The use of neural networks for the automatic detection and classification of weak photometric sub-components in early-type galaxies
Weak photometric subcomponents offer an important tool to understand the present structure and past evolution of early-type galaxies. The detection of such structures can be performed either by detailed modeling of the 2-d light distribution or by means of expecially tailored filtering techniques such as, for instance, the Adaptive Laplacian Algorithm. In this paper we present vive application of a Multi Layer Perceptron and a Self-organizing neural nets to the detection and classification of some Laplacian morphologies. In particular we discuss the construction of a training set of images of artificial galaxies and some preliminary results.
KeywordsPoint Spread Function Light Distribution Polar Ring Laplacian Image Luminosity Ratio
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