| Summary: | 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 [1] 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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