Soft clustering analysis of galaxy morphologies: A worked example with SDSS

  title={Soft clustering analysis of galaxy morphologies: A worked example with SDSS},
  author={Ren{\'e} Andrae and Peter Melchior and Matthias Bartelmann},
  journal={Astronomy and Astrophysics},
Context. The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need for an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover classes automatically. Aims. We briefly discuss the pitfalls of oversimplified classification methods and outline an alternative approach called “clustering analysis”. Methods. We have categorised different classification methods according to their capabilities… 
The weirdest SDSS galaxies: results from an outlier detection algorithm
How can we discover objects we did not know existed within the large datasets that now abound in astronomy? We present an outlier detection algorithm that we developed, based on an unsupervised
Parametrizing arbitrary galaxy morphologies: potentials and pitfalls
Given the enormous galaxy data bases of modern sky surveys, parametrizing galaxy morphologies is a very challenging task due to the huge number and variety of objects. We assess the different
Multivariate approaches to classification in extragalactic astronomy
This paper insists on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of the authors' classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.
Quantifying galaxy shapes: sérsiclets and beyond
Parametrization of galaxy morphologies is a challenging task, for instance in shear measurements of weak gravitational lensing or investigations of formation and evolution of galaxies. The huge
Modeling Techniques for Measuring Galaxy Properties in Multi-Epoch Surveys
Data analysis methods have always been of critical importance for quantitative sciences. In astronomy, the increasing scale of current and future surveys is driving a trend towards a separation of
Only marginal alignment of disc galaxies
Testing theories of angular-momentum acquisition of rotationally supported disc galaxies is the key to understanding the formation of this type of galaxies. The tidal-torque theory aims to explain
Multiwavelength Extragalactic Surveys: Examples of Data Mining
Examples of data mining from the multiwavelength astronomical surveys are given to provide the automated morphological galaxy classification by multiparametric diagrams and machine learning methods and to model the flux variability of galaxies with active nuclei.
Machine learning technique for morphological classification of galaxies from the SDSS
Context. Machine Learning methods are effective tools in astronomical tasks for classifying objects by their individual features. One of the promising utility is related to the morphological


A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images I. Method description
Context: Morphology is the most accessible tracer of the physical structure of galaxies, but its interpretation in the framework of galaxy evolution still remains a problem. Its dependence on
Galaxy Morphology without Classification: Self-organizing Maps
We examine a general framework for visualizing data sets of high (greater than 2) dimensionality and demonstrate the framework by taking the morphology of galaxies at moderate redshifts as an
Morphological Classification of galaxies by Artificial Neural Networks
We explore a method for automatic morphological classification of galaxies by an Artificial Neural Network algorithm. The method is illustrated using 13 galaxy parameters measured by machine
We describe the application of the "shapelet" linear decomposition of galaxy images to multiwavelength morphological classification using the u-, g-, r-, i-, and z-band images of 1519 galaxies from
Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey
We describe application of the "shapelet" linear decomposition of galaxy images to morphological classification using images of ~3000 galaxies from the Sloan Digital Sky Survey. After decomposing the
Neural computation as a tool for galaxy classification: methods and examples
We apply and compare various artificial neural network (ANN) and other algorithms for the automated morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear
Galaxies, Human Eyes, and Artificial Neural Networks
A systematic comparison of the dispersion among human experts classifying a uniformly selected sample of more than 800 digitized galaxy images, which replicates the classification by a human expert to the same degree of agreement as that between two human experts.
Color Separation of Galaxy Types in the Sloan Digital Sky Survey Imaging Data
We study the optical colors of 147,920 galaxies brighter than g* = 21, observed in five bands by the Sloan Digital Sky Survey (SDSS) over ~100 deg2 of high Galactic latitude sky along the celestial
The Relationship between Stellar Light Distributions of Galaxies and Their Formation Histories
A major problem in extragalactic astronomy is the inability to distinguish in a robust, physical, and modelindependent way how galaxy populations are physically related to each other and to their
Dimension-reduction techniques can greatly improve statistical inference in astronomy. A standard approach is to use Principal Components Analysis (PCA). In this work, we apply a recently developed