Tagging heavy flavours with boosted decision trees

J. Bastos

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Tagging heavy flavours with boosted decision trees

J. Bastos

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Abstract

This 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.

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J. Bastos. 2007-04-16. Tagging heavy flavours with boosted decision trees. https://arxiv.org/abs/physics/0702041

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