Boosted decision trees as an alternative to artificial neural networks for particle identification

被引：395

作者：

Roe, BP

Yang, HJ ^{[1
]}

Zhu, J

Liu, Y

Stancu, I

McGregor, G

机构：

[1] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA

[2] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA

[3] Univ Alabama, Dept Phys & Astron, Tuscaloosa, AL 35487 USA

[4] Los Alamos Natl Lab, Los Alamos, NM 87545 USA

来源：

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT | 2005年 / 543卷 / 2-3期

基金：

美国国家科学基金会;

关键词：

boosted decision trees; artificial neural network; particle identification; neutrino oscillations; MiniBooNE;

D O I：

10.1016/j.nima.2004.12.018

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

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.

引用

页码：577 / 584

页数：8

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