用数据驱动方法自动找新物理信号区，无需先验知识。
Toward Model-Agnostic Detection of New Physics Using Data-Driven Signal Regions
- 基于信号在特征空间局部聚集的假设，用噪声平滑检测高敏感区域。
- 在高维空间中识别出含大量信号事件的数据驱动信号区。
- 适用于未知新粒子搜索，适合高能物理实验数据分析人员。
在寻找高能物理中的新粒子时，选择能富含信号事件的信号区（SR）至关重要。现有方法多依赖先验知识，但对超出当前理解范围的新粒子难以适用。本文提出一种模型无关的方法，基于信号事件在特征空间中局部聚集的合理假设，将信号视为局部高频特征，利用低通滤波思想：定义在加入随机噪声后最被影响的区域为信号区。通过学习可能含信号事件与无信号但相似事件之间的密度比，克服高维空间密度估计难题。在模拟的 $ m{HH} ightarrow 4b$ 事件上验证，该方法能在高维特征空间中高效识别出信号集中区域，显著提升数据驱动信号区的发现能力。
原文摘要 · Abstract (English)
In the search for new particles in high-energy physics, it is crucial to select the Signal Region (SR) in such a way that it is enriched with signal events if they are present. While most existing search methods set the region relying on prior domain knowledge, it may be unavailable for a completely novel particle that falls outside the current scope of understanding. We address this issue by proposing a method built upon a model-agnostic but often realistic assumption about the localized topology of the signal events, in which they are concentrated in a certain area of the feature space. Considering the signal component as a localized high-frequency feature, our approach employs the notion of a low-pass filter. We define the SR as an area which is most affected when the observed events are smeared with additive random noise. We overcome challenges in density estimation in the high-dimensional feature space by learning the density ratio of events that potentially include a signal to the complementary observation of events that closely resemble the target events but are free of any signals. By applying our method to simulated $\mathrm{HH} \rightarrow 4b$ events, we demonstrate that the method can efficiently identify a data-driven SR in a high-dimensional feature space in which a high portion of signal events concentrate.
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