Publishing unbinned differential cross section results
Name
2109.13243.pdf
Description
Submitted version
Size
639.97 KB
Format
Adobe PDF
Checksum (MD5)
41c992d0634e6972d7562780185f624c
Author(s)
Arratia, Miguel
Butter, Anja
Campanelli, Mario
Croft, Vincent
Gillberg, Dag
Ghosh, Aishik
Lohwasser, Kristin
Malaescu, Bogdan
Mikuni, Vinicius
Nachman, Benjamin
Date Issued
January 1, 2022
Journal
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
Abstract
Abstract
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.
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.
MIT Department
Massachusetts Institute of Technology. Center for Theoretical Physics
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Attribution-NonCommercial-ShareAlike 4.0 International
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DOI of Published Version
https://doi.org/10.1088/1748-0221/17/01/p01024
