基于级联训练的重口味标签的多变量方法
A multivariate approach to heavy flavour tagging with cascade training
摘要：
本文比较了人工神经网络和提升决策树的性能，有和没有级联训练，在对撞机实验中标记b-喷流。它示出，使用蒙特卡罗模拟的$WH \to lνq\bar{q}$事件，对于50%的B-标记效率，光喷射拒绝功率由提升决策树没有级联训练是约55%高于人工神经网络。级联训练技术可以在此b标记效率水平下分别将提升决策树和人工神经网络的性能提高约35%和80%。我们的结论是级联训练的提升决策树方法是最有前途的技术，在对撞机实验标记重口味。
英文摘要：
This paper compares the performance of artificial neural networks and boosted decision trees, with and without cascade training, for tagging b-jets in a collider experiment. It is shown, using a Monte Carlo simulation of $WH \to lνq\bar{q}$ events, that for a b-tagging efficiency of 50%, the light jet rejection power given by boosted decision trees without cascade training is about 55% higher than that given by artificial neural networks. The cascade training technique can improve the performance of boosted decision trees and artificial neural networks at this b-tagging efficiency level by about 35% and 80% respectively. We conclude that the cascade trained boosted decision trees method is the most promising technique for tagging heavy flavours at collider experiments.
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。
