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Journal Articles Journal of Astronomical Telescopes, Instruments, and Systems Year : 2018

Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey

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Jason J. Wang
  • Function : Author
Marshall D. Perrin
Dmitry Savransky
  • Function : Author
Pauline Arriaga
Jeffrey K. Chilcote
  • Function : Author
Robert J. de Rosa
  • Function : Author
Maxwell A. Millar-Blanchaer
  • Function : Author
Christian Marois
Julien Rameau
  • Function : Author
Schuyler G. Wolff
  • Function : Author
Jacob Shapiro
  • Function : Author
Jean-Baptiste Ruffio
  • Function : Author
Jérôme Maire
  • Function : Author
Franck Marchis
James R. Graham
  • Function : Author
Bruce Macintosh
S. Mark Ammons
  • Function : Author
Vanessa P. Bailey
  • Function : Author
Travis S. Barman
  • Function : Author
Sebastian Bruzzone
  • Function : Author
Joanna Bulger
  • Function : Author
Tara Cotten
  • Function : Author
René Doyon
Michael P. Fitzgerald
  • Function : Author
Katherine B. Follette
  • Function : Author
Stephen Goodsell
  • Function : Author
Alexandra Z. Greenbaum
  • Function : Author
Pascale Hibon
  • Function : Author
Li-Wei Hung
  • Function : Author
Patrick Ingraham
  • Function : Author
Paul Kalas
Quinn M. Konopacky
  • Function : Author
James E. Larkin
  • Function : Author
Mark S. Marley
  • Function : Author
Stanimir Metchev
  • Function : Author
Eric L. Nielsen
  • Function : Author
Rebecca Oppenheimer
  • Function : Author
David W. Palmer
  • Function : Author
Jennifer Patience
  • Function : Author
Lisa A. Poyneer
  • Function : Author
Laurent Pueyo
  • Function : Author
Abhijith Rajan
  • Function : Author
Fredrik T. Rantakyrö
  • Function : Author
Adam C. Schneider
  • Function : Author
Anand Sivaramakrishnan
  • Function : Author
Inseok Song
  • Function : Author
Remi Soummer
  • Function : Author
Sandrine Thomas
  • Function : Author
J. Kent Wallace
  • Function : Author
Kimberly Ward-Duong
  • Function : Author
Sloane J. Wiktorowicz
  • Function : Author

Abstract

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.

Dates and versions

insu-03693580 , version 1 (10-06-2022)

Identifiers

Cite

Jason J. Wang, Marshall D. Perrin, Dmitry Savransky, Pauline Arriaga, Jeffrey K. Chilcote, et al.. Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey. Journal of Astronomical Telescopes, Instruments, and Systems, 2018, 4, ⟨10.1117/1.JATIS.4.1.018002⟩. ⟨insu-03693580⟩
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