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.
@article{arxiv.hep-ex/9505004,
title = {Neural Network based Electron Identification in the ZEUS Calorimeter},
author = {H. Abramowicz and A. Caldwell and R. Sinkus},
journal= {arXiv preprint arXiv:hep-ex/9505004},
year = {2010}
}
Comments
20 pages, latex, 16 figures appended as uuencoded file