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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

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此摘要是机器生成的。

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.

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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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.