Paper
6 April 1995 Signal/background classification in a cosmic ray space experiment by a modular neural system
Roberto Bellotti, Marcello Castellano, Carlo Nicola De Marzo, Giuseppe Satalino
Author Affiliations +
Abstract
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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Roberto Bellotti, Marcello Castellano, Carlo Nicola De Marzo, and Giuseppe Satalino "Signal/background classification in a cosmic ray space experiment by a modular neural system", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205112
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Cited by 2 scholarly publications.
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KEYWORDS
Prototyping

Classification systems

Data modeling

Particles

Sensors

Neurons

Radon

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