FETA: 用于异常检测的流增强输运方法
FETA: Flow-Enhanced Transportation for Anomaly Detection
AI总结：
本研究提出名为FETA的流增强输运异常检测方法，通过基于流的模型构建标准模型本底模板，在LHC奥运会数据集上验证其具有与现有方案相当的灵敏度，可为新粒子搜寻提供补充信息。
摘要：
共振异常检测是用于新粒子模型无关搜寻的一种很有前景的框架。弱监督共振异常检测方法将含有潜在信号的数据与从边带区域推断出的标准模型（SM）本底模板进行对比。我们提出了一种生成该本底模板的方法，该方法使用基于流的模型在高保真SM模拟与数据之间建立映射。流模型在信号区域被屏蔽的情况下于边带区域训练，且以共振特征（质量）为条件，从而可插值到信号区域。为验证该方法，我们使用了大型强子对撞机（LHC）奥运会数据集的模拟对撞数据。研究发现，我们的流构建本底方法与近期其他方案相比具有相当的灵敏度，因此可提供补充信息以改进未来的搜寻工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
