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PDE-Foam-A probability density estimation method using self-adapting phase-space binning
KTH, School of Engineering Sciences (SCI), Physics.
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2009 (English)In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 606, no 3, 717-727 p.Article in journal (Refereed) Published
Abstract [en]

Probability density estimation (PDE) is a multi-variate discrimination technique based on sampling signal and background densities defined by event samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase space. In this paper, we present a modification of the PDE method that uses a self-adapting binning method to divide the multi-dimensional phase space in a finite number of hyper-rectangles (cells). The binning algorithm adjusts the size and position of a predefined number of cells inside the multi-dimensional phase space, minimising the variance of the signal and background densities inside the cells. The implementation of the binning algorithm (PDE-Foam) is based on the MC event-generation package Foam. We present performance results for representative examples (toy models) and discuss the dependence of the obtained results on the choice of parameters. The new PDE-Foam shows improved classification capability for small training samples and reduced classification time compared to the original PDE method based on range searching.

Place, publisher, year, edition, pages
2009. Vol. 606, no 3, 717-727 p.
Keyword [en]
Multi-variate discrimination technique, Probability density estimation, Self-adapting phase-space binning
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-32179DOI: 10.1016/j.nima.2009.05.028ISI: 000268453600058Scopus ID: 2-s2.0-67649435522OAI: oai:DiVA.org:kth-32179DiVA: diva2:409491
Note
QC 20110408Available from: 2011-04-08 Created: 2011-04-08 Last updated: 2017-12-11Bibliographically approved

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