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.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-10-07
DOI
https://doi.org/10.1051/epjconf/202533701176/pdf
Primary Topic
Neutrino Physics Research
Type
article
Field-Weighted Citation Impact
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article

Keep-up Production in JUNO’s Offline Data Processing

Weiqing Yin, Yizhou Zhang, Tao Lin
Springer Link (Chiba Institute of Technology)
Neutrino Physics Research
article

Keep-up Production in JUNO’s Offline Data Processing

Weiqing Yin, Yizhou Zhang, Tao Lin
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.

Springer Link (Chiba Institute of Technology)
Decent work and economic growth
Openalex Percentile: Top 18%
Neutrino Physics Research
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Keep-up Production in JUNO’s Offline Data Processing — Weiqing Yin, Yizhou Zhang, et al. · Springer Link (Chiba Institute of Technology) (2025) | TGRS Research Map | TGRS