by Raluca Muresan, Mircea Pentia
http://www.quark.lu.se/~raluca/diploma.ps
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Abstract:
This work uses a neural network technique (Hopfield method) in order to reconstruct particle tracks starting from a data set obtained with a coordinate detector system, placed around a high energy accelerated particle interaction region. A learning algorithm for finding the optimal connections of the signal points, have been elaborated and tested. We used a single layer neural network with constraints in order to obtain the particle tracks drawn through the detected signal points. The dynamics of the system is given by the MFT equations which determine the system evolution to a minimum of the associated energy function. We carried out a computing program that has been tested on a lot of Monte Carlo simulated data. With this program we have obtained good results even for noise/signal ratio 200
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