Speaker
Description
JUNO-TAO, the near detector for JUNO, is designed to provide high-statistics and high-resolution reference energy spectrum for the neutrino mass ordering analysis of JUNO. To achieve this goal, it is necessary to monitor the data quality of the detector operation and provide timely feedback on detector anomalies. In this talk, we present an online Data Quality Monitoring (DQM) system for the TAO experiment. The system contains two main parts. First, a backend Job Server, triggered by Kafka messages, processes RTRAW/ESD data to generate per-channel quality metrics and reconstruction information. Second, a Flask-based Web Server provides interactive visualization service via APIs. Three monitoring functionalities are provided: (1) channel-level SiPM quality monitoring, including the dark count rate (DCR), gain, time-offset, and occupancy, which are displayed as 2D channel maps; (2) long-term detector stability trending of DCR, gain, and the rate of cosmic ray muons passing through the detector; (3) per-run reconstruction monitoring. The system has successfully been deployed for routine TAO data-taking operations and has been running stably since February 2026.
Keywords: JUNO-TAO; Data Quality Monitoring; Channel Quality; Detector Stability; Reconstruction
| 请选择分会 | 中微子物理、粒子天体物理与宇宙学 |
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