发现新物理学的新颖性--互补性很重要
Detecting New Physics as Novelty -- Complementarity Matters
摘要：
新颖性检测是机器学习的一项任务，旨在在没有先验知识的情况下检测新事件。特别是，它的技术可以应用于检测碰撞机新现象的意外信号。本文中，我们开发了一种利用互补性的分析方案，最初在参考文献中进行了研究。~ \cite{Hajer:2018kqm}，介于基于隔离和基于聚类的新颖性评估器之间。这种方法可以显着提高性能和整体适用性的新奇检测在对撞机，我们证明使用各种二维高斯样本模仿对撞机事件。作为原理的进一步证明，我们随后将该方案应用于在大型强子对撞机上检测两个显著不同的信号，其特征在于$t\bar{t}γγ$最终状态：$t\bar t h$，在双光子质谱中产生窄共振，以及引力介导的超对称性，这导致在高横向动量下的广泛分布。与LHC现有的专门搜索相比，这两个信号的灵敏度令人鼓舞。
英文摘要：
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.
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