使用提升决策树标记重口味
Tagging heavy flavours with boosted decision trees
摘要：
本文评估了提升决策树标记b-jet的性能。它示出，使用蒙特卡洛模拟的$WH \to lνq\bar{q}$事件，提高决策树优于前馈神经网络。结果表明，对于60%的b标记效率，由提升决策树给出的光喷射拒绝比由神经网络给出的光喷射拒绝高约35%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
