Signal/Background classification in a cosmic ray space experiment by a modular neural system

In the cosmic ray space experiments, the separation of the signal from background is a hard task. Due to the well-known critical conditions that characterize this class of experiments, some changes of the detector performances can be observed during the data taking. As a consequence, differences between the test and real data are found as systematic errors in the classification phase. In this paper, a modular classification system based on neural networks is proposed for the signal/background discrimination task in cosmic ray space experiments, without a priori knowledge of the discriminating feature distributions. The system is composed by two neural modules. The first one is a self organizing map (SOM) that both clusters the real data space in suitable classes of similarity and builds a prototype for each of them; a skilled inspection of the prototypes defines the signal and background. The second one, a multi layer perceptron (MLP) with a single hidden layer, adapts the classification model based on training/test data to the real experimental conditions. The MLP synaptic weights adaptive formation takes into account the labelled real data set as defined in the first system-phase. The modular neural system has been applied in the context of TRAMP-Si experiment, performed on the NASA Balloon-Borne Magnet Facility, for the positron/proton discrimination.