Speaker
Description
(on behalf of the JUNO Collaboration)
Jiangmen Underground Neutrino Observatory (JUNO), under construction in southern China, is a multi-purpose neutrino experiment designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters. Equipped with a 20-kton liquid scintillator central detector viewed by 17,612 20-inch and 25,6000 3-inch photomultiplier tubes, JUNO could reach the unprecedented energy resolution of 3% at 1 MeV.
JUNO is expected to start data taking in 2024 and plans to run for more than 20 years with about 2 petabytes of raw data each year. The large volume of data has brought a great challenge to the JUNO offline data processing and analysis.
This contribution will comprehensively review the development of JUNO offline software (JUNOSW) which started in 2012 in order to support JUNO’s specific requirements, and will particularly highlight the following topics:
1） Data processing framework which supports buffering and management of multiple events, event splitting and mixing, TBB-based multi-threading, and integration of machine learning etc.
2）Unified detector geometry management to support multiple applications including simulation, calibration, reconstruction and detector visualization.
3）ROOT based event data model charactering data representations at different processing stages and complicated relationships between them.
4）Event index based correlation analysis to support selection of sparse physics events from the large volume of data.
The JUNO data processing and analysis chain was completed and has been used by several rounds of Monte Carlo data challenge on both local computing clusters and the distributed computing infrastructure.
| Consider for long presentation | Yes |
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