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"LHCb剥离项目":可持续的传统数据处理,用于高能物理

Nathan Allen Grieser¹, Eduardo Rodrigues², Niladri Sahoo³

¹University of Cincinnati, Cincinnati, USA.

Computing and software for big science

|December 1, 2025

概括

在LHCb剥离项目精炼碰撞数据进行分析,优化处理遗留和实时数据集. 这确保了LHCb协作对有价值的物理数据的持续访问.

科学领域:

高能物理高能物理
粒子物理学数据分析数据分析

背景情况:

LHCb实验产生了大量的碰撞数据,需要复杂的处理.
高效的数据精制对于离线分析和科学发现至关重要.

研究的目的:

提供LHCb剥离项目的框架和操作的全面概述.
详细介绍数据处理基础设施和竞选管理方面的进展.
讨论遗留和实时数据处理的挑战和未来方向.

主要方法:

对于Stripping框架使用一个Python可配置的架构.
将框架与LHCb计算系统集成,用于大规模的竞选管理.
实现基于GitLab的工作流程,持续集成和自动化,以实现高效的处理.

主要成果:

成功地重新分析了运行1和2的旧数据.
优化基础设施的遗留和实时数据处理.
组织和计算处理技术的进步.

结论:

剥离项目对于管理LHCb数据至关重要,确保访问有价值的物理数据集.
持续优化和采用现代化工作流程是高效数据处理的关键.
学到的经验教训将指导未来的路线图,用于持续的数据访问和分析.

关键词:

数据处理和离线分析.高能物理学的高能物理在LHCb实验中,

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