Journal
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
Volume 543, Issue 2-3, Pages 577-584Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.nima.2004.12.018
Keywords
boosted decision trees; artificial neural network; particle identification; neutrino oscillations; MiniBooNE
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The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics. (c) 2005 Elsevier B.V. All rights reserved.
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