A Case Study on the Use of Statistical Classification Methods in Particle Physics

Current research in experimental particle physics is dominated by high profile and large scale experiments. One of the major tasks in these experiments is the selection of interesting or relevant events. In this paper we propose to use statistical classification algorithms for this task. To illustrate our method we apply it to an Monte-Carlo (MC) dataset from the BaBar experiment. One of the major obstacles in constructing a classifier for this task is the imbalanced nature of the dataset. Only about 0.5% of the data are interesting events. The rest are background or noise events. We show how ROC curves can be used to find a suitable cutoff value to select a reasonable subset of a stream for further analysis. Finally, we estimate the CP asymmetry of the \({B}^{\pm }\rightarrow D{K}^{\pm }\) decay using the samples extracted by the classifiers.