关于确定多维效率的多变量技术的潜力
On the potential of multivariate techniques for the determination of multidimensional efficiencies
摘要：
粒子碰撞或衰变的微分测量可以提供对粒子物理学的标准模型之外的物理学的严格约束。特别地，描述重介子多体衰变的运动学和角度变量的分布是不平凡的，并且可以标记潜在的相互作用物理。在大型强子对撞机和香料工厂的出现开启的高亮度时代，差分测量越来越不受统计精度的支配，需要精确确定同时取决于几个变量的效率，而不是分解这些变量。本文档反映了多变量技术用于确定此类多维效率的潜力。我们进行了两个案例研究表明，多层感知器神经网络可以确定和纠正的失真所引入的衰变$B^{0}\rightarrow K^{*0}(\rightarrow K^{+}π^{-}) μ^{+}μ^{-}$和$D^{0}\rightarrow K^{-}π^{+}π^{+}π^{-}$的多维相空间中的重建和选择标准，在一个最小的分析工作的价格。我们的结论是，这种方法已经可以用于统计精度尚未达到百分比水平的测量，并且使用更复杂的机器学习方法，上述潜力非常有希望。
英文摘要：
Differential measurements of particle collisions or decays can provide stringent constraints on physics beyond the Standard Model of particle physics. In particular, the distributions of the kinematical and angular variables that characterise heavy me- son multibody decays are non trivial and can sign the underlying interaction physics. In the era of high luminosity opened by the advent of the Large Hadron Collider and of Flavor Factories, differential measurements are less and less dominated by statistical precision and require a precise determination of efficiencies that depend simultaneously on several variables and do not factorise in these variables. This docu- ment is a reflection on the potential of multivariate techniques for the determination of such multidimensional efficiencies. We carried out two case studies that show that multilayer perceptron neural networks can determine and correct for the distortions introduced by reconstruction and selection criteria in the multidimensional phase space of the decays $B^{0}\rightarrow K^{*0}(\rightarrow K^{+}π^{-}) μ^{+}μ^{-}$ and $D^{0}\rightarrow K^{-}π^{+}π^{+}π^{-}$, at the price of a minimal analysis effort. We conclude that this method can already be used for measurements which statistical precision does not yet reach the percent level and that with more sophisticated machine learning methods, the aforementioned potential is very promising.
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