CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals

We propose a new model independent technique for constructing background data templates for use in searches for new physics processes at the LHC. This method, called Curtains, uses invertible neural networks to parameterise the distribution of side band data as a function of the resonant observable....

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Detalles Bibliográficos
Autores principales: Raine, John Andrew, Klein, Samuel, Sengupta, Debajyoti, Golling, Tobias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10072325/
https://www.ncbi.nlm.nih.gov/pubmed/37025653
http://dx.doi.org/10.3389/fdata.2023.899345

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10072325/
https://www.ncbi.nlm.nih.gov/pubmed/37025653
http://dx.doi.org/10.3389/fdata.2023.899345

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