Abstract
Coronagraphic imaging of exoplanets and circumstellar environments using ground-based instruments on large telescopes is intrinsically limited by speckles induced by uncorrected aberrations. These aberrations originate from the imperfect correction of the atmosphere by an extreme adaptive optics system; from static optical defects; or from small opto-mechanical variations due to changes in temperature, pressure, or gravity vector. More than the speckles themselves, the performance of high-contrast imagers is ultimately limited by their temporal stability, since most post-processing techniques rely on difference of images acquired at different points in time. Identifying the origin of the aberrations and the timescales involved is therefore crucial to understanding the fundamental limits of dedicated high-contrast instruments. In previous works we demonstrated the use of a Zernike wavefront sensor called ZELDA for sensing non-common path aberrations (NCPA) in the VLT/SPHERE instrument. We now use ZELDA to investigate the stability of the instrumental aberrations using five long sequences of measurements obtained at high cadence on the internal calibration source. Our study reveals two regimes of decorrelation of the NCPA. The first, with a characteristic timescale of a few seconds and an amplitude of a few nanometers, is induced by a fast internal turbulence within the enclosure. The second is a slow quasi-linear decorrelation on the order of a few 10−3 nmrms s−1 that acts on timescales from minutes to hours. We use coronagraphic image reconstruction to demonstrate that these two NCPA contributions have a measurable impact on differences of images, and that the fast internal turbulence is a dominating term over to the slow linear decorrelation. We also use dedicated sequences where the derotator and atmospheric dispersion compensators emulate a real observation to demonstrate the importance of performing observations symmetric around the meridian, which minimizes speckle decorrelation, and therefore maximizes the sensitivity to point sources in difference of images.
Highlights
High-contrast imagers on large ground-based telescopes, such as SPHERE on the VLT (Beuzit et al 2019), GPI on Gemini South (Macintosh et al 2014), and SCExAO on Subaru (Jovanovic et al 2015), are designed to suppress stellar light and reveal the faint signal of nearby planetary companions (e.g., Macintosh et al 2015; Chauvin et al 2017; Keppler et al 2018) or circumstellar disks (e.g., Boccaletti et al 2015; Kalas et al 2015; Garufi et al 2017)
In ground-based systems these errors come from the imperfect correction of the atmosphere by the extreme adaptive optics (ExAO) system (Macintosh et al 2005; Hinkley et al 2007), which typically have variation timescales of a few seconds; from effects that are not detectable by the wavefront sensor (WFS), such as the low-wind effect (Sauvage et al 2015; Milli et al 2018); from effects that are introduced by the ExAO system itself, such as the wind-driven halo (Cantalloube et al 2018, 2020); and from temporal variations inside the instrument itself, which create aberrations that are not all seen by the wavefront sensor of the ExAO system and are not corrected (Sauvage et al 2007)
We recall that a Zernike sensor for Extremely Low-level Differential Aberration (ZELDA) measurement requires three types of data to produce optical path difference (OPD) maps calibrated in nanometers: data obtained with the mask inserted into the beam, clear pupil data obtained without the mask, and instrumental background data
Summary
High-contrast imagers on large ground-based telescopes, such as SPHERE on the VLT (Beuzit et al 2019), GPI on Gemini South (Macintosh et al 2014), and SCExAO on Subaru (Jovanovic et al 2015), are designed to suppress stellar light and reveal the faint signal of nearby planetary companions (e.g., Macintosh et al 2015; Chauvin et al 2017; Keppler et al 2018) or circumstellar disks (e.g., Boccaletti et al 2015; Kalas et al 2015; Garufi et al 2017) These instruments combine extreme adaptive optics (ExAO) systems (e.g., Guyon 2005; Fusco et al 2006) with efficient coronagraphs (e.g., Soummer 2005; Guyon et al 2005) and advanced post-processing methods (Racine et al 1999; Marois et al 2006; Lafrenière et al 2007; Cantalloube et al 2015; Ruffio et al 2017).
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