High-dimensional and Permutation Invariant Anomaly Detection
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914702962982912 |
|---|---|
| author | Mikuni, Vinicius Nachman, Benjamin |
| author_facet | Mikuni, Vinicius Nachman, Benjamin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_03933 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | High-dimensional and Permutation Invariant Anomaly Detection Mikuni, Vinicius Nachman, Benjamin High Energy Physics - Phenomenology Artificial Intelligence Machine Learning High Energy Physics - Experiment 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. |
| title | High-dimensional and Permutation Invariant Anomaly Detection |
| topic | High Energy Physics - Phenomenology Artificial Intelligence Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2306.03933 |