Automated Source Classification using a Kohonen Network

Preprint

4 August 1995

preprint
Published by arXiv in arXiv

https://doi.org/10.48550/arXiv.astro-ph/9508019

Abstract

We report progress in the development of automatic star/galaxy classifier for processing images generated by large galaxy surveys like APM. Our classification method is based on neural networks using the Kohonen Self-Organizing Map approach. Our method is novel, since it does not need supervised learning, i.e. human factor, in training. The analysis presented here concentrates on separating point sources (stars) from extended ones. Using simple numerical experiments we compare our method of image classification to the more traditional (PSF-fitting) approach of DAOFIND.

Keywords

SOURCE CLASSIFICATION
IMAGE CLASSIFICATION
PROCESSING IMAGES
USING THE KOHONEN
AUTOMATED SOURCE
KOHONEN NETWORK
NEURAL NETWORKS
HUMAN FACTOR

All Related Versions

Version 1, 1995-08-04, ArXiv
Published version: The Astrophysical Journal, 452 (1), L77.

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