Keep-up Production in JUNO’s Offline Data Processing

The Jiangmen Underground Neutrino Observatory (JUNO) is a cutting-edge scientific experiment designed to address fundamental questions in neutrino physics, including the determination of the neutrino mass ordering and precision measurements of neutrino oscillation parameters. To support the massive data processing requirements of JUNO, we have developed the Keep Up Production (KUP) pipeline, a robust and scalable system for managing the offline data processing workflow. This paper presents the architecture, design, and implementation of the KUP pipeline, highlighting its key components, including job management, data visualization, and the use of modern web technologies. We also discuss the challenges posed by the complexity and volume of data, and how the KUP pipeline addresses these challenges through automation, modularity, and real-time monitoring.

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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701176
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Keep-up Production in JUNO’s Offline Data Processing

Yizhou Zhang, Tao Lin, Weiqing Yin
EPJ Web of Conferences
Distributed and Parallel Computing Systems
article

Keep-up Production in JUNO’s Offline Data Processing

Yizhou Zhang, Tao Lin, Weiqing Yin
article en

Abstract

The Jiangmen Underground Neutrino Observatory (JUNO) is a cutting-edge scientific experiment designed to address fundamental questions in neutrino physics, including the determination of the neutrino mass ordering and precision measurements of neutrino oscillation parameters. To support the massive data processing requirements of JUNO, we have developed the Keep Up Production (KUP) pipeline, a robust and scalable system for managing the offline data processing workflow. This paper presents the architecture, design, and implementation of the KUP pipeline, highlighting its key components, including job management, data visualization, and the use of modern web technologies. We also discuss the challenges posed by the complexity and volume of data, and how the KUP pipeline addresses these challenges through automation, modularity, and real-time monitoring.

EPJ Web of ConferencesVol. 337
Chinese Academy of Sciences (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Youth Innovation Promotion Association
Openalex Percentile: Top 68%
Distributed and Parallel Computing Systems
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Keep-up Production in JUNO’s Offline Data Processing — Yizhou Zhang, Tao Lin, et al. · EPJ Web of Conferences (2025) | TGRS Research Map | TGRS