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 ponciple component analysis the improvement in the performance is traced back to the number of variables used in the neural network approach.