ASTErIsM - Application of topometric clustering algorithms in automatic galaxy detection and classification

Abstract

We present a study on galaxy detection and shape classification using topometric clustering algorithms. We first use the DBSCAN algorithm to extract, from CCD frames, groups of adjacent pixels with significant fluxes and we then apply the DENCLUE algorithm to separate the contributions of overlapping sources. The DENCLUE separation is based on the localization of pattern of local maxima, through an iterative algorithm which associates each pixel to the closest local maximum. Our main classification goal is to take apart elliptical from spiral galaxies. We introduce new sets of features derived from the computation of geometrical invariant moments of the pixel group shape and from the statistics of the spatial distribution of the DENCLUE local maxima patterns. Ellipticals are characterized by a single group of local maxima, related to the galaxy core, while spiral galaxies have additional ones related to segments of spiral arms. We use two different supervised ensemble classification algorithms, Random Forest, and Gradient Boosting. Using a sample of ~ 24000 galaxies taken from the Galaxy Zoo 2 main sample with spectroscopic redshifts, and we test our classification against the Galaxy Zoo 2 catalog. We find that features extracted from our pipeline give on average an accuracy of ~ 93%, when testing on a test set with a size of 20% of our full data set, with features deriving from the angular distribution of density attractor ranking at the top of the discrimination power.

20 pages, 13 Figures, 8 Tables, Accepted for publication in the Monthly Notices of the Royal Astronomical Society

References in corpus (26)

• Scikit-learn: Machine Learning in Python
• scikit-image: Image processing in Python
• The Relationship Between Stellar Light Distributions of Galaxies and their Formation Histories
• A New Non-Parametric Approach to Galaxy Morphological Classification
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Galaxy Zoo 1 : Data Release of Morphological Classifications for nearly 900,000 galaxies
• Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey
• Galaxy Zoo: the dependence of morphology and colour on environment
• A Catalog of Detailed Visual Morphological Classifications for 14034 Galaxies in the Sloan Digital Sky Survey
• The Atlas3D project - VII. A new look at the morphology of nearby galaxies: the kinematic morphology-density relation
• Formation of early-type galaxies from cosmological initial conditions
• The internal structure and formation of early-type galaxies: the gravitational--lens system MG2016+112 at z=1.004
• The Structures of Distant Galaxies I: Galaxy Structures and the Merger Rate to z~3 in the Hubble Ultra-Deep Field
• Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available bayesian automated classification
• Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning
• The EFIGI catalogue of 4458 nearby galaxies with detailed morphology
• Random forest automated supervised classification of Hipparcos periodic variable stars
• CANDELS Visual Classifications: Scheme, Data Release, and First Results
• A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description
• AUTOMATED MORPHOLOGICAL CLASSIFICATION OF APM GALAXIES BY SUPERVISED ARTIFICIAL NEURAL NETWORKS
• Tidal Streams of Intracluster Light
• Neural computation as a tool for galaxy classification : methods and examples
• The Evolution of the Field and Cluster Morphology-Density Relation for Mass-Selected Samples of Galaxies
• Using Oblique Decision Trees for the Morphological Classification of Galaxies
• gamma-ray DBSCAN: a clustering algorithm applied to Fermi-LAT gamma-ray data. I. Detection performances with real and simulated data
• A Clustering Analysis of the Morphology of the 130 GeV Gamma-Ray Feature

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• Euclid. I. Overview of the Euclid mission
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• The Euclid Data Processing Challenges
• High energy gamma-ray detection of supernova remnants in the Large Magellanic Cloud
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• Search for $\gamma$-ray emission from SNRs in the Large Magellanic Cloud: a new cluster analysis at energies above 4 GeV
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