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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. 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...

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Detalles Bibliográficos
Autor principal:	CMS Collaboration
Lenguaje:	eng
Publicado:	2023
Materias:	Detectors and Experimental Techniques
Acceso en línea:	http://cds.cern.ch/record/2875710

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