Publishing unbinned differential cross section results

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

doi:10.1088/1748-0221/17/01/p01024

Publishing unbinned differential cross section results

Journal Article · 19 January 2022 · Journal of Instrumentation

DOI:https://doi.org/10.1088/1748-0221/17/01/p01024· OSTI ID:1871016

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

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.

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Research Organization:: Thomas Jefferson National Accelerator Facility, Newport News, VA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Nuclear Physics (NP)

DOE Contract Number:: AC02-05CH11231; SC0012567

OSTI ID:: 1871016

Report Number(s):: JLAB-PHY-22-3624; DOE/OR/23177-5502; arXiv:2109.13243

Journal Information:: Journal of Instrumentation, Journal Name: Journal of Instrumentation Journal Issue: 01 Vol. 17; ISSN 1748-0221

Publisher:: Institute of Physics (IOP)

Country of Publication:: United States

Language:: English

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