Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC
Sascha Caron, Luc Hendriks, Rob Verheyen
SciPost Phys. 12, 077 (2022) · published 25 February 2022
- doi: 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 [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.
Cited by 28
Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 2 Sascha Caron,
- 2 Luc Hendriks,
- 3 Rob Verheyen
- 1 Nationaal instituut voor Subatomaire Fysica / National Institute for Subatomic Physics [NIKHEF]
- 2 Radboud Universiteit Nijmegen / Radboud University Nijmegen [RUN]
- 3 University College London [UCL]