High-dimensional and permutation invariant anomaly detection

Journal Article · 28 February 2024 · SciPost Physics

DOI: https://doi.org/10.21468/SciPostPhys.16.3.062 · OSTI ID:2317701

Mikuni, Vinicius; Nachman, Benjamin

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.

• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2317701
• Alternate ID(s): OSTI ID: 2426880
• Journal Information: SciPost Physics, Journal Name: SciPost Physics Journal Issue: 3 Vol. 16; ISSN 2542-4653
• Publisher: Stichting SciPostCopyright Statement
• Country of Publication: Netherlands
• Language: English