Anomaly detection with convolutional autoencoders is a popular method to search for new physics in a model-agnostic manner. These techniques are powerful, but they are still a “black box,” since we do not know what high-level physical observables determine how anomalous an event is. To address this, we adapt a recently proposed technique by Faucett et al. [Phys. Rev. D 103, 036020 (2021).], which maps out the physical observables learned by a neural network classifier, to the case of anomaly detection. We propose two different strategies that use a small number of high-level observables to mimic the decisions made by the autoencoder on background events, one designed to directly learn the output of the autoencoder, and the other designed to learn the difference between the autoencoder’s outputs on a pair of events. Despite the underlying differences in their approach, we find that both strategies have similar ordering performance as the autoencoder and independently use the same six high-level observables. From there, we compare the performance of these networks as anomaly detectors. We find that both strategies perform similarly to the autoencoder across a variety of signals, giving a nontrivial demonstration that learning to order background events transfers to ordering a variety of signal events.

中文翻译：

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为射流子结构中的新物理创建简单、可解释的异常检测器

使用卷积自动编码器进行异常检测是一种以与模型无关的方式搜索新物理的流行方法。这些技术很强大，但它们仍然是一个“黑匣子”，因为我们不知道哪些高级物理可观测物决定了事件的异常程度。为了解决这个问题，我们采用了 Faucet等人最近提出的技术。[物理。修订版 D 103, 036020 (2021).]，它将神经网络分类器学习的物理可观察量映射到异常检测的情况。我们提出了两种不同的策略，它们使用少量高级可观察对象来模拟自动编码器对背景事件做出的决策，一种旨在直接学习自动编码器的输出，另一种旨在学习自动编码器输出之间的差异关于一对事件。尽管他们的方法存在潜在差异，但我们发现这两种策略具有与自动编码器相似的排序性能，并且独立使用相同的六个高级可观察对象。从那里，我们比较这些网络作为异常检测器的性能。我们发现这两种策略在各种信号上的表现都与自动编码器相似，