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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. We show first results on the application of conditional invertible networks (cINNs) to this challenge. Specifically, we construct and train a cINN to learn the...
| Autores principales: | , , , |
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| Lenguaje: | eng |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2872295 |