Neural network for real-time particle discrimination in high-energy physics

Roberto Messi; Enrico Pasqualucci; Luciano Paoluzi; Antonio Luigi Perrone; Gianfranco Basti

doi:10.1117/12.166015

21 January 1994 Neural network for real-time particle discrimination in high-energy physics

Roberto Messi, Enrico Pasqualucci, Luciano Paoluzi, Antonio Luigi Perrone, Gianfranco Basti

Proceedings Volume 2051, International Conference on Optical Information Processing; (1994) https://doi.org/10.1117/12.166015
Event: Optical Information Processing: International Conference, 1993, St. Petersburg, Russian Federation

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.

Citation Download Citation

Roberto Messi, Enrico Pasqualucci, Luciano Paoluzi, Antonio Luigi Perrone, and Gianfranco Basti "Neural network for real-time particle discrimination in high-energy physics", Proc. SPIE 2051, International Conference on Optical Information Processing, (21 January 1994); https://doi.org/10.1117/12.166015

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available