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
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• 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
• What's Anomalous in LHC Jets?
• Anomaly Detection under Coordinate Transformations
• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• Anomalies, Representations, and Self-Supervision
• Neural Embedding: Learning the Embedding of the Manifold of Physics Data
• Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics
• Mind the gap: The discrepancy between simulation and reality drives interpretations of the Galactic Center Excess
• Mixture-of-theories Training: Can We Find New Physics and Anomalies Better by Mixing Physical Theories?
• Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Tests
• Semi-visible jets, energy-based models, and self-supervision
• Non-resonant Anomaly Detection with Background Extrapolation
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions
• Simulation-based Anomaly Detection for Multileptons at the LHC
• How do the dynamics of the Milky Way -- Large Magellanic Cloud system affect gamma-ray constraints on particle dark matter?
• Detecting New Physics as Novelty -- Complementarity Matters
• Event Generation and Density Estimation with Surjective Normalizing Flows
• Finding excesses in model parameter space
• Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure
• Graph theory inspired anomaly detection at the LHC