Related papers: MegaMorph: classifying galaxy morphology using mul…
Star-galaxy separation is a crucial step in creating target catalogues for extragalactic spectroscopic surveys. A classifier biased towards inclusivity risks including spurious stars, wasting fibre hours, while a more conservative…
We analyze the Near Infrared ($\sim0.8-1\mu$m) rest-frame morphologies of galaxies with $\log M_*/M_\odot>9$ in the redshift range $0<z<6$, compare with previous HST-based results and release the first JWST-based morphological catalog of…
We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21,…
Extragalactic surveys provide significant statistical data for the study of crucial galaxy parameters used to constrain galaxy evolution, e.g. stellar mass (M$_*$) and star formation rate (SFR), under different environmental conditions.…
Morphological classification conveys abundant information on the formation, evolution, and environment of galaxies. In this work, we refine the two-step galaxy morphological classification framework ({\tt\string USmorph}), which employs a…
(abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss. To address this we need increased numbers of classified…
We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric…
The correlation between a galaxy's morphology and its observed optical spectrum is investigated. As an example, 4000 galaxies from the 2dF Galaxy Redshift Survey, which possess both good quality spectra and have visually determined…
We investigate the photometric properties of 456 bright galaxies using imaging data recorded during the commissioning phase of the Sloan Digital Sky Survey (SDSS). Morphological classification is carried out by correlating results of…
The morphology of a galaxy has been shown to encode the evolutionary history and correlates strongly with physical properties such as stellar mass, star formation rates and past merger events. While the majority of galaxies in the local…
In this paper we define an observationally robust, multi-parameter space for the classification of nearby and distant galaxies. The parameters include luminosity, color, and the image-structure parameters: size, image concentration,…
Our aim in this work is to answer, using simulated narrow-band photometry data, the following general question: What can we learn about galaxies from these new generation cosmological surveys? For instance, can we estimate stellar age and…
We analyze 347 galaxies at redshift $4<z<9.5$ using JWST observations from the CEERS program by fitting a two-dimensional parametric model simultaneously to the seven-filter NIRCam images to measure the overall structural parameters and…
We present a nonparametric morphology analysis of the stellar continuum and nebular emission lines for a sample of local galaxies. We explore the dependence of the various morphological parameters on wavelength and morphological type. Our…
Context: Galaxy morphology is a fundamental property to describe galaxy evolution. However, the observed morphology of a particular galaxy may depend on the observed wavelength. Aims: Our aim is to investigate the wavelength dependence and…
We present a method for automated classification of galaxies with low signal-to-noise (S/N) spectra typical of redshift surveys. We develop spectral simulations based on the parameters for the 2dF Galaxy Redshift Survey, and with these…
Upcoming large imaging surveys will allow detailed studies of the structure and morphology of galaxies aimed at addressing how galaxies form and evolve. Computational approaches are needed to characterize their morphologies over large…
We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift…
With a large sample of bright, low-redshift galaxies with optical$-$near-IR imaging from the GAMA survey we use bulge-disc decompositions to understand the wavelength-dependent behavior of single-S\'ersic structural measurements. We denote…
[abridged]We calculate multi-wavelength spectral energy distributions (SEDs) (spanning optical to millimeter wavelengths) from simulations of major galaxy mergers with black hole feedback which produce submillimeter bright galaxies (SMGs),…