High-dimensional and Permutation Invariant Anomaly Detection

Abstract

Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particularly at the constituent level, incorporating desirable properties such as permutation invariance and variable-length inputs becomes difficult within popular density estimation methods. In this work, we introduce a permutation-invariant density estimator for particle physics data based on diffusion models, specifically designed to handle variable-length inputs. We demonstrate the efficacy of our methodology by utilizing the learned density as a permutation-invariant anomaly detection score, effectively identifying jets with low likelihood under the background-only hypothesis. To validate our density estimation method, we investigate the ratio of learned densities and compare to those obtained by a supervised classification algorithm.

7 pages, 5 figures

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Cited by in corpus (10)

• Machine Learning for Anomaly Detection in Particle Physics
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• A Method to Simultaneously Facilitate All Jet Physics Tasks
• Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
• Anomaly detection with flow-based fast calorimeter simulators
• Non-resonant Anomaly Detection with Background Extrapolation
• Unifying Simulation and Inference with Normalizing Flows
• Semi-supervised permutation invariant particle-level anomaly detection
• Data-Driven High-Dimensional Statistical Inference with Generative Models
• Variational inference for pile-up removal at hadron colliders with diffusion models