Mining the GPIES database

The Gemini Planet Imager Exoplanet Survey (GPIES) is a direct imaging campaign designed to search for new, young, self-luminous, giant exoplanet. To date, GPIES has observed nearly 500 targets, and generated over 30,000 individual exposures using its integral field spectrograph (IFS) instrument. The GPIES team has developed a campaign data system that includes a database incorporating all of the metadata collected along with all individual raw data products, including environmental conditions and instrument performance metrics. In addition to the raw data, the same database also indexes metadata associated with multiple levels of reduced data products, including contrast measures for individual images and combined image sequences, which serve as the primary metric of performance for the final science products. Finally, the database is used to track telemetry products from the GPI adaptive optics (AO) subsystem, and associate these with corresponding IFS data. Here, we discuss several data exploration and visualization projects enabled by the GPIES database. Of particular interest are any correlations between instrument performance (final contrast) and environmental or operating conditions. We show single and multiple-parameter fits of single-image and observing sequence contrast as functions of various seeing measures, and discuss automated outlier rejection and other fitting concerns. We also explore unsupervised learning techniques, and self-organizing maps, in particular, in order to produce lowdimensional mappings of the full metadata space, in order to provide new insights on how instrument performance may correlate with various factors. Supervised learning techniques are then employed in order to partition the space of raw (single image) to final (full sequence) contrast in order to better predict the value of the final data set from the first few completed observations. Finally, we discuss the particular features of the database design that aid in performing these analyses, and suggest potential future upgrades and refinements.

[1]  Vanessa P. Bailey,et al.  Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey , 2018, 1801.01902.

[2]  B. Macintosh,et al.  Angular Differential Imaging: A Powerful High-Contrast Imaging Technique , 2005, astro-ph/0512335.

[3]  Dmitry Savransky,et al.  Gemini Planet Imager observational calibrations XI: pipeline improvements and enhanced calibrations after two years on sky , 2016, Astronomical Telescopes + Instrumentation.

[4]  J. Bovy,et al.  Data analysis recipes: Fitting a model to data , 2010, 1008.4686.

[5]  Dmitry Savransky,et al.  Status and performance of the Gemini Planet Imager adaptive optics system , 2016, Astronomical Telescopes + Instrumentation.

[6]  Andrew W. Serio,et al.  First light of the Gemini Planet Imager , 2014, Proceedings of the National Academy of Sciences.

[7]  Laurent Pueyo,et al.  pyKLIP: PSF Subtraction for Exoplanets and Disks , 2015 .

[8]  Jason J. Wang,et al.  Discovery and spectroscopy of the young jovian planet 51 Eri b with the Gemini Planet Imager , 2015, Science.

[9]  Dmitry Savransky,et al.  Gemini Planet Imager Calibrations, Pipeline Updates, and Campaign Data Processing , 2017 .

[10]  B. Macintosh,et al.  Spatially filtered wave-front sensor for high-order adaptive optics. , 2004, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  Dmitry Savransky,et al.  Performance of the Gemini Planet Imager's adaptive optics system. , 2016, Applied optics.

[12]  Fredrik T. Rantakyrö,et al.  Gemini Planet Imager observational calibrations I: Overview of the GPI data reduction pipeline , 2014, Astronomical Telescopes and Instrumentation.

[13]  Daniel Foreman-Mackey,et al.  Data Analysis Recipes: Using Markov Chain Monte Carlo , 2017, 1710.06068.

[14]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[15]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[16]  Gordon A. H. Walker,et al.  Speckle Noise and the Detection of Faint Companions , 1999 .

[17]  Wes McKinney,et al.  Data Structures for Statistical Computing in Python , 2010, SciPy.

[18]  Daniel Foreman-Mackey,et al.  emcee: The MCMC Hammer , 2012, 1202.3665.

[19]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[20]  R. Soummer,et al.  DETECTION AND CHARACTERIZATION OF EXOPLANETS AND DISKS USING PROJECTIONS ON KARHUNEN–LOÈVE EIGENIMAGES , 2012, 1207.4197.

[21]  Bruce Macintosh,et al.  Experimental Design for the Gemini Planet Imager , 2011, 1103.6085.

[22]  Vanessa P. Bailey,et al.  Air, telescope, and instrument temperature effects on the Gemini Planet Imager’s image quality , 2018, Astronomical Telescopes + Instrumentation.

[23]  Dmitry Savransky,et al.  Campaign Scheduling and Analysis for the Gemini Planet Imager , 2013, Proceedings of the International Astronomical Union.

[24]  Dmitry Savransky,et al.  Evidence That the Directly Imaged Planet HD 131399 Ab Is a Background Star , 2017, 1705.06851.

[25]  Laurent Pueyo,et al.  DETECTION AND CHARACTERIZATION OF EXOPLANETS USING PROJECTIONS ON KARHUNEN–LOEVE EIGENIMAGES: FORWARD MODELING , 2016, 1604.06097.

[26]  Vanessa P. Bailey,et al.  Improving and Assessing Planet Sensitivity of the GPI Exoplanet Survey with a Forward Model Matched Filter , 2017, 1705.05477.

[27]  J. R. Graham,et al.  TLOCI: A Fully Loaded Speckle Killing Machine , 2013 .

[28]  E. W. Greisen,et al.  Representations of spectral coordinates in FITS , 2005 .