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Proceedings Paper

Exoplanet imaging with LOCI processing: photometry and astrometry with the new SOSIE pipeline
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Paper Abstract

The Angular, Simultaneous Spectral and Reference Star Differential Imaging techniques (ADI, SSDI and RSDI) are currently the main observing approaches that are being used to pursue large-scale direct exoplanet imaging surveys and will be a key component of next-generation high-contrast imaging instrument science. To allow detection of faint planets, images from these observing techniques are combined in a way to retain the planet flux while subtracting as much as possible the residual speckle noise. The LOCI algorithm is a very efficient way of combining a set of reference images to subtract the noise of a given image. Although high contrast performances have been achieved with ADI/SSDI/RSDI & LOCI, achieving high accuracy photometry and astrometry can be a challenge, due to various biases coming mainly from the inevitable partial point source self-subtraction for ADI/SSDI and how LOCI is designed to suppress the noise. We present here several biases that we hare uncovered while analyzing data on the HR8799 planetary system and how we have modified our analysis pipeline to calibrate or remove these effects so that high accuracy astrometry and photometry is achievable. In addition, several new upgrades are presented in a new archive-based (i.e. performing ADI, SSDI and RSDI with LOCI as a single PSF subtraction step) multi-instrument reduction and analysis pipeline called SOSIE.

Paper Details

Date Published: 27 July 2010
PDF: 12 pages
Proc. SPIE 7736, Adaptive Optics Systems II, 77361J (27 July 2010); doi: 10.1117/12.857225
Show Author Affiliations
Christian Marois, NRC Herzberg Institute of Astrophysics (Canada)
Bruce Macintosh, Lawrence Livermore National Lab. (United States)
Jean-Pierre Véran, NRC Herzberg Institute of Astrophysics (Canada)

Published in SPIE Proceedings Vol. 7736:
Adaptive Optics Systems II
Brent L. Ellerbroek; Michael Hart; Norbert Hubin; Peter L. Wizinowich, Editor(s)

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