Astronomy and Astrophysics – Astrophysics
Scientific paper
Jan 1996
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1996a%26as..115..195s&link_type=abstract
Astronomy and Astrophysics Supplement, v.115, p.195
Astronomy and Astrophysics
Astrophysics
2
Method: Data Analysis, Image Processing
Scientific paper
We propose a method to classify faint objects from digital astronomical images based on a layered feedforward neural network which has been trained by the backpropagation procedure (Werbos 1974). An "academic" example showing that artificial neural network method behaves as a Bayesian classifier is discussed. A comparison of the classification results obtained from simulated data by the neural network classifier and by the well-established resolution classifier (Valdes 1982a) is performed in order to assess the reliability and limitations of the neural network classifier. A similar behaviour, up to the same faintness limit to which the resolution classifier works, is found in both classifiers. The method proposed in this paper offers a clear advantage, in terms of speed, over traditional methods in the classification of large samples of data; it allows a uniform and objective classification of large amounts of astronomical data in short computing times, which is useful for the analysis of astronomical observations with high data rates.
Gaitan Vicens
Garrido Ll.
Perez-Fournon Ismael
Serra-Ricart Miquel
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