Threshold optimization and variable construction for classification in the MAGIC and FACT experiments

Abstract

In the MAGIC and FACT experiments, random forests are usually used for a classification of a gamma ray signal and hadronic background. Random forests use a set of tree classifiers and aggregate the single decisions of the trees into one overall decision. In this work a method to choose an optimal threshold value for the random forest classification is introduced. The method is based on the minimization of the MSE of an estimator for the number of gamma particles in the data set. In a second step, new variables for the classification are introduced in this work. The idea of these variables is to fit bivariate distributions to images recorded by the two telescopes and using distance measures for densities to calculate the distance between the observed and fitted distributions. With a reasonable choice of distributions to fit, it can be expected that such distances are smaller for gamma observations than for the hadronic background. In a third step, the new threshold optimization and the new variable construction are combined and compared to the methods currently in use. It can be seen that the new methods lead to substantial improvements of the classification with regard to the aim of the analysis.