Support Vector Machines and CASGM20 Parameters Applied to Morphological Classification of Reconstructed 2D Images of Extended Objects Within the ESA-Gaia Mission

Details Support Vector Machines and CASGM20 Parameters Applied to Morphological Classification of Reconstructed 2D Images of Extended Objects Within the ESA-Gaia Mission Support Vector Machines and CASGM20 Parameters Applied to Morphological Classification of Reconstructed 2D Images of Extended Objects Within the ESA-Gaia Mission

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008aipc.1082..151k&link_type=abstract

CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: ``Classification and Di

Computer Science

Databases

Image Processing, Galactic Center, Bar, Circumnuclear Matter, And Bulge, Astronomical Catalogs, Atlases, Sky Surveys, Databases, Retrieval Systems, Archives, Etc.

Scientific paper

In this work we present some parameters that are being studied to perform a purely morphological analysis of reconstructed images of extended objects, particularly galaxies, in the context of the ESA-Gaia mission. Those parameters, known as Concentration, Asymmetry, Clumpiness, Gini's coefficient and the Momentum of the brightest 20% of the galaxy, form a set that is becoming commonly used when a limited number of pixels is available to analyse, such as will be the case for Gaia reconstructed images. We comment about small modifications on those parameters that are planned to be performed. We also report tests with a preliminar version of the code that is being written to analyse Gaia images on a sample based on the Frei catalog of galaxies. Finally, we comment on the possibility of using Support Vector Machines to perform the morphological classification based on those measured parameters, and conclude that a very good level of segregation can be obtained for a two-class discrimination.

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