Tagging heavy flavours with boosted decision trees
João A. Bastos · arXiv (Cornell University) · 2007
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.