We extend a recently developed galaxy morphology classification method, Quantitative Multiwavelength Morphology (QMM), to connect galaxy morphologies to their underlying physical properties. The traditional classification of galaxies approaches the problem separately through either morphological classification or, in more recent times, through analysis of physical properties. A combined approach has significant potential in producing a consistent and accurate classification scheme as well as shedding light on the origin and evolution of galaxy morphology. Here we present an analysis of a volume limited sample of 31703 galaxies from the fourth data release of the Sloan Digital Sky Survey. We use an image analysis method called Pixel-z to ext...
Knowledge of the morphology of galaxies is essential in studying galaxy formation and evolution. Whe...
Aims. This work investigates the potential of using the wavelength-dependence of galaxy st...
Context. Machine learning methods are effective tools in astronomical tasks for classifying objects ...
International audienceWe extend a recently developed galaxy morphology classification method, Quanti...
We give an approach to estimate galaxy morphology from digital images. In particular, our algorithm ...
The goal of galaxy classification is to understand the physical basis for the wide range in shapes a...
Abstract: We present an application of Mathematical Morphology (MM) for the classification of astro-...
We devise improved photometric parameters for the morphological classification of galaxies using a b...
We present the morphological catalogue of galaxies in nearby clusters of the WIde-field Nearby Galax...
We present a quantitative method to classify galaxies, based on multi-wavelength data and constructe...
In this paper, we show that the determination of the morphological type could be difficult when we o...
Studying the evolution of the morphological distribution of galaxies in different environments can p...
We present two new non-parametric methods for quantifying galaxy morphology: the relative distributi...
The classification of galaxies based on their morphology is a field in astrophysics that aims to und...
Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of ga...
Knowledge of the morphology of galaxies is essential in studying galaxy formation and evolution. Whe...
Aims. This work investigates the potential of using the wavelength-dependence of galaxy st...
Context. Machine learning methods are effective tools in astronomical tasks for classifying objects ...
International audienceWe extend a recently developed galaxy morphology classification method, Quanti...
We give an approach to estimate galaxy morphology from digital images. In particular, our algorithm ...
The goal of galaxy classification is to understand the physical basis for the wide range in shapes a...
Abstract: We present an application of Mathematical Morphology (MM) for the classification of astro-...
We devise improved photometric parameters for the morphological classification of galaxies using a b...
We present the morphological catalogue of galaxies in nearby clusters of the WIde-field Nearby Galax...
We present a quantitative method to classify galaxies, based on multi-wavelength data and constructe...
In this paper, we show that the determination of the morphological type could be difficult when we o...
Studying the evolution of the morphological distribution of galaxies in different environments can p...
We present two new non-parametric methods for quantifying galaxy morphology: the relative distributi...
The classification of galaxies based on their morphology is a field in astrophysics that aims to und...
Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of ga...
Knowledge of the morphology of galaxies is essential in studying galaxy formation and evolution. Whe...
Aims. This work investigates the potential of using the wavelength-dependence of galaxy st...
Context. Machine learning methods are effective tools in astronomical tasks for classifying objects ...