Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey

被引：26

作者：

Wang, Jason J. ^{[1
]}

Perrin, Marshall D. ^{[2
]}

Savransky, Dmitry ^{[3
]}

Arriaga, Pauline ^{[4
]}

Chilcote, Jeffrey K. ^{[5
,6
,7
]}

De Rosa, Robert J. ^{[1
]}

Millar-Blanchaer, Maxwell A. ^{[8
]}

Marois, Christian ^{[9
,10
]}

Rameau, Julien ^{[11
,12
]}

Wolff, Schuyler G.

Shapiro, Jacob ^{[3
]}

Ruffio, Jean-Baptiste ^{[5
]}

Maire, Jerome ^{[13
]}

Marchis, Franck ^{[14
]}

Graham, James R. ^{[1
]}

Macintosh, Bruce ^{[5
]}

Ammons, S. Mark ^{[15
]}

Bailey, Vanessa P. ^{[5
]}

Barman, Travis S. ^{[16
]}

Bruzzone, Sebastian ^{[17
]}

Bulger, Joanna ^{[18
]}

Cotten, Tara ^{[19
]}

Doyon, Rene ^{[13
]}

Duchene, Gaspard ^{[1
,20
]}

Fitzgerald, Michael P. ^{[4
]}

Follette, Katherine B. ^{[21
]}

Goodsell, Stephen ^{[22
]}

Greenbaum, Alexandra Z. ^{[23
]}

Hibon, Pascale ^{[24
]}

Hung, Li-Wei ^{[25
]}

Ingraham, Patrick ^{[26
]}

Kalas, Paul ^{[1
]}

Konopacky, Quinn M. ^{[13
]}

Larkin, James E.

Marley, Mark S. ^{[27
]}

Metchev, Stanimir ^{[28
]}

Nielsen, Eric L. ^{[5
,14
]}

Oppenheimer, Rebecca ^{[29
]}

Palmer, David W.

Patience, Jennifer ^{[30
]}

Poyneer, Lisa A.

Pueyo, Laurent ^{[2
]}

Rajan, Abhijith ^{[2
]}

Rantakyro, Fredrik T.

Schneider, Adam C. ^{[30
]}

Sivaramakrishnan, Anand ^{[2
]}

Song, Inseok

Soummer, Remi ^{[2
]}

Thomas, Sandrine

Wallace, J. Kent ^{[8
]}

机构：

[1] Univ Calif Berkeley, Dept Astron, Berkeley, CA USA

[2] Space Telescope Sci Inst, 3700 San Martin Dr, Baltimore, MD 21218 USA

[3] Cornell Univ, Sibley Sch Mech & Aerosp Engn, Ithaca, NY 14853 USA

[4] UCLA, Dept Phys & Astron, Los Angeles, CA USA

[5] Stanford Univ, Kavli Inst Particle Astrophys & Cosmol, Stanford, CA 94305 USA

[6] Univ Toronto, Dunlap Inst Astron & Astrophys, Toronto, ON, Canada

[7] Univ Notre Dame, Dept Phys, Notre Dame, IN USA

[8] CALTECH, Jet Prop Lab, Pasadena, CA USA

[9] Natl Res Council Canada Herzberg, Victoria, BC, Canada

[10] Univ Victoria, Phys & Astron Dept, Victoria, BC, Canada

[11] Univ Montreal, Inst Rech Exoplanetes, Dept Phys, Montreal, PQ, Canada

[12] Johns Hopkins Univ, Dept Phys & Astron, Baltimore, MD 21218 USA

[13] Univ Calif San Diego, Ctr Astrophys & Space Sci, La Jolla, CA 92093 USA

[14] Carl Sagan Ctr, SETI Inst, Mountain View, CA USA

[15] Lawrence Livermore Natl Lab, Livermore, CA USA

[16] Univ Arizona, Lunar & Planetary Lab, Tucson, AZ 85721 USA

[17] Univ Western Ontario, Dept Phys & Astron, Ctr Planetary Sci & Explorat, London, ON, Canada

[18] NAOJ, Subaru Telescope, Hilo, HI USA

[19] Univ Georgia, Dept Phys & Astron, Athens, GA 30602 USA

[20] Univ Grenoble Alpes, IPAG, CNRS, Grenoble, France

[21] Merrill Sci Ctr, Amherst Coll, Dept Phys & Astron, Amherst, MA USA

[22] Gemini Observ, Hilo, HI USA

[23] Univ Michigan, Dept Astron, Ann Arbor, MI 48109 USA

[24] European Southern Observ, Santiago, Chile

[25] Natl Pk Serv, Nat Sounds & Night Skies Div, Ft Collins, CO USA

[26] Large Synopt Survey Telescope, Tucson, AZ USA

[27] NASA, Ames Res Ctr, Div Space Sci, Moffett Field, CA 94035 USA

[28] SUNY Stony Brook, Dept Phys & Astron, Stony Brook, NY 11794 USA

[29] Amer Museum Nat Hist, Dept Astrophys, New York, NY 10024 USA

[30] Arizona State Univ, Sch Earth & Space Explorat, Tempe, AZ USA

[31] Aerosp Corp, El Segundo, CA 90245 USA

来源：

JOURNAL OF ASTRONOMICAL TELESCOPES INSTRUMENTS AND SYSTEMS | 2018年 / 4卷 / 01期

基金：

美国国家科学基金会;

关键词：

high contrast imaging; exoplanets; circumstellar disks; data processing; Gemini planet imager; Data Cruncher; PERFORMANCE; PHOTOMETRY; ASTROMETRY;

D O I：

10.1117/1.JATIS.4.1.018002

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

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. (c) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

引用

页数：14

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