Caron, Sascha; Hendriks, Luc; Verheyen, Rob; (2022) Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC. SciPost Physics , 12 (2) , Article 077. 10.21468/scipostphys.12.2.077.

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Abstract

We propose a new method to define anomaly scores and apply this to particle physics collider events. Anomalies can be either rare, meaning that these events are a minority in the normal dataset, or different, meaning they have values that are not inside the dataset. We quantify these two properties using an ensemble of One-Class Deep Support Vector Data Description models, which quantifies differentness, and an autoregressive flow model, which quantifies rareness. These two parameters are then combined into a single anomaly score using different combination algorithms. We train the models using a dataset containing only simulated collisions from the Standard Model of particle physics and test it using various hypothetical signals in four different channels and a secret dataset where the signals are unknown to us. The anomaly detection method described here has been evaluated in a summary paper where it performed very well compared to a large number of other methods. The method is simple to implement and is applicable to other datasets in other fields as well.

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