This work presents an approach to discriminate between neutrons and /spl gamma/-rays in nuclear fusion applications, based on a neural network able to analyze the shape of light pulses produced by these ionizing particles in an organic liquid scintillator. Such an approach is particularly promising especially for the possibility of classifying correctly (either as neutrons or as /spl gamma/-rays) fast superimposed events (pile-ups). Satisfactory experimental results were obtained at the Frascati Tokamak Upgrade, ENEA-Frascati, Italy.
A Neural System for Radiation Discrimination in Nuclear Fusion Applications / Esposito, B.; Fortuna, L.; Rizzo, Alessandro. - (2004), pp. 776-779. (Intervento presentato al convegno IEEE International Symposium on Circuits and Systems, ISCAS '04 tenutosi a Vancouver, Canada nel May 23-26, 2004) [10.1109/ISCAS.2004.1329923].
A Neural System for Radiation Discrimination in Nuclear Fusion Applications
RIZZO, Alessandro
2004-01-01
Abstract
This work presents an approach to discriminate between neutrons and /spl gamma/-rays in nuclear fusion applications, based on a neural network able to analyze the shape of light pulses produced by these ionizing particles in an organic liquid scintillator. Such an approach is particularly promising especially for the possibility of classifying correctly (either as neutrons or as /spl gamma/-rays) fast superimposed events (pile-ups). Satisfactory experimental results were obtained at the Frascati Tokamak Upgrade, ENEA-Frascati, Italy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
