Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
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The application of supervised artificial neural networks (ANNs) for quasar selection is investigated, using the list of candidates and their classification from White et al. (2000). The adopted architectures are 7:1 and 7:2:1, both with seven input parameters - optical and radio data from APM POSS-I (E, O plates) and VLA/FIRST, and a single output interpreted as a quasar probability. Both models were trained on samples of ∼ 800 sources and yielded similar performance on independent test samples, with reliability as large as 90 to 80% for completeness from 70 to 90%. For comparison, the quasar fraction from the original list of candidates was 56%. The accuracy found with ANNs is similar to that obtained by White et al. using oblique decision trees and training samples of similar size. In view of the large degree of overlapping between quasars and nonquasars in parameter space, this performance is probably the best that can be achieved with this database. Predictions of the probabilities for the 98 candidates without spectroscopic classification in White et al. are presented, showing a good agreement between the two ANN models and with the values obtained by White et al. Eight of these sources have recent spectroscopic classification from the NASA Extragalactic Database or from the Sloan Digital Sky Survey Data Release 2 and the classes are consistent with their probabilities, reinforcing the ability of ANNs to optimize the selection of quasars. This work presents the first analysis of the performance of ANNs for quasar selection and it shows that ANNs provide a promising technique to single out specific object types in astronomical databases. An article with the full description of this work has been accepted for publication in Monthly Notices of the Royal Astronomical Society ("Selection of quasar candidates from combined radio and optical survey", Carballo, Cofiño and González-Serrano. © 2004. The Royal Astronomical Society).