Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC

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.

7 pages, 5 figures; improved text

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Cited by in corpus (27)

• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• Machine Learning and LHC Event Generation
• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Bump Hunting in Latent Space
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• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
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• Anomalies, Representations, and Self-Supervision
• Neural Embedding: Learning the Embedding of the Manifold of Physics Data
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• Mind the gap: The discrepancy between simulation and reality drives interpretations of the Galactic Center Excess
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• Finding excesses in model parameter space
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