Speaker
Description
The Jiangmen Underground Neutrino Observatory (JUNO) is a large-scale neutrino experiment with multiple physics goals. After many years of dedicated effort, the construction of the JUNO detector has been successfully completed, and physics data-taking officially commenced on August 26, 2025.
The detector readout system produces waveform data at a rate of approximately 40 GB/s at a 1 kHz trigger rate, making it impractical to store all the raw data. To address this challenge, the Online Event Classification (OEC) software is employed to reduce the data rate by more than two orders of magnitude. The OEC system saves reconstructed time/charge (T/Q) information for all events and selectively stores waveforms for events of interest, which will be used in subsequent offline precision reconstruction.
This contribution presents the software of the OEC system, including the multithreaded Low-Level Event Classification (LEC) module and the single-threaded High-Level Event Classification (HEC) module. In addition, a middleware layer has been developed to support the integration of offline algorithms into the online environment. Finally, we report on the computing performance observed during data-taking operations.