Physics
Scientific paper
Jan 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004nimpa.516..511b&link_type=abstract
Nuclear Instruments and Methods in Physics Research Section A, Volume 516, Issue 2-3, p. 511-528.
Physics
53
Classification, Discrimination, Multivariate, Neural Networks, Kernel Methods, Nearest-Neighbour, Regression Trees
Scientific paper
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.
Bock Rudolf K.
Chilingarian Ashot A.
Gaug Markus
Hakl F.
Hengstebeck Thomas
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