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Proceedings Paper

Application of the neural network for particle energy reconstruction in a longitudinal sampling calorimeter
Author(s): A. V. Korablev; Thomas Lindblad; A. A. Sokolov
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Paper Abstract

For hadron calorimeters with a transverse structure there exists a possibility to reconstruct the particles energy with a better resolution using the neural network algorithm. For the calorimeter with a novel longitudinal structure that capability of the neural network method for the better determination of the particles energy in comparison with the traditional method was studied. The research is based on the information from the experiment at IHEP (Serpukhov) with the test (pi) --beam with energies 10, 20, 30 and 40 GeV. Using of the neural network improve the energy resolution of a system of electromagnetic calorimeter and hadron calorimeter with scintillators parallel to beam.

Paper Details

Date Published: 22 March 1999
PDF: 5 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343065
Show Author Affiliations
A. V. Korablev, Institute for High Energy Physics (Russia)
Thomas Lindblad, Royal Institute of Technology (Sweden)
A. A. Sokolov, Institute for High Energy Physics (Russia)

Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)

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