Using backpropagation to reckon with discrete and continuous signals from a silicon calorimeter

Gianfranco Basti; Patrizia Castiglione; Marco Casolino; Antonio Luigi Perrone; Piergiorgio Picozza; Aldo Morselli

doi:10.1117/12.169998

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2 March 1994 Using backpropagation to reckon with discrete and continuous signals from a silicon calorimeter

Gianfranco Basti, Patrizia Castiglione, Marco Casolino, Antonio Luigi Perrone, Piergiorgio Picozza, Aldo Morselli

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Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.169998
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

We want to present a further development of our technique of backpropagation with stochastic preprocessing to recognize particle tracks in a silicon calorimeter on a satellite to detect cosmic ray composition. In the first release we applied our technique to distinguish between two classes of discrete patterns. In the present release we developed the stochastic preprocessing to deal with continuous patterns such as the energy deposited by a cosmic particle. From the theoretical standpoint we demonstrate that by such a preprocessing technique the neural net is able to represent the complexity of learning set in a polynomial and not exponential time. This work is a part of `Skynnet' international project supported by INFN (National Institute for Nuclear Physics) and partially devoted to the application of neural techniques for recognition of high energy particle tracks in spatial environment.

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Gianfranco Basti, Patrizia Castiglione, Marco Casolino, Antonio Luigi Perrone, Piergiorgio Picozza, and Aldo Morselli "Using backpropagation to reckon with discrete and continuous signals from a silicon calorimeter", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.169998

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