Model Selection in Top Quark Tagging with a Support Vector Classifier

The problem of tagging a top quark generation event in data coming from the collider detector at Fermilab is considered and tackled through the use of a support vector machine classifier. In order to select a fitting model, a twofold procedure has been adopted. The SVC hyperparameters have been selected through the bootstrap technique and then an additional tuning of the bias value and the error relevance has been performed by means both of a purity vs. efficiency curve and of the AUC value. The generalization capability of the model has been evaluated using the maximal discrepancy criterion.