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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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Bibliografiset tiedot
Päätekijät: John Andrew Raine, Samuel Klein, Debajyoti Sengupta, Tobias Golling
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: Frontiers Media S.A. 2023-03-01
Sarja:Frontiers in Big Data
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Linkit:https://www.frontiersin.org/articles/10.3389/fdata.2023.899345/full
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