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

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dc.creatorRoe, Byron P.
dc.creatorYang, Hai-Jun
dc.creatorZhu, Ji
dc.creatorLiu, Yong
dc.creatorStancu, Ion
dc.creatorMcGregor, Gordon
dc.date2004-08-30
dc.date2004-11-19
dc.date.accessioned2026-07-07T05:52:34Z
dc.date.available2026-07-07T05:52:34Z
dc.descriptionThe 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.
dc.description6 pages, 5 figures; Accepted for publication in Nucl. Inst. & Meth. A
dc.identifierhttps://arxiv.org/abs/physics/0408124
dc.identifierhttp://arxiv.org/abs/physics/0408124
dc.identifierNucl.Instrum.Meth. A543 (2005) 577-584
dc.identifierdoi:10.1016/j.nima.2004.12.018
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/86360
dc.subjectData Analysis, Statistics and Probability
dc.subjectHigh Energy Physics - Experiment
dc.titleBoosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification
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