Related papers: Automated Morphological Classification of SDSS Red…
We perform a detailed photometric analysis (bulge-disk-bar decomposition and Concentration-Asymmetry-Clumpiness - CAS parametrization) for a well defined sample of isolated galaxies, extracted from the Catalog of Isolated Galaxies…
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…
Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emission-line ratios to separate galaxies.…
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…
We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy…
The morphological types of 5840 galaxies were classified by a visual inspection of color images using the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) to produce a morphology catalog of a representative sample of local galaxies with…
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 describe an automated method, the Cut & Enhance method (CE) for detecting clusters of galaxies in multi-color optical imaging surveys. This method uses simple color cuts, combined with a density enhancement algorithm, to up-weight pairs…
The Sloan Digital Sky Survey (SDSS) and photometric/spectroscopic surveys in the GOODS-South field (the Chandra Deep Field-South, CDFS) are used to construct volume-limited, stellar mass-selected samples of galaxies at redshifts 0<z<1. The…
We present a new method to classify galaxies from large surveys like the Sloan Digital Sky Survey using inclination-corrected concentration, inclination-corrected location on the color-magnitude diagram, and apparent axis ratio. Explicitly…
Morphological features in galaxies, like spiral arms, bars, rings, tidal tails etc. carry information about their structure, origin and evolution. It is therefore important to catalog and study such features and to correlate them with other…
We present a catalog of 799 clusters of galaxies in the redshift range z_est = 0.05 - 0.3 selected from ~400 deg^2 of early SDSS commissioning data along the celestial equator. The catalog is based on merging two independent selection…
In this study, we investigate the morphology of galaxies in the TNG100 simulation by applying mock observation techniques and compare the results with the observational data from the Sloan Digital Sky Survey (SDSS). By employing a…
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…
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 explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that…
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…
Classifying the morphologies of galaxies is an important step in understanding their physical properties and evolutionary histories. The advent of large-scale surveys has hastened the need to develop techniques for automated morphological…
We present a set of bulge-disk decompositions for a sample of 71,825 SDSS main-sample galaxies in the redshift range 0.003<z<0.05. We have fit each galaxy with either a de Vaucouleurs ('classical') or an exponential ('pseudo-') bulge and an…
This thesis makes use of the imaging data from the Advanced Camera for Surveys (ACS) of the Hubble Space Telescope (HST) in the Cosmic Evolution Survey (COSMOS) and the Deep Extragalactic VIsible Legacy Survey (DEVILS) field. We provide…