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Proceedings Paper

Neural network for real-time particle discrimination in high-energy physics
Author(s): Roberto Messi; Enrico Pasqualucci; Luciano Paoluzi; Antonio Luigi Perrone; Gianfranco Basti
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Paper Abstract

With respect to three different paradigms of neural networks generally studied: (1) the convergent one; (2) the oscillatory one; (3) the chaotic one; we propose a fourth one. In some general sense, it makes the precedent ones three particular cases of itself. The core of the approach is based on a mutual redefinition of short term memory (STM) and long term memory (LTM) able to overcome computability problems typical of pattern recognition. An application is shown about real time particle discrimination in high energy physics at ADONE e+e- storage ring in Frascati (Italy). The computational effectiveness of the proposed solution has made us able to have real-time particle discrimination in software.

Paper Details

Date Published: 21 January 1994
PDF: 14 pages
Proc. SPIE 2051, International Conference on Optical Information Processing, (21 January 1994); doi: 10.1117/12.166015
Show Author Affiliations
Roberto Messi, INFN and Univ. of Rome Tor Vergata (Italy)
Enrico Pasqualucci, INFN and Univ. of Rome Tor Vergata (Italy)
Luciano Paoluzi, INFN and Univ. of Rome Tor Vergata (Italy)
Antonio Luigi Perrone, INFN and Univ. of Rome Tor Vergata (Italy)
Gianfranco Basti, INFN and Pontifical Gregorian Univ. (Italy)


Published in SPIE Proceedings Vol. 2051:
International Conference on Optical Information Processing
Yuri V. Gulyaev; Dennis R. Pape, Editor(s)

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