A Multi-variate Discrimination Technique Based on Range-Searching

TL;DR

The paper introduces PDE-RS, a fast, transparent multi-variate classification method based on range-searching, which performs comparably to neural networks but with less computation, aiding in particle physics data analysis.

Contribution

It presents PDE-RS, a novel multi-variate event classification technique using range-searching, with detailed algorithmic description and validation on physics data examples.

Findings

Performs as well as neural networks in classification tasks.

Requires less computation time than neural networks.

Eases evaluation of systematic and statistical uncertainties.

Abstract

We present a fast and transparent multi-variate event classification technique, called PDE-RS, which is based on sampling the signal and background densities in a multi-dimensional phase space using range-searching. The employed algorithm is presented in detail and its behaviour is studied with simple toy examples representing basic patterns of problems often encountered in High Energy Physics data analyses. In addition an example relevant for the search for instanton-induced processes in deep-inelastic scattering at HERA is discussed. For all studied examples, the new presented method performs as good as artificial Neural Networks and has furthermore the advantage to need less computation time. This allows to carefully select the best combination of observables which optimally separate the signal and background and for which the simulations describe the data best. Moreover, the systematic and statistical uncertainties can be easily evaluated. The method is therefore a powerful tool to find a small number of signal events in the large data samples expected at future particle colliders.