Binning high-dimensional classifier output for HEP analyses through a clustering algorithm

EPJ Web of Conferences, vol. 295, pp. 012104

Abstract

Abstract The usage of Deep Neural Networks (DNNs) as multi-classifiers is widespread in modern HEP analyses. In standard categorisation methods, the high-dimensional output of the DNN is often reduced to a one-dimensional distribution by exclusively passing the information about the highest class score to the statistical inference method. Correlations to other classes are hereby omitted. Moreover, in common statistical inference tools, the classification values need to be binned, which relies on the researcher’s expertise and is often non-trivial. To overcome the challenge of binning multiple dimensions and preserving the correlations of the event-related classification information, we perform K-means clustering on the high-dimensional DNN output to create bins without marginalising any axes. We evaluate our method in the context of a simulated cross section measurement at the CMS experiment, showing an increased expected sensitivity over the standard binning approach.

Authors 3

1. S. Diekmann corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. N. Eich corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. Martin Erdmann Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University