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Related papers: The Hubble Sequence at $z\sim0$ in the IllustrisTN…

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The connections among galaxies, the dark matter halos where they form and the properties of the large-scale Cosmic Web still need to be completely disentangled. We use the cosmological hydrodynamical simulation TNG100 of the IllustrisTNG…

We present a catalog of visual like H-band morphologies of $\sim50.000$ galaxies ($H_{f160w}<24.5$) in the 5 CANDELS fields (GOODS-N, GOODS-S, UDS, EGS and COSMOS). Morphologies are estimated with Convolutional Neural Networks (ConvNets).…

The advent of the Hubble Space Telescope (HST) has provided images of galaxies at moderate and high redshifts and changed the scope of galaxy morphologies considerably. It is evident that the Hubble Sequence requires modifications in order…

Astrophysics · Physics 2016-08-30 A. Naim , K. U. Ratnatunga , R. E. Griffiths

We present the analysis of a sample of twenty-four SLACS-like galaxy-galaxy strong gravitational lens systems with a background source and deflectors from the Illustris-1 simulation. We study the degeneracy between the complex mass…

Astrophysics of Galaxies · Physics 2020-05-27 W. Enzi , S. Vegetti , G. Despali , J. -W. Hsueh , R. B. Metcalf

Galaxies are theorized to form and co-evolve with their dark matter halos, such that their stellar masses and halo masses should be well-correlated. However, it is not known whether other observable galaxy features, such as their…

Cosmology and Nongalactic Astrophysics · Physics 2024-07-19 Austin J. Larson , John F. Wu , Craig Jones

We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is the compact star-forming phase in the…

This work is focused on the morphological classification of galaxies following the Hubble sequence in which the different classes are arranged in a hierarchy. The proposed method, BCNN, is composed of two main modules. First, a…

Instrumentation and Methods for Astrophysics · Physics 2024-09-04 Jonathan Serrano-Pérez , Raquel Díaz Hernández , L. Enrique Sucar

Because early black holes (BHs) grew to $\sim10^{9} ~M_\odot$ in less than 1 Gyr of cosmic time, BH seeding models face stringent constraints. To efficiently constrain the parameter space of possible seeding criteria, we combine the…

Astrophysics of Galaxies · Physics 2023-09-21 Analis Eolyn Evans , Laura Blecha , Aklant Kumar Bhowmick

The Canada-France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ~5000 square degrees of the sky, representing a first-rate opportunity to identify recently-merged galaxies. Due to the large number of…

The recently discovered discrepancy between galaxy mass measurements from weak lensing and predictions from abundance matching questions our understanding of cosmology, or of the galaxy-halo connection, or of both. We re-examined this…

Cosmology and Nongalactic Astrophysics · Physics 2019-06-05 Jacob Svensmark , Davide Martizzi , Adriano Agnello

We present a panoramic view of several scaling relations (ScRs) of galaxies of different morphology. The ScRs are obtained from the data of two large surveys (WINGS and MANGA). We analyze the distribution (parameterized by the percent over…

Astrophysics of Galaxies · Physics 2026-01-26 Mauro D'Onofrio , Francesco Brevi , Cesare Chiosi , Paola Marziani

We investigate the consistency of visual morphological classifications of galaxies by comparing classifications for 831 galaxies from six independent observers. The galaxies were classified on laser print copy images or on computer screen…

We take a deep learning-based approach for galaxy merger identification in Subaru HSC-SSP, specifically through the use of deep representation learning and fine-tuning, with the aim of creating a pure and complete merger sample within the…

We present the topology of a volume-limited sample of 11,884 galaxies, selected from an apparent-magnitude limited sample of over 100,000 galaxies observed as part of the Sloan Digital Sky Survey (SDSS). The data currently cover three main…

The origin of diverse kinematic morphologies observed in low-mass galaxies is unclear. In this study, we investigate the kinematic morphologies of central galaxies with stellar mass $10^{8.5-9.0} M_\odot$ at $z=0$ in the TNG50-1…

Astrophysics of Galaxies · Physics 2024-07-04 Guangquan Zeng , Lan Wang , Liang Gao , Hang Yang

We use a combination of self-supervised machine learning and visual classification to identify tidal features in a sample of 34,331 galaxies with stellar masses $\log_{10}(M_{*}/\rm{M}_{\odot})\geq9.5$ and redshift $z\leq0.4$, drawn from…

Astrophysics of Galaxies · Physics 2025-09-24 A. Desmons , S. Brough , F. Lanusse , L. Canepa , A. Khalid

Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any…

Astrophysics of Galaxies · Physics 2019-06-12 W. J. Pearson , L. Wang , J. W. Trayford , C. E. Petrillo , F. F. S. van der Tak

We use the cosmological hydrodynamical simulation TNG50 to study the galaxy mass-morphology relation, as measured by the rotational support of the stellar component of simulated galaxies. For isolated galaxies with stellar mass in the range…

Astrophysics of Galaxies · Physics 2025-06-25 Bruno M. Celiz , Julio F. Navarro , Mario G. Abadi , Volker Springel

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…

We use the IllustrisTNG (TNG) simulations to explore the galaxy-halo connection as inferred from state-of-the-art cosmological, magnetohydrodynamical simulations. With the high mass resolution and large volume achieved by combining the 100…

Cosmology and Nongalactic Astrophysics · Physics 2019-09-25 Sownak Bose , Daniel J. Eisenstein , Lars Hernquist , Annalisa Pillepich , Dylan Nelson , Federico Marinacci , Volker Springel , Mark Vogelsberger