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

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

2004-08-30

arxiv.org/abs/physics/0408124v2

Nucl.Instrum.Meth. A543 (2005) 577-584

Physics

Data Analysis, Statistics and Probability

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

Scientific paper

10.1016/j.nima.2004.12.018

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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