Transient Signal Detection with Neural Networks: The Search for the Desired Signal

Matched filtering has been one of the most powerful techniques employed for transient detection. Here we will show that a dynamic neural network outperforms the conventional approach. When the artificial neural network (ANN) is trained with supervised learning schemes there is a need to supply the desired signal for all time, although we are only interested in detecting the transient. In this paper we also show the effects on the detection agreement of different strategies to construct the desired signal. The extension of the Bayes decision rule (0/1 desired signal), optimal in static classification, performs worse than desired signals constructed by random noise or prediction during the background.

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