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Discoveries of new phenomena often involve a dedicated search for ahypothetical physics signature. Recently, novel deep learning techniques haveemerged for anomaly detection in the absence of a signal prior. However, byignoring signal priors, the sensitivity of these approaches is significantlyreduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK),whereby we introduce alternative signal priors that capture some of the salientfeatures of new physics signatures, allowing for the recovery of sensitivityeven when the alternative signal is incorrect. This approach can be applied toa broad range of physics models and neural network architectures. In thispaper, we apply QUAK to anomaly detection of new physics events at the CERNLarge Hadron Collider utilizing variational autoencoders with normalizing flow.

Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge