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arXiv 2011.03550hep-phhep-ex
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准反常知识：寻找嵌入知识的新物理学

Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge

Sang Eon Park, Dylan Rankin, Silviu-Marian Udrescu, Mikaeel Yunus, Philip Harris

备注：
25 pages, 9 figures
发表信息：
J. High Energ. Phys. 2021, 30 (2021)
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摘要：

新现象的发现通常涉及对假设的物理特征的专门搜索。最近，出现了新的深度学习技术，用于在没有信号先验的情况下进行异常检测。然而，通过忽略信号先验，这些方法的灵敏度显着降低。我们提出了一种新的策略，称为准异常知识（QUAK），据此，我们引入替代信号先验，捕获新的物理签名的一些显着特征，允许恢复灵敏度，即使当替代信号是不正确的。这种方法可以应用于广泛的物理模型和神经网络架构。在本文中，我们应用QUAK的新的物理事件的异常检测在欧洲核子研究中心大型强子对撞机利用变分自编码器与归一化流。

英文摘要：

Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the sensitivity of these approaches is significantly reduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alternative signal priors that capture some of the salient features of new physics signatures, allowing for the recovery of sensitivity even when the alternative signal is incorrect. This approach can be applied to a broad range of physics models and neural network architectures. In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider utilizing variational autoencoders with normalizing flow.

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