Application of the neural network for particle energy reconstruction in a longitudinal sampling calorimeter

A. V. Korablev; Thomas Lindblad; A. A. Sokolov

doi:10.1117/12.343065

22 March 1999 Application of the neural network for particle energy reconstruction in a longitudinal sampling calorimeter

A. V. Korablev, Thomas Lindblad, A. A. Sokolov

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Proceedings Volume 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks; (1999) https://doi.org/10.1117/12.343065
Event: Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, 1998, Stockholm, Sweden

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.

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A. V. Korablev, Thomas Lindblad, and A. A. Sokolov "Application of the neural network for particle energy reconstruction in a longitudinal sampling calorimeter", Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); https://doi.org/10.1117/12.343065

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