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

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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.
6 pages, 5 figures; Accepted for publication in Nucl. Inst. & Meth. A

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Data Analysis, Statistics and Probability, High Energy Physics - Experiment

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http://salesiana.dossiersoluciones.com/handle/123456789/86360

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/physics/0408124
 http://arxiv.org/abs/physics/0408124
 Nucl.Instrum.Meth. A543 (2005) 577-584
 doi:10.1016/j.nima.2004.12.018

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arXiv