Speaker
Description
The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kiloton underground
liquid scintillator detector located in Guangdong, China, which is currently filling in the
liquid scintillator. JUNO's primary physics goal is to determine the neutrino mass
hierarchy with high precision. Additionally, it is designed to detect a wide range of
neutrino interactions, covering an energy range from MeV to GeV.
To efficiently manage the high event rate and diverse physics signals, JUNO implements
the Online Event Classification (OEC) system to pre-classify detected events and retain
essential raw information. The OEC system can use the offline waveform reconstruction
algorithm on the Data Acquisition (DAQ) system, while at the same time, the FPGA TQ
can also be used for waveform reconstruction. Additionally, the OEC will distinguish
different event types according to their physical characteristics, and the waveforms of
some event types that require storage will be stored.
By integrating optimized classification and data retention strategies, OEC plays a crucial
role in balancing bandwidth efficiency while preserving key physics data, supporting
JUNO’s extensive physics program as data-taking is now underway. This poster will
introduce how the OEC system is designed to classify events at JUNO.
