Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey

作者：Wang Jason J; Perrin Marshall D; Savransky Dmitry; Arriaga Pauline; Chilcote Jeffrey K; De Rosa Robert J; Millar Blanchaer Maxwell A; Marois Christian; Rameau Julien; Wolff Schuyler G; Shapiro Jacob; Ruffio Jean Baptiste; Maire Jerome; Marchis Franck; Graham James R; Macintosh Bruce; Ammons S Mark; Bailey Vanessa P; Barman Travis S; Bruzzone Sebastian; Bulger Joanna; Cotten Tara; Doyon Rene; Duchene Gaspard; Fitzgerald Michael P; Follette Katherine B

来源：Journal of Astronomical Telescopes, Instruments, and Systems, 2018, 4(1): 018002.

DOI：10.1117/1.JATIS.4.1.018002

• high contrast imaging
• exoplanets
• circumstellar disks
• data processing
• Gemini planet imager
• Data Cruncher

The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.

• 出版日期2018-1
• 单位UCLA