Deep Set Auto Encoders for Anomaly Detection in Particle Physics

Abstract

There is an increased interest in model agnostic search strategies for physics beyond the standard model at the Large Hadron Collider. We introduce a Deep Set Variational Autoencoder and present results on the Dark Machines Anomaly Score Challenge. We find that the method attains the best anomaly detection ability when there is no decoding step for the network, and the anomaly score is based solely on the representation within the encoded latent space. This method was one of the top-performing models in the Dark Machines Challenge, both for the open data sets as well as the blinded data sets.

arXiv commentv2: change metrics for analyzing models, conclusions unchanged

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Cited by in corpus (19)

• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• Anomaly detection in high-energy physics using a quantum autoencoder
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Symmetries, Safety, and Self-Supervision
• Learning new physics efficiently with nonparametric methods
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• 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
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• Simulation-based Anomaly Detection for Multileptons at the LHC
• Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging
• Foundations of automatic feature extraction at LHC--point clouds and graphs
• Event Generation and Density Estimation with Surjective Normalizing Flows
• Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure