Cargando…
A Method for Inferring Signal Strength Modifiers by Conditional Invertible Neural Networks
The continuous growth in model complexity in high-energy physics (HEP) collider experiments demands increasingly time-consuming model fits. As parameter inference with conditional invertible neural networks (cINNs) is very fast, the application of these versatile networks in a VH-Analysis at CMS is...
| Autor principal: | |
|---|---|
| Lenguaje: | eng |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2875710 |