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Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification

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arxiv physics/0408124 v2 pith:FY62YJA3 submitted 2004-08-30 physics.data-an hep-ex

Byron P. Roe , Hai-Jun Yang , Ji Zhu , Yong Liu , Ion Stancu , Gordon McGregor This is my paper · ORCID
classification physics.data-anhep-ex
keywords artificialexperimentidentificationnetworksparticlealgorithmsboostedboosting
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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.

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Cited by 5 Pith papers

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