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arXiv 2310.07342hep-ex
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高能物理实验中的培训与入职举措

Training and Onboarding initiatives in High Energy Physics experiments

S. Hageboeck, A. Reinsvold Hall, N. Skidmore, G. A. Stewart, G. Benelli, B. Carlson, C. David, J. Davies, W. Deconinck, D. DeMuth,, P. Elmer, R. B. Garg, K. Lie… 展开作者

S. Hageboeck, A. Reinsvold Hall, N. Skidmore, G. A. Stewart, G. Benelli, B. Carlson, C. David, J. Davies, W. Deconinck, D. DeMuth,, P. Elmer, R. B. Garg, K. Lieret, V. Lukashenko, S. Malik, A. Morris, H. Schellman, J. Veatch, M. Hernandez Villanueva

AI总结:

本文记录并分析了ATLAS、CMS、LHCb、Belle II和DUNE等高能物理实验的分析软件培训与入职举措,旨在为未来实验确定关键考量因素。

摘要:

本文记录了ATLAS、CMS、LHCb、Belle II和DUNE等高能物理(HEP)实验当前的分析软件培训与入职活动。随着数据集不断增大,分析和相关软件日益复杂,新合作成员的快速高效入职对HEP实验愈发重要。2022年,HEP Software Foundation(HSF)举办了一系列会议,供各实验展示其举措。我们在此对这些举措进行记录与分析,试图为未来实验确定一系列关键考量因素。

英文摘要:

In this paper we document the current analysis software training and onboarding activities in several High Energy Physics (HEP) experiments: ATLAS, CMS, LHCb, Belle II and DUNE. Fast and efficient onboarding of new collaboration members is increasingly important for HEP experiments as analyses and the related software become ever more complex with growing datasets. A meeting series was held by the HEP Software Foundation (HSF) in 2022 for experiments to showcase their initiatives. Here we document and analyse these in an attempt to determine a set of key considerations for future experiments.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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