论文标题

机器学习的排除限制而无需嵌套

Machine-Learned Exclusion Limits without Binning

论文作者

Arganda, Ernesto, Perez, Andres D., Rios, Martin de los, Seoane, Rosa María Sandá

论文摘要

机器学习的可能性(MLL)将机器学习分类技术与基于可能性的推理测试相结合,以估计高维数据集的实验灵敏度。我们通过包括内核密度估计器(KDE)来扩展MLL方法,以避免将分类器输出固定以提取所得的一维信号和背景概率密度函数。我们首先在具有多元高斯分布生成的玩具模型上测试我们的方法,其中已知真正的概率分布函数。后来,我们将该方法应用于LHC的两种感兴趣的情况:搜索异国情调的希格斯玻色子,以及$ z'$ boson腐烂到Lepton Pairs。与基于物理的数量相反,ML输出的典型波动为纯信号和纯净的样品提供了非平滑概率分布。由于KDE方法的良好性能和灵活性,非平滑度被传播到密度估计中。我们研究了它对最终显着性计算的影响,并使用几个独立的ML输出实现的平均值比较结果,这使我们能够获得更平滑的分布。我们得出的结论是,显着性估计对于这个问题不明智。

Machine-Learned Likelihoods (MLL) combines machine-learning classification techniques with likelihood-based inference tests to estimate the experimental sensitivity of high-dimensional data sets. We extend the MLL method by including Kernel Density Estimators (KDE) to avoid binning the classifier output to extract the resulting one-dimensional signal and background probability density functions. We first test our method on toy models generated with multivariate Gaussian distributions, where the true probability distribution functions are known. Later, we apply the method to two cases of interest at the LHC: a search for exotic Higgs bosons, and a $Z'$ boson decaying into lepton pairs. In contrast to physical-based quantities, the typical fluctuations of the ML outputs give non-smooth probability distributions for pure-signal and pure-background samples. The non-smoothness is propagated into the density estimation due to the good performance and flexibility of the KDE method. We study its impact on the final significance computation, and we compare the results using the average of several independent ML output realizations, which allows us to obtain smoother distributions. We conclude that the significance estimation turns out to be not sensible to this issue.

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