High-dimensional and permutation invariant anomaly detection
Vinicius Mikuni, Benjamin Nachman
SciPost Phys. 16, 062 (2024) · published 29 February 2024
- doi: 10.21468/SciPostPhys.16.3.062
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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.
Cited by 6
Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Vinicius Mikuni,
- 1 2 Benjamin Nachman