FunTuple:用于 LHCb 实验离线数据处理的新 N 元组组件。

Q1 Computer Science
Computing and Software for Big Science Pub Date : 2024-01-01 Epub Date: 2024-02-24 DOI:10.1007/s41781-024-00116-1
Abhijit Mathad, Martina Ferrillo, Sacha Barré, Patrick Koppenburg, Patrick Owen, Gerhard Raven, Eduardo Rodrigues, Nicola Serra
{"title":"FunTuple:用于 LHCb 实验离线数据处理的新 N 元组组件。","authors":"Abhijit Mathad, Martina Ferrillo, Sacha Barré, Patrick Koppenburg, Patrick Owen, Gerhard Raven, Eduardo Rodrigues, Nicola Serra","doi":"10.1007/s41781-024-00116-1","DOIUrl":null,"url":null,"abstract":"<p><p>The offline software framework of the LHCb experiment has undergone a significant overhaul to tackle the data processing challenges that will arise in the upcoming Run 3 and Run 4 of the Large Hadron Collider. This paper introduces FunTuple, a novel component developed for offline data processing within the LHCb experiment. This component enables the computation and storage of a diverse range of observables for both reconstructed and simulated events by leveraging on the tools initially developed for the trigger system. This feature is crucial for ensuring consistency between trigger-computed and offline-analysed observables. The component and its tool suite offer users flexibility to customise stored observables, and its reliability is validated through a full-coverage set of rigorous unit tests. This paper comprehensively explores FunTuple's design, interface, interaction with other algorithms, and its role in facilitating offline data processing for the LHCb experiment for the next decade and beyond.</p>","PeriodicalId":36026,"journal":{"name":"Computing and Software for Big Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11358189/pdf/","citationCount":"0","resultStr":"{\"title\":\"FunTuple: A New N-tuple Component for Offline Data Processing at the LHCb Experiment.\",\"authors\":\"Abhijit Mathad, Martina Ferrillo, Sacha Barré, Patrick Koppenburg, Patrick Owen, Gerhard Raven, Eduardo Rodrigues, Nicola Serra\",\"doi\":\"10.1007/s41781-024-00116-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The offline software framework of the LHCb experiment has undergone a significant overhaul to tackle the data processing challenges that will arise in the upcoming Run 3 and Run 4 of the Large Hadron Collider. This paper introduces FunTuple, a novel component developed for offline data processing within the LHCb experiment. This component enables the computation and storage of a diverse range of observables for both reconstructed and simulated events by leveraging on the tools initially developed for the trigger system. This feature is crucial for ensuring consistency between trigger-computed and offline-analysed observables. The component and its tool suite offer users flexibility to customise stored observables, and its reliability is validated through a full-coverage set of rigorous unit tests. This paper comprehensively explores FunTuple's design, interface, interaction with other algorithms, and its role in facilitating offline data processing for the LHCb experiment for the next decade and beyond.</p>\",\"PeriodicalId\":36026,\"journal\":{\"name\":\"Computing and Software for Big Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11358189/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computing and Software for Big Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s41781-024-00116-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/2/24 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computing and Software for Big Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s41781-024-00116-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/24 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0

摘要

为了应对大型强子对撞机即将进行的运行 3 和运行 4 中出现的数据处理挑战,大型强子对撞机 b 实验的离线软件框架进行了重大改革。本文介绍了为大型强子对撞机实验离线数据处理而开发的新型组件 FunTuple。通过利用最初为触发系统开发的工具,该组件能够计算和存储重建和模拟事件的各种观测值。这一功能对于确保触发计算的观测数据与离线分析的观测数据之间的一致性至关重要。该组件及其工具套件为用户提供了定制存储观测值的灵活性,其可靠性通过一套全覆盖的严格单元测试得到了验证。本文全面探讨了 FunTuple 的设计、界面、与其他算法的交互,以及它在未来十年及以后促进 LHCb 实验离线数据处理方面的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FunTuple: A New N-tuple Component for Offline Data Processing at the LHCb Experiment.

The offline software framework of the LHCb experiment has undergone a significant overhaul to tackle the data processing challenges that will arise in the upcoming Run 3 and Run 4 of the Large Hadron Collider. This paper introduces FunTuple, a novel component developed for offline data processing within the LHCb experiment. This component enables the computation and storage of a diverse range of observables for both reconstructed and simulated events by leveraging on the tools initially developed for the trigger system. This feature is crucial for ensuring consistency between trigger-computed and offline-analysed observables. The component and its tool suite offer users flexibility to customise stored observables, and its reliability is validated through a full-coverage set of rigorous unit tests. This paper comprehensively explores FunTuple's design, interface, interaction with other algorithms, and its role in facilitating offline data processing for the LHCb experiment for the next decade and beyond.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computing and Software for Big Science
Computing and Software for Big Science Computer Science-Computer Science (miscellaneous)
CiteScore
6.20
自引率
0.00%
发文量
15
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信