The automated data processing architecture for the GPI Exoplanet Survey

Jason J. Wang; Marshall D. Perrin; Dmitry Savransky; Pauline Arriaga; Jeffrey K. Chilcote; Robert J. De Rosa; Maxwell A. Millar-Blanchaer; Christian Marois; Julien Rameau; Schuyler G. Wolff; Jacob Shapiro; Jean-Baptiste Ruffio; James R. Graham; Bruce Macintosh

doi:10.1117/12.2274531

1 September 2017 The automated data processing architecture for the GPI Exoplanet Survey

Jason J. Wang, Marshall D. Perrin, Dmitry Savransky, Pauline Arriaga, Jeffrey K. Chilcote, Robert J. De Rosa, Maxwell A. Millar-Blanchaer, Christian Marois, Julien Rameau, Schuyler G. Wolff, Jacob Shapiro, Jean-Baptiste Ruffio, James R. Graham, Bruce Macintosh

Author Affiliations +

Proceedings Volume 10400, Techniques and Instrumentation for Detection of Exoplanets VIII; 1040026 (2017) https://doi.org/10.1117/12.2274531
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

The Gemini Planet Imager Exoplanet Survey (GPIES) is a multi-year 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 GPIES Data Cruncher, combines multiple data reduction pipelines together to intelligently 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 data reduction pipelines. 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.

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Jason J. Wang, Marshall D. Perrin, Dmitry Savransky, Pauline Arriaga, Jeffrey K. Chilcote, Robert J. De Rosa, Maxwell A. Millar-Blanchaer, Christian Marois, Julien Rameau, Schuyler G. Wolff, Jacob Shapiro, Jean-Baptiste Ruffio, James R. Graham, and Bruce Macintosh "The automated data processing architecture for the GPI Exoplanet Survey", Proc. SPIE 10400, Techniques and Instrumentation for Detection of Exoplanets VIII, 1040026 (1 September 2017); https://doi.org/10.1117/12.2274531

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