High-dimensional and Permutation Invariant Anomaly Detection

• URL: http://arxiv.org/abs/2306.03933v5
• Date: Wed, 7 Feb 2024 18:16:43 GMT
• Title: High-dimensional and Permutation Invariant Anomaly Detection
• Authors: Vinicius Mikuni, Benjamin Nachman
• Abstract summary: We introduce a permutation-invariant density estimator for particle physics data based on diffusion models. We demonstrate the efficacy of our methodology by utilizing the learned density as a permutation-invariant anomaly detection score.
• Score: 0.1450405446885067
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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