2007/02/05 by Bastos, J.
#Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.physics/0702041
This paper evaluates the performance of boosted decision trees for tagging b-jets. It is shown, using a Monte Carlo simulation of WH → lνqq 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.