仿真辅助的无似然异常检测
Simulation Assisted Likelihood-free Anomaly Detection
摘要：
由于缺乏在大型强子对撞机（LHC）发现新粒子的证据，扩大搜索计划至关重要。已经提出了各种与模型无关的搜索，增加了对意外信号的敏感性。通常有两种类型的搜索：严重依赖模拟的搜索和完全基于（未标记的）数据的搜索。本文介绍了一种混合方法，使这两种方法的最佳。对于在一个已知特征中共振的潜在信号，这种新方法首先学习参数化的重新加权函数来变形给定的模拟以匹配边带中的数据。然后将该函数内插到信号区域中，然后重新加权的仅背景模拟可用于监督学习以及背景估计。来自重新加权模拟的背景估计允许用于分类的特征与共振特征之间的非平凡相关性。一个双喷气搜索与喷气子结构被用来说明新的方法。仿真辅助无似然异常检测（SALAD）的未来应用包括多种最终状态以及与其他模型无关方法的潜在组合。
英文摘要：
Given the lack of evidence for new particle discoveries at the Large Hadron Collider (LHC), it is critical to broaden the search program. A variety of model-independent searches have been proposed, adding sensitivity to unexpected signals. There are generally two types of such searches: those that rely heavily on simulations and those that are entirely based on (unlabeled) data. This paper introduces a hybrid method that makes the best of both approaches. For potential signals that are resonant in one known feature, this new method first learns a parameterized reweighting function to morph a given simulation to match the data in sidebands. This function is then interpolated into the signal region and then the reweighted background-only simulation can be used for supervised learning as well as for background estimation. The background estimation from the reweighted simulation allows for non-trivial correlations between features used for classification and the resonant feature. A dijet search with jet substructure is used to illustrate the new method. Future applications of Simulation Assisted Likelihood-free Anomaly Detection (SALAD) include a variety of final states and potential combinations with other model-independent approaches.
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