Neural Network based Electron Identification in the ZEUS Calorimeter

We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions characterized by the presence of a scattered electron in the final state. The performance of the algorithm is compared to an electron identification method based on a classical probabilistic approach. By means of a principle component analysis the improvement in the performance is traced back to the number of variables used in the neural network approach. DESY 95-054 ISSN 0418-9833 Supported by the Israeli Academy of Science, contract Nr. 419/94 Supported by the National Science Foundation Supported by the German Federal Minister for Research and Technology (BMFT), contract Nr. 6HH19I 1