Speckle statistics in adaptive optics images at visible wavelengths

Abstract

Abstract. Residual speckles in adaptive optics (AO) images represent a well-known limitation on the achievement of the contrast needed for faint source detection. Speckles in AO imagery can be the result of either residual atmospheric aberrations, not corrected by the AO, or slowly evolving aberrations induced by the optical system. We take advantage of the high temporal cadence (1 ms) of the data acquired by the System for Coronagraphy with High-order Adaptive Optics from R to K bands-VIS forerunner experiment at the Large Binocular Telescope to characterize the AO residual speckles at visible wavelengths. An accurate knowledge of the speckle pattern and its dynamics is of paramount importance for the application of methods aimed at their mitigation. By means of both an automatic identification software and information theory, we study the main statistical properties of AO residuals and their dynamics. We therefore provide a speckle characterization that can be incorporated into numerical simulations to increase their realism and to optimize the performances of both real-time and postprocessing techniques aimed at the reduction of the speckle noise.

SFADI: The Speckle-free Angular Differential Imaging Method

“…In fact, the frame by frame PSF evolution is due to the rapid variation of the adaptive optics residuals, which appears as a pattern of speckles changing shape with a timescale of less than 10 ms, at visible wavelengths, as shown in Stangalini et al (2017a). Hence, if the frame rate is fast enough, i.e.…”

Recurrence quantification analysis as a post-processing technique in adaptive optics high contrast imaging

“…The fast exposure time adopted for the observations allows us to freeze the evolution of atmospheric speckles and to easily recover the residual jitter in the focal plane. For additional details we refer the reader to Pedichini et al (2017) and Stangalini et al (2017). We remark that the forerunner experiment is a pathfinder of the SHARK-VIS high-contrast imager, now approved and at the time of writing in its construction phase.…”

“…Here we see that the best results are obtained for m = 1, independently of τ. However, in order to be on the safe side, in agreement with Stangalini et al (2017), we have chosen τ = 10 ms, which is the de-correlation time scale of the speckles.…”

“…This is possible whenever the exposure time of the im- marco.stangalini@inaf.it ages is shorter than the typical lifetime of the speckles themselves, so that they can be identified and efficiently masked out. The same authors successfully tested this technique on the high cadence (1 ms) data acquired at visible wavelengths by the SHARK-VIS forerunner experiment at LBT (Stangalini et al 2014;Pedichini et al 2017;Stangalini et al 2017), demonstrating an increase in contrast of the order of a factor of two over the ADI method. This also demonstrates the advantage of high frame rates that freeze the atmospheric evolution, thus allowing the identification and removal of short-lived speckles from the data.…”

Stochastic Speckle Discrimination with Time-tagged Photon Lists: Digging below the Speckle Noise Floor

“…In the limit of Kolmogorov atmospheric turbulence and frozen flow, the speckle decorrelation time can be thought of as the wind crossing time across the telescope pupil (Macintosh et al 2005). In reality, the turbulence is not Kolmogorovic and the AO loop and telescope vibrations can further complicate the speckle temporal power spectrum density (PSD) (Stangalini et al 2016). This may result in a faster speckle decorrelation time, or a temporal PSD described by multiple exponential timescales.…”