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

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. r 2003 Elsevier B.V. All rights reserved.

[1]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[2]  James P. Egan,et al.  Signal detection theory and ROC analysis , 1975 .

[3]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[4]  A. G. Ivakhnenko,et al.  Self-Organizing Methods in Modelling and Clustering: GMDH Type Algorithms , 1988, Systems Analysis and Simulation 1988, I: Theory and Foundations. Proceedings of the International Symposium held in Berlin (GDR), September 12–16, 1988.

[5]  M. Jirina,et al.  The Modified GMDH: Sigmoidal and Polynomial Neural Net , 1994 .

[6]  Garrido,et al.  Discriminating signal from background using neural networks: Application to top-quark search at the Fermilab Tevatron. , 1996, Physical review. D, Particles and fields.

[7]  D. Fegan.  hadron separation at TeV energies , 1997 .

[8]  J. Knapp,et al.  CORSIKA: A Monte Carlo code to simulate extensive air showers , 1998 .

[9]  F. Samuelson,et al.  Kernel analysis in TeV gamma-ray selection , 2001 .

[10]  E. Lorenz,et al.  A method to correct HILLAS parameters of imaging Cherenkov telescope data taken at different background light levels , 2001 .

[11]  A. Vaiciulis,et al.  Support vector machines in analysis of top quark production , 2002 .

[12]  A. Vardanyan,et al.  Multivariate approach for selecting sets of differentially expressed genes. , 2002, Mathematical biosciences.

[13]  M. Gaug.  AMANDA event reconstruction and cut evaluation methods , 2002 .

[14]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[15]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[16]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[17]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.