Publishing unbinned differential cross section results
Author(s)
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; ... Show more Show less
DownloadSubmitted version (639.9Kb)
Open Access Policy
Open Access Policy
Creative Commons Attribution-Noncommercial-Share Alike
Terms of use
Metadata
Show full item recordAbstract
<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 least simplify comparisons between experiments and with theoretical predictions. Furthermore, many-dimensional measurements can be used to define observables after the measurement instead of before. There is currently no community standard for publishing unbinned data. While there are also essentially no measurements of this type public, unbinned measurements are expected in the near future given recent methodological advances. The purpose of this paper is to propose a scheme for presenting and using unbinned results, which can hopefully form the basis for a community standard to allow for integration into analysis workflows. This is foreseen to be the start of an evolving community dialogue, in order to accommodate future developments in this field that is rapidly evolving.</jats:p>
Date issued
2022-01-01Department
Massachusetts Institute of Technology. Center for Theoretical PhysicsJournal
Journal of Instrumentation
Publisher
IOP Publishing
Citation
Arratia, Miguel, Butter, Anja, Campanelli, Mario, Croft, Vincent, Gillberg, Dag et al. 2022. "Publishing unbinned differential cross section results." Journal of Instrumentation, 17 (01).
Version: Original manuscript