Neural Network based Electron Identification in the ZEUS Calorimeter

TL;DR

This paper introduces a neural network-based electron identification algorithm for the ZEUS calorimeter, demonstrating improved performance over classical methods in selecting deep inelastic electron-proton interactions.

Contribution

It presents a novel neural network approach for electron identification in the ZEUS calorimeter, outperforming traditional probabilistic methods.

Findings

Neural network approach improves electron identification accuracy.

Principal component analysis reveals key variables for performance enhancement.

Comparison shows neural network outperforms classical methods.

Abstract

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.