论文标题
将新物理学视为新颖性 - 互补性很重要
Detecting New Physics as Novelty -- Complementarity Matters
论文作者
论文摘要
新颖性检测是机器学习的任务，旨在在没有先验知识的情况下检测新事件。特别是，它的技术可用于检测山脉新现象的意外信号。在本文中，我们开发了一种分析方案，该方案利用了互补性，最初是在基于隔离和基于群集的新颖性评估者之间进行了参考〜\ cite {hajer：2018kqm}的研究。这种方法可以显着提高山脉新颖性检测的性能和整体适用性，我们使用模仿对撞机事件的各种二维高斯样本进行了证明。作为进一步的原则证明，我们随后将该方案应用于LHC的两个显着不同的信号，其中包含$ t \ bar {t}γγ$最终状态：$ t \ bar t h $，在二型群质谱中具有狭窄的共鸣，并在较高的跨度分布中，在较高的跨度分布中，在较高的跨度分布中，这是一个很高的跨度分布。与LHC现有的专用搜索相比，发现这两个信号的敏感性令人鼓舞。
Novelty detection is a task of machine learning that aims at detecting novel events without a prior knowledge. In particular, its techniques can be applied to detect unexpected signals from new phenomena at colliders. In this paper, we develop an analysis scheme that exploits the complementarity, originally studied in Ref.~\cite{Hajer:2018kqm}, between isolation-based and clustering-based novelty evaluators. This approach can significantly improve the performance and overall applicability of novelty detection at colliders, which we demonstrate using a variety of two dimensional Gaussian samples mimicking collider events. As a further proof of principle, we subsequently apply this scheme to the detection of two significantly different signals at the LHC featuring a $t\bar{t}γγ$ final state: $t\bar t h$, giving a narrow resonance in the diphoton mass spectrum, and gravity-mediated supersymmetry, which results in broad distributions at high transverse momentum. Compared to existing dedicated searches at the LHC, the sensitivities for both signals are found to be encouraging.
