Publishing unbinned differential cross section results

Publishing unbinned differential cross section results

<jats:title>Abstract</jats:title> <jats:p>Machine learning tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many dimensions. Unbinned measurements can enable, improve, or at le...

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Bibliographic Details
Main Authors: Arratia, Miguel, Butter, Anja, Campanelli, Mario, Croft, Vincent, Gillberg, Dag, Ghosh, Aishik, Lohwasser, Kristin, Malaescu, Bogdan, Mikuni, Vinicius, Nachman, Benjamin, Rojo, Juan, Thaler, Jesse, Winterhalder, Ramon
Other Authors: Massachusetts Institute of Technology. Center for Theoretical Physics
Format: Article
Language:English
Published: IOP Publishing 2022
Online Access:https://hdl.handle.net/1721.1/142237

Internet

https://hdl.handle.net/1721.1/142237

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