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...
| Main Authors: | , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2022
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| Online Access: | https://hdl.handle.net/1721.1/142237 |
| _version_ | 1811074950972309504 |
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| author | 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 |
| author2 | Massachusetts Institute of Technology. Center for Theoretical Physics |
| author_facet | Massachusetts Institute of Technology. Center for Theoretical Physics 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 |
| author_sort | Arratia, Miguel |
| collection | MIT |
| description | <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> |
| first_indexed | 2024-09-23T09:58:01Z |
| format | Article |
| id | mit-1721.1/142237 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:58:01Z |
| publishDate | 2022 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1422372023-04-14T18:34:52Z 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 Massachusetts Institute of Technology. Center for Theoretical Physics <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> 2022-05-02T19:02:08Z 2022-05-02T19:02:08Z 2022-01-01 2022-05-02T18:59:16Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142237 Arratia, Miguel, Butter, Anja, Campanelli, Mario, Croft, Vincent, Gillberg, Dag et al. 2022. "Publishing unbinned differential cross section results." Journal of Instrumentation, 17 (01). en 10.1088/1748-0221/17/01/p01024 Journal of Instrumentation Attribution-NonCommercial-ShareAlike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IOP Publishing arXiv |
| spellingShingle | 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 Publishing unbinned differential cross section results |
| title | Publishing unbinned differential cross section results |
| title_full | Publishing unbinned differential cross section results |
| title_fullStr | Publishing unbinned differential cross section results |
| title_full_unstemmed | Publishing unbinned differential cross section results |
| title_short | Publishing unbinned differential cross section results |
| title_sort | publishing unbinned differential cross section results |
| url | https://hdl.handle.net/1721.1/142237 |
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