cosmological simulations within the $\Lambda$CDM framework to observations. To this end, we create realistic mock…
Using deep near-infrared imaging of the Hubble Deep Field South with ISAAC on the Very Large Telescope we find 6 large disk-like galaxies at redshifts z = 1.4-3.0. The galaxies, selected in K_s (2.2 micron), are regular and surprisingly…