We have studied the application of different classification algorithms in the analysis of simulated high energy physics data. Whereas Neural Network algorithms have become a standard tool for data analysis, the performance of other classifiers such as Support Vector Machines has not yet been tested in this environment. We chose two different problems to compare the performance of a Support Vector Machine and a Neural Net trained with back-propagation: tagging events of the type e + e − → cc and the identification of muons produced in multihadronic e + e − annihilation events. Artificial Neural Networks (ANN) are a useful tool to solve multi-dimensional classification problems in high energy physics, in cases where one-dimensional cut techniques are not sufficient. They are used both as hard-coded chip for very fast low-level pattern recognition in on-line triggering [1,2] and as a statistical tool for particle and event classifications in offline data analysis. In offline data analysis, a Monte Carlo simulation of the physics process and the detector response is necessary to train an ANN by supervised learning. ANN algorithms have been applied successfully in classification problems such as gluon-jet tagging [3] and b-quark tagging [4] . The ANN classifiers [5] constructed in this paper have sigmoid nodes. Design and tuning issues were solved by applying practical experience rules [6] .
Vannerem et al. in REF attempted to test SVM in this environment by using support vector classifiers in the analysis of simulated high energy physics data.
Classifying LEP data with support vector algorithms
arXiv: High Energy Physics - Experiment
Physics Computer Science