This work deals with the discrimination of neutrons and gamma-rays on the basis of their different pulse shapes in scintillator detectors; this technique is widely employed in nuclear fusion applications. After a thorough phase of data analysis, a Multi Layer Perceptron (MLP) is trained with the aim of processing the shape of light pulses produced by these ionizing particles in an organic liquid scintillator and digitally acquired. Moreover, fast superimposed events (called pile-ups) are detected and a further MLP is trained to analyze them and recover the original superimposed events. Satisfactory experimental results were obtained at the Frascati Tokamak Upgrade, ENEA-Frascati, Italy.
|Publication status||Published - 2004|
|Event||2004 IEEE International Joint Conference on Neural Networks - Proceedings - , Hungary|
Duration: 1 Jan 2004 → …
|Conference||2004 IEEE International Joint Conference on Neural Networks - Proceedings|
|Period||1/1/04 → …|
All Science Journal Classification (ASJC) codes
Esposito, B., Fortuna, L., & Rizzo, A. (2004). Neural neutron/gamma discrimination in organic scintillators for fusion applications. Paper presented at 2004 IEEE International Joint Conference on Neural Networks - Proceedings, Hungary. https://doi.org/10.1109/IJCNN.2004.1381130