A Multi-variate Discrimination Technique Based on Range-Searching
arXiv:hep-ex/0211019 · doi:10.1016/S0168-9002(03)00376-0
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.
Submitted to NIM, 18 pages, 8 figures
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Cited by in corpus (8)
- Search for lepton-flavor violation at HERA
- Search for QCD-instanton induced events in deep inelastic ep scattering at HERA
- Isolated tau leptons in events with large missing transverse momentum at HERA
- Review of Searches for Rare Processes and Physics Beyond the Standard Model at HERA
- PDE-Foam - a probability-density estimation method using self-adapting phase-space binning
- Study of tau-pair production at HERA
- Events with Isolated Charged Leptons and Large Missing Transverse Momentum at HERA
- A simulation study on the measurement of D0-D0bar mixing parameter y at BES-III