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An Integrated Data System for the gp Imager Exoplanet Survey Campaign Marshall D. Perrin, Christian Marois, Jérôme Maire, Jeffrey Chilcote, Michael P. Fitzgerald, Raphael Galicher, Kathleen Morzinski, Laurent Pueyo, Rémi Soummer, James R. Graham, Bruce A. Macintosh We draw our target list from published young association catalogs and a proprietary list—the STScI, Herzberg Institute of , Dunlap Institute for , UCLA, Steward Observatory, & Lawrence Livermore National Labresult of thousands of hours of telescope observations in preparation for GPI—that adds several hundred new young (<100 Myr, <75 pc) and adolescent (<300 Myr, <35 pc) stars (Patience, Song et al., in prep). The latter , older but closer than the known young associations, allow our survey to probe in to the 5 AU ice line. Combined with GPI’s high contrast, this target sample will allow imaging of a population of Jovian continuous with the known Summary indirectly-detected planets (see figure 2), for the first time providing us with a full view of planetary properties across the entire range of separations found in the planets of our own . The international Exoplanet Survey (GPIES) Campaign Data System Goals !"!"# $%&'(%)**&+,&')+-'./0123&'04'5-6)+7&-'8)9)'5+)*:323'40/';2,%'(0+9/)39' collaboration will soon conduct a comprehensive survey of 600 nearby The scientific success of the young stars to detect Jovian planets and debris disks around nearby GPIES campaign will depend stars, the largest systematic high contrast imaging survey to date. To crucially on the production of a achieve these goals will require a sophisticated data handling system. large, uniformly processed, statistically significant sample of detected exoplanets, including The GPIES Campaign Data System will build on the publicly-available GPI accurate spectra, photometry, data pipeline to deliver an integrated environment for automated and , and images of Figure 3: Speckle suppression enables us to achieve the highest possible contrast, as demonstrated with a Above:sequence Simulated GPI single image, 1 hr reduced ADI sequence, and an extraced planetary spectrum. of 60 1-minute raw IFS frames produced by our GPI AO data simulator. Left: a spectral data cube associated circumstellar debris. showing the speckle pattern that persists within the square “dark hole” created by GPI’s AO and interactive data reduction, analysis, and archiving of high contrast ; center: a PSF-suppressed final image in which three planets of 3-7 MJ mass are revealed (Sky rotation sets the FOV); and right: an extracted spectrum for the outer planet (The grey line is the input images. The resulting reduced datasets and derived measurements of We need a unified system to organize and atmosphere model).iterative reductions to refine properties of ensure consistent data processing at Achieving all these detected ambitious goals planets. will require Furthermore great care in understanding it must the be reduction, planets and disks will be gathered into a high-level science archive that calibration, and analysis of GPI data. Even with the sophisticated wavefront control and stages of data analysis, from basic detector coronagraphic starlightpossible suppression, to there re-reduce remains a halo the of residual entire speckles campaign around each target will be used to guide the campaign's execution and produce uniform that must be modeled and subtracted in software to achieve the desired sensitivity. This PSF halo calibration to spectral or polarimetric must be suppresseddata by set at least consistently an order of magnitude, as the without data introducing pipeline any spurious statistical analyses. This archive will become public after the survey is datacube generation, to PSF subtraction systematics that biasalgorithms improve. And we must do all this derived measurements. Detection of planets requires distinguishing faint point sources from a complex and time-variable speckle pattern. Dynamical studies need completed. This system uses modern, open-source software, Virtual using LOCI or other algorithms. milliarcsecond New astrometricwhile enabling the efficient interactions and precision to constrain orbits in a reasonable amount of time. Tests of Observatory standards, and the Canadian Advanced Network for observations must be processed rapidly in a planet thermal historyaccess to data of a widespread, distributed and atmospheric properties demand superb spectrophotometric accuracy. fully automated manner, while it must also The GPI team hascollaboration. been at the forefront of this field, inventing the SSDI and ADI observing Astronomical Research (CANFAR) . modes (Marois et. al 2000, 2006) and the LOCI data reduction algorithm that has become the be possible to perform interactive modern and standard used by most high contrast programs today (Lafreniere et al. 2007), along with corresponding advances in polarimetric data analyses for disk imaging (Perrin et al. 2004, 2008, Hinkley et al. 2009). Our team continues to pursue innovations in these algorithms to provide improved astrometric and spectrophotometric measurements with reduced systematic biases (Marois et al. 2010, Soummer et al. 2011) while taking optimal advantage of the increased

4 System Architecture Raw Multi-Stage Data Reduction CADC New Data Data Data Our campaign data system is has a modular Archive Integrity The initial detection of a candidate planet by Monitor Checker architecture, incorporating the base GPI the GPI data system is the start of a long Data Reduction Pipeline algorithms (Maire Fake process, not its end! The PSF subtraction Companion et al. 2010, 2012) together with tools for Injector algorithms used to enhance contrast are Some jobs automatically retrieving and reducing new run locally known to bias photometric and astrometric observations, detecting and characterizing $6/21('*9: @(-(189, measurements. Our team has led the way in Data <,4,)(-*&4 Reduced companion candidates, assessing sens- Data Data developing innovative new approaches to Reduction Reduction itivities and biases using fake companions Job Job Master Some jobs run obtain improved measurements with reduced Generator on CANFAR cloud injected into real data, managing instrument systematic biases (Marois et al. 2010, Soummer !"#$ %&'()*+,-). calibrations. See diagram at right. "-5,) /0,1-)('2 et al. 2011, while taking optimal advantage of %/62789-): ,3-)(1-*&4 Manual the increased information content in integral ;*<5=0),1*7*&4 requests for A&?,'2B--*4< Furthermore, the outputs from this process (7-)&+,-).2>2 field datacubes (Pueyo et al. Observatory & GPI Observatory re-reduction must be made available to all team members Calibrations #(4?*?(-, 05&-&+,-). 2012). The GPI Data System is designed to Database @,-,1-*&4 Results in real time. All data stores (shown as Database enable multiple reduction codes to be cylinders at right, including file storage and Web Page Campaign Status Console deployed on the same dataset, tracking and SQL databases) are hosted in the VOspace Campaign comparing the outputs of each for maximum cloud storage, and a web-based front end Scheduler science return and continual improvement. will provide status and data displays Master server controller complementary to interactive tools.

Archiving for High Contrast

High Contrast in the Cloud: During the campaign, the GPIES archives must store all vital information about the campaign’s current state and results, organizing it so that team members can trace provenance and history VOSpace and CANFAR for any data product, assess needs for followup or repeat observations, and craft scientific conclusions drawing upon the various results. Data products must feed back into higher-level Ensuring that all members of the GPI team decision making software to best use upcoming telescope time to maximize the science return, have immediate access to new data and up- for instance by deriving on-the-fly updates to exoplanet statistical models that guide target to-date archives is critical to successful prioritization. collaboration. To achieve this, we have adopted the CADC’s virtual observatory (VO) After the GPIES survey’s completion, its fully calibrated reduced data and detailed source space distributed storage system (Kavelaars catalogs will be released to the community as high-level science products. Our campaign data et al. 2012). VOspace offers the following center will transition into serving as a public archive. The data that will be stored include: advantages: Above: The GPI team VOspace accessed via web page and vofs file system. Raw and reduced images Candidates found and planets confirmed - Easy access: Accessible via a web interface, Python VOspace is part of CANFAR, the Canadian - - Multiple reductions with different (astrometry/photometry/spectroscopy/ tools, or mounted as a network drive. Advanced Network for Astronomy Research. - - Huge capacity: many Tb of shared cloud storage, algorithms, versions, or parameter settings variability) and followed (many epochs for accessible to the entire team. - Large cloud compute grid provides >500 CPUs - Observation log including and orbits) - Rapid data sharing: The GPI instrument computer available as virtual machines for large scale database other quality metadata - Circumstellar disks images, and their uploads new data to the VO space just after an reduction/analysis image has been acquired. Reduced images can - Provides tremendous flexibility for varying scale - Measured contrast curves measured properties likewise be near-instantly shared among the whole computational load, with low/no cost overhead. team. Great tool for rapid analyses with people - Ongoing development to improve & enhance services. distributed in many cities. Robust infrastructure: Secure access controls, VOspace and CANFAR: like Dropbox and Amazon EC2 References: - Kavelaars, J. et al. ADASS XXI, ASP Conf Ser. Marois et al 2010 Proc. SPIE. 7736 automatic offsite backup, local mirroring for faster optimized for astronomical research! Maire et al. 2010 Proc SPIE 7735 Pueyo et al. 2012 ApJS 199 access to frequently used files, & more. Maire et al. 2012 Proc SPIE, this meeting. Soummer et al. 2011 ApJ