Abstract
The Jiangmen Underground Neutrino Observatory (JUNO) experiment is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters with an unprecedented energy resolution of 3% at 1 MeV. It is composed of a 20 kton liquid scintillator central detector equipped with 18000 20-inch PMTs and 25000 3-inch PMTs, a water pool with 2000 20-inch PMTs, and a top tracker. Conditions data, coming from calibration and detector monitoring, are heterogeneous, different type of conditions data has different write rates, data format and data volume. JUNO conditions data management system (JCDMS) is developed to homogeneously treat all these heterogeneous conditions data in order to provide easy management and convenient access with both Restful API and web interfaces, support good scalability and maintenance for long time running. The paper describes the status and development of JCDMS including the data model, workflows, interfaces, data caching and performance of the system.
Highlights
The Jiangmen Underground Neutrino Observatory (JUNO) experiment is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters with an unprecedented energy resolution of 3% at 1 MeV
The Central Detector (CD) filled with 20 kton liquid scintillator (LS) and equipped with 18000 20-inch Photomultiplier Tubes (PMTs) and 25000 3-inch PMTs is submerged in a water pool for shielding from natural radioactivity
The water pool is quipped with 2000 20-inch PMTs
Summary
The Jiangmen Underground Neutrino Observatory (JUNO) experiment is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters with an unprecedented energy resolution of 3% at 1 MeV. They play very important roles during almost all event data processing and physics analysis. The conditions data mainly come from detector configuration, calibration as well as detector running and monitoring. They are produced or updated with different frequency, stored in different formats, and their sizes are different depending on the type of conditions data, from several kilobytes to several tens of gigabytes per year. The conditions data are heterogeneous, and they all vary with time It is a crucial task on how to effectively manage the conditions data, for short term access to these data, and for long term maintenance and evolution of conditions data
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