Application of Neural Networks for Energy Reconstruction

Abstract

The possibility to use Neural Networks for reconstruction of the energy deposited in the calorimetry system of the CMS detector is investigated. It is shown that using feed - forward neural network, good linearity, Gaussian energy distribution and good energy resolution can be achieved. Significant improvement of the energy resolution and linearity is reached in comparison with other weighting methods for energy reconstruction.

18 pages, 13 figures, LATEX, submitted to: Nuclear Instruments & Methods A

References in corpus (2)

• Studies of the Response of the Prototype CMS Hadron Calorimeter, Including Magnetic Field Effects, to Pion, Electron, and Muon Beams
• Application of neural network for photon-hadron discrimination in a preshower detector in high energy heavy ion collisions

Cited by in corpus (5)

• Comparison of Neural Network and Hadronic Model Predictions of Two-Photon Exchange Effect
• Applications of Neural Networks in Hadron Physics
• ANN-based energy reconstruction procedure for TACTIC gamma-ray telescope and its comparison with other conventional methods
• Neural Network Parameterizations of Electromagnetic Nucleon Form Factors
• Detector Optimization Studies and Light Higgs Decay into Muons at CLIC