ABCDisCo：使用机器学习自动化ABCD方法
ABCDisCo: Automating the ABCD Method with Machine Learning
摘要：
ABCD方法是高能物理中最广泛使用的数据驱动背景估计技术之一。该方法利用两个独立的分类器将信号和背景分成四个区域，从而可以利用其他三个控制区域来简单地估计信号区域中的背景。通常，独立分类器被“手动”选择为直观和物理动机的变量。在这里，我们探索使用机器学习自动设计一个或两个分类器的可能性。我们展示了如何使用最先进的去相关方法来构建功能强大但独立的鉴别器。在此过程中，我们发现了ABCD方法以前不受重视的方面：其准确性取决于控制区域中的低信号污染，不仅是整体，而且是相对于信号区域中的信号分数。我们展示了三个例子的方法：一个简单的模型，由三维高斯;提升强子顶喷标记;和重铸搜索成对的双射共振。在所有情况下，通过机器学习自动化ABCD方法可以显著提高ABCD闭合、背景抑制和信号污染方面的性能。
英文摘要：
The ABCD method is one of the most widely used data-driven background estimation techniques in high energy physics. Cuts on two statistically-independent classifiers separate signal and background into four regions, so that background in the signal region can be estimated simply using the other three control regions. Typically, the independent classifiers are chosen "by hand" to be intuitive and physically motivated variables. Here, we explore the possibility of automating the design of one or both of these classifiers using machine learning. We show how to use state-of-the-art decorrelation methods to construct powerful yet independent discriminators. Along the way, we uncover a previously unappreciated aspect of the ABCD method: its accuracy hinges on having low signal contamination in control regions not just overall, but relative to the signal fraction in the signal region. We demonstrate the method with three examples: a simple model consisting of three-dimensional Gaussians; boosted hadronic top jet tagging; and a recasted search for paired dijet resonances. In all cases, automating the ABCD method with machine learning significantly improves performance in terms of ABCD closure, background rejection and signal contamination.
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