论文标题
羊膜：用于异常检测的流动增强运输
FETA: Flow-Enhanced Transportation for Anomaly Detection
论文作者
论文摘要
共振异常检测是对新粒子独立搜索的有希望的框架。弱监督的共振异常检测方法将数据与潜在信号与从边带区域推论的标准模型（SM）背景的模板进行了比较。我们提出了一种生成此背景模板的方法，该模板使用基于流的模型来在高保真SM模拟和数据之间创建映射。该流量在信号区域盲目的边带区域进行训练，并且该流程在谐振特征（质量）上进行条件，以便可以将其插入信号区域。为了说明这种方法，我们使用了大型强子对撞机（LHC）奥运会数据集中的模拟碰撞。我们发现，流动构建的背景方法对其他最新建议具有竞争敏感性，因此可以提供补充信息以改善未来的搜索。
Resonant anomaly detection is a promising framework for model-independent searches for new particles. Weakly supervised resonant anomaly detection methods compare data with a potential signal against a template of the Standard Model (SM) background inferred from sideband regions. We propose a means to generate this background template that uses a flow-based model to create a mapping between high-fidelity SM simulations and the data. The flow is trained in sideband regions with the signal region blinded, and the flow is conditioned on the resonant feature (mass) such that it can be interpolated into the signal region. To illustrate this approach, we use simulated collisions from the Large Hadron Collider (LHC) Olympics Dataset. We find that our flow-constructed background method has competitive sensitivity with other recent proposals and can therefore provide complementary information to improve future searches.
