Abstract
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 language | English |
|---|---|
| Pages (from-to) | 261 - 264 |
| Number of pages | 4 |
| Journal | The Astrophysical journal |
| Volume | 541 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2000 |
| MoE publication type | A1 Journal article-refereed |
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Fuzzy classifier for star-galaxy separation. / Mähönen, Petri (Corresponding Author); Frantti, Tapio.
In: The Astrophysical journal, Vol. 541, No. 1, 2000, p. 261 - 264.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Fuzzy classifier for star-galaxy separation
AU - Mähönen, Petri
AU - Frantti, Tapio
PY - 2000
Y1 - 2000
N2 - 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.
AB - 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.
U2 - 10.1086/309424
DO - 10.1086/309424
M3 - Article
VL - 541
SP - 261
EP - 264
JO - The Astrophysical journal
JF - The Astrophysical journal
SN - 0004-637X
IS - 1
ER -