QBDT，一种新的高能物理系统不确定性Boosting决策树训练方法
QBDT, a new boosting decision tree method with systematic uncertainties into training for High Energy Physics
摘要：
针对高能物理领域的分类问题，提出了一种新的提升决策树（BDT）方法QBDT。在许多高能物理的研究中，由于存在巨大的背景和各种系统不确定性，人们努力提高信号的显著性。为什么不开发一种直接针对意义的BDT方法呢？事实上，意义在这种新方法中起着核心作用。它用于在构建树时分割节点，也是对BDT分数有贡献的权重。由于系统不确定性可以很容易地包含在显著性计算中，因此该方法能够通过训练来学习减少系统不确定性的影响。以质子-质子碰撞$pp \to h + X \to γτ^+τ^-+X$中稀有辐射Higgs衰变的研究为例，比较了QBDT和目前流行的梯度BDT（GradeBDT）方法。QBDT被发现，以减少信号强度和系统的不确定性源之间的相关性，从而给一个更好的意义。系统不确定度源对信号强度不确定度的贡献为GradBDT法的50- 85%。
英文摘要：
A new boosting decision tree (BDT) method, QBDT, is proposed for the classification problem in the field of high energy physics (HEP). In many HEP researches, great efforts are made to increase the signal significance with the presence of huge background and various systematical uncertainties. Why not develop a BDT method targeting the significance directly? Indeed, the significance plays a central role in this new method. It is used to split a node in building a tree and to be also the weight contributing to the BDT score. As the systematical uncertainties can be easily included in the significance calculation, this method is able to learn about reducing the effect of the systematical uncertainties via training. Taking the search of the rare radiative Higgs decay in proton-proton collisions $pp \to h + X \to γτ^+τ^-+X$ as example, QBDT and the popular Gradient BDT (GradBDT) method are compared. QBDT is found to reduce the correlation between the signal strength and systematical uncertainty sources and thus to give a better significance. The contribution to the signal strength uncertainty from the systematical uncertainty sources using the new method is 50-85~\% of that using the GradBDT method.
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