Applications of neural networks to shower analysis in a highly segmented LAr calorimeter

A new method was developed to evaluate the signals of a calorimeter with neural networks. After training with simulated data, energy reconstruction and particle identification is possible. The method was developed for the liquid argon calorimeter of the H1 experiment at DESY in Hamburg, Germany, but it uses quantities which are available at any segmented calorimeter.