Tagging heavy flavours with boosted decision trees

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dc.creatorBastos, J.
dc.date2007-02-05
dc.date2007-04-16
dc.date.accessioned2026-07-07T07:56:37Z
dc.date.available2026-07-07T07:56:37Z
dc.descriptionThis paper evaluates the performance of boosted decision trees for tagging b-jets. It is shown, using a Monte Carlo simulation of $WH \to lνq\bar{q}$ events that boosted decision trees outperform feed-forward neural networks. The results show that for a b-tagging efficiency of 60% the light jet rejection given by boosted decision trees is about 35% higher than that given by neural networks.
dc.description12 pages, 13 figures
dc.identifierhttps://arxiv.org/abs/physics/0702041
dc.identifierhttp://arxiv.org/abs/physics/0702041
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/127375
dc.subjectData Analysis, Statistics and Probability
dc.subjectHigh Energy Physics - Experiment
dc.titleTagging heavy flavours with boosted decision trees
dc.typetext

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