Fuzzy classifier for star-galaxy separation

Petri Mähönen (Corresponding Author), Tapio Frantti

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)


The sizes of astronomical surveys are increasing rapidly. Hence, the automatic classification of objects is growing more important. This classification is traditionally based, e.g., on point-spread function fitting. Recently several different neural network approaches have been introduced. In this paper we introduce a simple method that is based on fuzzy set reasoning. The analysis presented here concentrates on separating point sources (stars) from extended ones. The tests show that the neural network approach is superior if compared to direct fuzzy classification. The paper shows that the inherent ability of neural networks to process complex nonlinear data justifies the use of them in astronomical classification. However, a combined fuzzy and neural network approach can be useful at least in special cases.

Original languageEnglish
Pages (from-to)261-264
JournalThe Astrophysical journal
Issue number1
Publication statusPublished - 2000
MoE publication typeA1 Journal article-refereed


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