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Methods for multidimensional event classification: a case study using images from a Cherenkov gamma-ray telescope

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Title: Methods for multidimensional event classification: a case study using images from a Cherenkov gamma-ray telescope
Authors: Bock, R.K.1 rkb@mail.cern.ch, Chilingarian, A.2, Gaug, M.3, Hakl, F.4, Hengstebeck, T.5, Jiřina, M.4, Klaschka, J.4, Kotrč, E.4, Savický, P.4, Towers, S.6, Vaiciulis, A.7, Wittek, W.1
Source: Nuclear Instruments & Methods in Physics Research Section A. Jan2004, Vol. 516 Issue 2/3, p511. 18p.
Subjects: Monte Carlo method, Cherenkov radiation, Telescopes, Multivariate analysis
Abstract: We present results from a case study comparing different multivariate classification methods. The input is a set of Monte Carlo data, generated and approximately triggered and pre-processed for an imaging gamma-ray Cherenkov telescope. Such data belong to two classes, originating either from incident gamma rays or caused by hadronic showers. There is only a weak discrimination between signal (gamma) and background (hadrons), making the data an excellent proving ground for classification techniques.The data and methods are described, and a comparison of the results is made. Several methods give results comparable in quality within small fluctuations, suggesting that they perform at or close to the Bayesian limit of achievable separation. Other methods give clearly inferior or inconclusive results. Some problems that this study can not address are also discussed. [Copyright &y& Elsevier]
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Database: Engineering Source
Description
Abstract:We present results from a case study comparing different multivariate classification methods. The input is a set of Monte Carlo data, generated and approximately triggered and pre-processed for an imaging gamma-ray Cherenkov telescope. Such data belong to two classes, originating either from incident gamma rays or caused by hadronic showers. There is only a weak discrimination between signal (gamma) and background (hadrons), making the data an excellent proving ground for classification techniques.The data and methods are described, and a comparison of the results is made. Several methods give results comparable in quality within small fluctuations, suggesting that they perform at or close to the Bayesian limit of achievable separation. Other methods give clearly inferior or inconclusive results. Some problems that this study can not address are also discussed. [Copyright &y& Elsevier]
ISSN:01689002
DOI:10.1016/j.nima.2003.08.157