A Multivariate Training Technique with Event Reweighting

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dc.creatorYang, Hai-Jun
dc.creatorDai, Tiesheng
dc.creatorWilson, Alan
dc.creatorZhao, Zhengguo
dc.creatorZhou, Bing
dc.date2007-08-27
dc.date2008-02-20
dc.date.accessioned2026-07-07T11:18:54Z
dc.date.available2026-07-07T11:18:54Z
dc.descriptionAn event reweighting technique incorporated in multivariate training algorithm has been developed and tested using the Artificial Neural Networks (ANN) and Boosted Decision Trees (BDT). The event reweighting training are compared to that of the conventional equal event weighting based on the ANN and the BDT performance. The comparison is performed in the context of the physics analysis of the ATLAS experiment at the Large Hadron Collider (LHC), which will explore the fundamental nature of matter and the basic forces that shape our universe. We demonstrate that the event reweighting technique provides an unbiased method of multivariate training for event pattern recognition.
dc.description20 pages, 8 figures
dc.identifierhttps://arxiv.org/abs/0708.3635
dc.identifierhttp://arxiv.org/abs/0708.3635
dc.identifierJINST3:P04004,2008
dc.identifierdoi:10.1088/1748-0221/3/04/P04004
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/193504
dc.subjectData Analysis, Statistics and Probability
dc.titleA Multivariate Training Technique with Event Reweighting
dc.typetext

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