共振异常检测的仿真辅助解相关方法
Simulation-Assisted Decorrelation for Resonant Anomaly Detection
摘要：
越来越多的弱和无监督的机器学习异常检测方法正在被提出，以显着扩展大型强子对撞机和其他地方的搜索程序。这些方法的一个典型例子是寻找共振新物理，其中可以在不变的质谱中执行碰撞搜索。完全依赖数据的方法的一个重大挑战是，它们容易受到机器学习分类器对不变质量的依赖性的影响。我们探索两种解决方案，以最低限度地将模拟到学习这一挑战。特别是，我们研究了仿真辅助似然无异常检测（SALAD）的分类器和不变质量之间的相关性的鲁棒性。接下来，我们提出了一种新的方法，只使用模拟去相关，但没有标签的分类（CWoLa）的方法来实现信号的灵敏度。这两种方法进行了比较，使用一个完整的背景拟合分析从LHC奥运会的模拟数据和数据的相关性是强大的。
英文摘要：
A growing number of weak- and unsupervised machine learning approaches to anomaly detection are being proposed to significantly extend the search program at the Large Hadron Collider and elsewhere. One of the prototypical examples for these methods is the search for resonant new physics, where a bump hunt can be performed in an invariant mass spectrum. A significant challenge to methods that rely entirely on data is that they are susceptible to sculpting artificial bumps from the dependence of the machine learning classifier on the invariant mass. We explore two solutions to this challenge by minimally incorporating simulation into the learning. In particular, we study the robustness of Simulation Assisted Likelihood-free Anomaly Detection (SALAD) to correlations between the classifier and the invariant mass. Next, we propose a new approach that only uses the simulation for decorrelation but the Classification without Labels (CWoLa) approach for achieving signal sensitivity. Both methods are compared using a full background fit analysis on simulated data from the LHC Olympics and are robust to correlations in the data.
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