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The Vera C. Rubin Observatory will produce an unprecedented astronomical dataset for studies of the deep and dynamic universe. Its Legacy Survey of Spaceand Time (LSST) will image the entire southern sky every three to four days andproduce tens of petabytes of raw image data and associated calibration dataover the course of the experiment's run. More than 20 terabytes of data must bestored every night, and annual campaigns to reprocess the entire dataset sincethe beginning of the survey will be conducted over ten years. The Productionand Distributed Analysis (PanDA) system was evaluated by the Rubin ObservatoryData Management team and selected to serve the Observatory's needs due to itsdemonstrated scalability and flexibility over the years, for its DirectedAcyclic Graph (DAG) support, its support for multi-site processing, and itshighly scalable complex workflows via the intelligent Data Delivery Service(iDDS). PanDA is also being evaluated for prompt processing where data must beprocessed within 60 seconds after image capture. This paper will brieflydescribe the Rubin Data Management system and its Data Facilities (DFs).Finally, it will describe in depth the work performed in order to integrate thePanDA system with the Rubin Observatory to be able to run the Rubin SciencePipelines using PanDA.

Integrating the PanDA Workload Management System with the Vera C. Rubin Observatory