Coherent Differential Imaging of high-contrast extended sources with VLT/SPHERE

TL;DR

This paper tackles the persistent challenge of residual starlight and non-common path aberrations in high-contrast imaging by applying coherent differential imaging (CDI) with temporal speckle modulation on VLT/SPHERE. The authors demonstrate a complete CDI workflow, from single-sequence probing with small deformable mirror offsets to multi-sequence combination using batch, KL coherent component projection, and KL image projection, culminating in a high-pass filtered recovery of circumstellar disks. Compared with conventional ADI, CDI significantly reduces self-subtraction and better preserves extended disk structures, as shown in four disk targets with quantitative improvements in recovered disk flux relative to ADI (e.g., up to ~216× in HD 163296). The results indicate CDI as an efficient, non-redundant approach to calibrate static/quasi-static aberrations in future direct-imaging surveys, with potential benefits for space-based coronagraphy, while outlining current limitations related to bandwidth, nonlinearity, and field of view. Overall, CDI offers a promising route to robust high-contrast imaging of extended sources, enabling more faithful disk morphologies and flux measurements in challenging regimes.

Abstract

High-contrast imaging relies on advanced coronagraphs and adaptive optics (AO) to attenuate the starlight. However, residual aberrations, especially non-common path aberrations between the AO channel and the coronagraph channel, limit the instrument performance. While post-processing techniques such as spectral or angular differential imaging (ADI) can partially address those issues, they suffer from self-subtraction and inefficiencies at small angular separations or when observations are conducted far from transit. We previously demonstrated the on-sky performance of coherent differential imaging (CDI), which offers a promising alternative. It allows for isolating coherent starlight residuals through speckle modulation, which can then be subtracted from the raw images during post-processing. This work aims to validate a CDI method on real science targets, demonstrating its effectiveness in imaging almost face-on circumstellar disks, which are typically challenging to retrieve with ADI. We temporally modulated the speckle field in VLT/SPHERE images, applying small phase offsets on the AO deformable mirror while observing stars surrounded by circumstellar material: HR 4796A, CPD-36 6759, HD 169142, and HD 163296. We hence separated the astrophysical scene from the stellar speckle field, whose lights are mutually incoherent. Combining a dozen of data frames and reference coronagraph point spread functions through a Karhunen-Loève image projection framework, we recover the circumstellar disks without the artifacts that are usually introduced by common post-processing algorithms (e.g., self-subtraction). The CDI method therefore represents a promising strategy for calibrating the effect of static and quasi-static aberrations in future direct imaging surveys. Indeed, it is efficient, does not require frequent telescope slewing, and does not introduce image artifacts to first order.

Figures (9)

• Figure 1: Image decomposition of HD 163296 through CDI. Left: Coronagraph image with no probe (i.e., total intensity image). Center: PWP-estimated speckle field. Right: CDI result (estimated astrophysical scene subtracted from the total intensity image) after one sequence of PWP. The negative values, indicated by black-colored regions, show local over-subtraction in areas where PWP cannot properly estimate the speckle field.
• Figure 2: $I_{CDI}$ results for different reference estimations. Top left: $I_{ref,i}=0$, no CDI post-processing applied (equivalent to noADI). Top right: $I_{ref,i} = I_{PWP,i}$, a simple batch process used. Bottom left: Karhunen-Loève coherent component projection. Bottom right: Karhunen-Loève image projection. All images have been high-passed filtered, as explained in Sec. \ref{['subsec:highpass_filter']}.
• Figure 3: Illustration of the $M_{PWP}$ eigenvector decomposition. Top row: Principal component 1, 2, and 3. Bottom row: Principal component 5, 10, and 15.
• Figure 4: Comparison of disk images obtained with ADI (left column) and CDI (center column). For each disk, the ADI and CDI images are displayed using the same color scale; however, the scales vary from one disk to another. ADI and CDI results were obtained through the same dataset. We use K-klip ADI Soummer2012 with $K_{klip} = N_{filt}/2$. Right column: Comparison of disk intensities, for pixels inside the region encircled in green, obtained with both ADI and CDI as a function of the position angle.
• Figure 5: Effect of the high-pass filter on the off-axis source throughput for various Gaussian kernel standard deviations.