The use of neural networks for the automatic detection and classification of weak photometric sub-components in early-type galaxies

  • G. Di Sciascio
  • G. Longo
  • R. Tagliaferri
  • M. Capaccioli
  • G. Richter
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

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.

Keywords

Dust Convolution sinO 

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Copyright information

© Springer-Verlag London Limited 1997

Authors and Affiliations

  • G. Di Sciascio
    • 1
  • G. Longo
    • 1
  • R. Tagliaferri
    • 2
  • M. Capaccioli
    • 1
  • G. Richter
    • 3
  1. 1.Osservatorio Astronomico di CapodimonteItaly
  2. 2.Dipartimento di Informatica ed ApplicazioniUniversità di Salerno and INFMItaly
  3. 3.Astrophysikalisches InstitutGermany

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