The Vera C. Rubin Observatory Prompt Processing System
Krzysztof Findeisen (1), Kian-Tat Lim (2), Dan Speck (3), Hsin-Fang Chiang (2), Erin Leigh Howard (1), Ian S. Sullivan (1), Eric C. Bellm (1) ((1) University of Washington, (2) SLAC National Accelerator Laboratory, (3) Burwood Group)
TL;DR
The paper describes the Prompt Processing system of the Vera C. Rubin Observatory, which automatically processes vast amounts of raw image data to generate millions of transient alerts efficiently and reliably.
Contribution
It details the system architecture and performance results that demonstrate meeting high throughput, low latency, and reliability standards during commissioning.
Findings
System processes 10 TB of images nightly
Generates up to 10 million transient alerts per night
Meets throughput, latency, and reliability requirements
Abstract
Vera C. Rubin Observatory's Prompt Processing system will automatically process 10 TB of raw images to produce up to 10 million transient alerts per night. We summarize how Prompt Processing meets its throughput, latency, and reliability requirements and present results from Rubin Observatory Commissioning.
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Taxonomy
TopicsAstrophysics and Cosmic Phenomena · Astronomy and Astrophysical Research · Gamma-ray bursts and supernovae