Tagging heavy flavours with boosted decision trees
نویسنده
چکیده
This paper evaluates the performance of boosted decision trees for tagging b-jets. It is shown, using a Monte Carlo simulation of WH → lνqq̄ events that boosted decision trees outperform feed-forward neural networks. The results show that for a b-tagging efficiency of 90% the b-jet purity given by boosted decision trees is almost 20% higher than that given by neural networks.
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