Related papers: Machine learning technique for morphological class…

We present an enhanced unsupervised machine learning (UML) module within our previous \texttt{USmorph} classification framework featuring two components: (1) hierarchical feature extraction via a pre-trained ConvNeXt convolutional neural…

We propose a variant of residual networks (ResNets) for galaxy morphology classification. The variant, together with other popular convolutional neural networks (CNNs), are applied to a sample of 28790 galaxy images from Galaxy Zoo 2…

We present a classification of galaxies in the Pan-STARRS1 (PS1) 3$\pi$ survey based on their recent star formation history and morphology. Specifically, we train and test two Random Forest (RF) classifiers using photometric features…

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 detailed visual classifications for 14034 galaxies in the Sloan Digital Sky Survey (SDSS) Data Release 4 (DR4). Our sample includes nearly all spectroscopically-targeted galaxies in the redshift range 0.01<z<0.1 down…

We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within $z<1$. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the…

We use automated surface photometry and pattern classification techniques to morphologically classify galaxies. The two-dimensional light distribution of a galaxy is reconstructed using Fourier series fits to azimuthal profiles computed in…

We present a new galaxy cluster catalog constructed from SDSS DR9 using an Adaptive Matched Filter technique. Our main catalog has 46,479 galaxy clusters with richness $\Lambda_{200} > 20$ in the redshift range 0.045 $\le z <$ 0.641 in…

We have undertaken a dedicated program of automatic source classification in the WISE database merged with SuperCOSMOS scans, comprehensively identifying galaxies, quasars and stars on most of the unconfused sky. We use the Support Vector…

Galaxy morphology offers significant insights into the evolutionary pathways and underlying physics of galaxies. As astronomical data grows with surveys such as Euclid and Vera C. Rubin , there is a need for tools to classify and analyze…

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…

Testing theories of hierarchical structure formation requires estimating the distribution of galaxy morphologies and its change with redshift. One aspect of this investigation involves identifying galaxies with disturbed morphologies (e.g.,…

It is difficult to accurately identify galaxy mergers and it is an even larger challenge to classify them by their mass ratio or merger stage. In previous work we used a suite of simulated mergers to create a classification technique that…

Aims. We explore machine learning techniques to forecast star formation rate, stellar mass, and metallicity across galaxies with redshifts ranging from 0.01 to 0.3. Methods. Leveraging CatBoost and deep learning architectures, we utilize…

Mass loss is a key aspect of stellar evolution, particularly in evolved massive stars, yet episodic mass loss remains poorly understood. To investigate this, we need evolved massive stellar populations across various galactic environments.…

We study the morphology of luminous and massive galaxies at 0.3<z<0.7 targeted in the Baryon Oscillation Spectroscopic Survey (BOSS) using publicly available Hubble Space Telescope imaging from COSMOS. Our sample (240 objects) provides a…

Using the galaxy data of the Sloan Digital Sky Survey Data Release 8 (SDSS DR8), I explore whether the concentration index is a good morphological classification tool and find that a reasonably pure late-type galaxy sample can be…

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,…

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 describe application of the `shapelet' linear decomposition of galaxy images to morphological classification using images of $\sim$ 3000 galaxies from the Sloan Digital Sky Survey. After decomposing the galaxies we perform a principal…