Equivariant, safe and sensitive — graph networks for new physics
Abstract This study introduces a novel Graph Neural Network (GNN) architecture that leverages infrared and collinear (IRC) safety and equivariance to enhance the analysis of collider data for Beyond the Standard Model (BSM) discoveries. By integrating equivariance in the rapidity-azimuth plane with...
Сохранить в:
| Главные авторы: | , , , , |
|---|---|
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
SpringerOpen
2024-07-01
|
| Серии: | Journal of High Energy Physics |
| Предметы: | |
| Online-ссылка: | https://doi.org/10.1007/JHEP07(2024)245 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
|