Paper
27 July 2010 Exoplanet imaging with LOCI processing: photometry and astrometry with the new SOSIE pipeline
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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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christian Marois, Bruce Macintosh, and Jean-Pierre Véran "Exoplanet imaging with LOCI processing: photometry and astrometry with the new SOSIE pipeline", Proc. SPIE 7736, Adaptive Optics Systems II, 77361J (27 July 2010); https://doi.org/10.1117/12.857225
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Cited by 77 scholarly publications.
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KEYWORDS
Point spread functions

Planets

Photometry

Signal to noise ratio

Exoplanets

Stars

Image processing

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