Optimal Transport Event Representation for Anomaly Detection
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910058629038080 |
|---|---|
| author | Cai, Tianji Bhargava, Aditya Nachman, Benjamin |
| author_facet | Cai, Tianji Bhargava, Aditya Nachman, Benjamin |
| contents | We introduce optimal transport (OT) as a physics-based intermediate event representation for weakly supervised anomaly detection. With only $0.5\%$ injection of resonant signals in the LHC Olympics benchmark datasets, the OT-augmented feature set achieves nearly twice the significance improvement of standard high-level observables provided in the benchmark, while end-to-end deep learning on low-level four-momenta struggles in the low-signal regime. The gains persist across signal types and classifiers, underscoring the value of structured representations in machine learning for anomaly detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04839 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal Transport Event Representation for Anomaly Detection Cai, Tianji Bhargava, Aditya Nachman, Benjamin High Energy Physics - Phenomenology High Energy Physics - Experiment We introduce optimal transport (OT) as a physics-based intermediate event representation for weakly supervised anomaly detection. With only $0.5\%$ injection of resonant signals in the LHC Olympics benchmark datasets, the OT-augmented feature set achieves nearly twice the significance improvement of standard high-level observables provided in the benchmark, while end-to-end deep learning on low-level four-momenta struggles in the low-signal regime. The gains persist across signal types and classifiers, underscoring the value of structured representations in machine learning for anomaly detection. |
| title | Optimal Transport Event Representation for Anomaly Detection |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2512.04839 |