Point Spread Function Quality Research Articles

On-sky validation of image-based adaptive optics wavefront sensor referencing

Context. Differentiating between a true exoplanet signal and residual speckle noise is a key challenge in high-contrast imaging (HCI). Speckles result from a combination of fast, slow, and static wavefront aberrations introduced by atmospheric turbulence and instrument optics. While wavefront control techniques developed over the last decade have shown promise in minimizing fast atmospheric residuals, slow and static aberrations such as non-common path aberrations (NCPAs) remain a key limiting factor for exoplanet detection. NCPAs are not seen by the wavefront sensor (WFS) of the adaptive optics (AO) loop, hence the difficulty in correcting them. Aims. We propose to improve the identification and rejection of slow and static speckles in AO-corrected images. The algorithm known as the Direct Reinforcement Wavefront Heuristic Optimisation (DrWHO) performs a frequent compensation operation on static and quasi-static aberrations (including NCPAs) to boost image contrast. It is applicable to general-purpose AO systems as well as HCI systems. Methods. By changing the WFS reference at every iteration of the algorithm (a few tens of seconds), DrWHO changes the AO system point of convergence to lead it towards a compensation mechanism for the static and slow aberrations. References are calculated using an iterative lucky-imaging approach, where each iteration updates the WFS reference, ultimately favoring high-quality focal plane images. Results. We validated this concept through both numerical simulations and on-sky testing on the SCExAO instrument at the 8.2-m Subaru telescope. Simulations show a rapid convergence towards the correction of 82% of the NCPAs. On-sky tests were performed over a 10 min run in the visible (750 nm). We introduced a flux concentration (FC) metric to quantify the point spread function (PSF) quality and measure a 15.7% improvement compared to the pre-DrWHO image. Conclusions. The DrWHO algorithm is a robust focal-plane wavefront sensing calibration method that has been successfully demonstrated on-sky. It does not rely on a model and does not require wavefront sensor calibration or linearity. It is compatible with different wavefront control methods, and can be further optimized for speed and efficiency. The algorithm is ready to be incorporated in scientific observations, enabling better PSF quality and stability during observations.

Statistical Detection of IGM Structures during Cosmic Reionization Using Absorption of the Redshifted 21 cm line by H i against Compact Background Radio Sources

Abstract Detecting neutral hydrogen structures in the intergalactic medium (IGM) during cosmic reionization via absorption (21 cm forest) against a background radiation is considered independent and complementary to the three-dimensional tomography and power spectrum techniques. The direct detection of this absorption requires very bright (≳10–100 mJy) background sources at high redshifts (z ≳ 8), which are evidently rare; very long times of integration; or instruments of very high sensitivity. This motivates a statistical one-dimensional (1D) power spectrum approach along narrow sightlines but with fainter background objects (∼1–10 mJy), which are likely to be more abundant and significant contributors at high redshifts. The 1D power spectrum reduces cosmic variance and improves sensitivity especially on small spatial scales. Using standard radiative transfer and fiducial models for the instrument, the background sources, and the evolution of IGM structures during cosmic reionization, the potential of the 1D power spectrum along selected narrow directions is investigated against uncertainties from thermal noise and the chromatic synthesized point spread function (PSF) response. Minimum requirements on the number of high-redshift background sources, the telescope sensitivity, and the PSF quality are estimated for a range of instrumental, background source, and reionization model parameters. The 1D power spectrum is intrinsically stronger at higher redshifts. A ∼1000 hr observing campaign targeting ∼100 narrow sightlines to radio-faint, high-redshift background objects with modern radio telescopes, especially the Square Kilometre Array, can detect the 1D power spectrum on a range of spatial scales and redshifts, and potentially discriminate between models of cosmic reionization.

On-sky verification of Fast and Furious focal-plane wavefront sensing: Moving forward toward controlling the island effect at Subaru/SCExAO

Context. High-contrast imaging (HCI) observations of exoplanets can be limited by the island effect (IE). The IE occurs when the main wavefront sensor (WFS) cannot measure sharp phase discontinuities across the telescope’s secondary mirror support structures (also known as spiders). On the current generation of telescopes, the IE becomes a severe problem when the ground wind speed is below a few meters per second. During these conditions, the air that is in close contact with the spiders cools down and is not blown away. This can create a sharp optical path length difference between light passing on opposite sides of the spiders. Such an IE aberration is not measured by the WFS and is therefore left uncorrected. This is referred to as the low-wind effect (LWE). The LWE severely distorts the point spread function (PSF), significantly lowering the Strehl ratio and degrading the contrast. Aims. In this article, we aim to show that the focal-plane wavefront sensing (FPWFS) algorithm, Fast and Furious (F&F), can be used to measure and correct the IE/LWE. The F&F algorithm is a sequential phase diversity algorithm and a software-only solution to FPWFS that only requires access to images of non-coronagraphic PSFs and control of the deformable mirror. Methods. We deployed the algorithm on the SCExAO HCI instrument at the Subaru Telescope using the internal near-infrared camera in H-band. We tested with the internal source to verify that F&F can correct a wide variety of LWE phase screens. Subsequently, F&F was deployed on-sky to test its performance with the full end-to-end system and atmospheric turbulence. The performance of the algorithm was evaluated by two metrics based on the PSF quality: (1) the Strehl ratio approximation (SRA), and (2) variance of the normalized first Airy ring (VAR). The VAR measures the distortion of the first Airy ring, and is used to quantify PSF improvements that do not or barely affect the PSF core (e.g., during challenging atmospheric conditions). Results. The internal source results show that F&F can correct a wide range of LWE phase screens. Random LWE phase screens with a peak-to-valley wavefront error between 0.4 μm and 2 μm were all corrected to a SRA > 90% and an VAR ⪅ 0.05. Furthermore, the on-sky results show that F&F is able to improve the PSF quality during very challenging atmospheric conditions (1.3–1.4″seeing at 500 nm). Closed-loop tests show that F&F is able to improve the VAR from 0.27–0.03 and therefore significantly improve the symmetry of the PSF. Simultaneous observations of the PSF in the optical (λ = 750 nm, Δλ = 50 nm) show that during these tests we were correcting aberrations common to the optical and NIR paths within SCExAO. We could not conclusively determine if we were correcting the LWE and/or (quasi-)static aberrations upstream of SCExAO. Conclusions. The F&F algorithm is a promising focal-plane wavefront sensing technique that has now been successfully tested on-sky. Going forward, the algorithm is suitable for incorporation into observing modes, which will enable PSFs of higher quality and stability during science observations.

Closed-Loop Restoration Approach to Blurry Images Based on Machine Learning and Feedback Optimization.

Blind image deconvolution (BID) aims to remove or reduce the degradations that have occurred during the acquisition or processing. It is a challenging ill-posed problem due to a lack of enough information in degraded image for unambiguous recovery of both point spread function (PSF) and clear image. Although recently many powerful algorithms appeared; however, it is still an active research area due to the diversity of degraded images as well as degradations. Closed-loop control systems are characterized with their powerful ability to stabilize the behavior response and overcome external disturbances by designing an effective feedback optimization. In this paper, we employed feedback control to enhance the stability of BID by driving the current estimation quality of PSF to the desired level without manually selecting restoration parameters and using an effective combination of machine learning with feedback optimization. The foremost challenge when designing a feedback structure is to construct or choose a suitable performance metric as a controlled index and a feedback information. Our proposed quality metric is based on the blur assessment of deconvolved patches to identify the best PSF and computing its relative quality. The Kalman filter-based extremum seeking approach is employed to find the optimum value of controlled variable. To find better restoration parameters, learning algorithms, such as multilayer perceptron and bagged decision trees, are used to estimate the generic PSF support size instead of trial and error methods. The problem is modeled as a combination of pattern classification and regression using multiple training features, including noise metrics, blur metrics, and low-level statistics. Multi-objective genetic algorithm is used to find key patches from multiple saliency maps which enhance performance and save extra computation by avoiding ineffectual regions of the image. The proposed scheme is shown to outperform corresponding open-loop schemes, which often fails or needs many assumptions regarding images and thus resulting in sub-optimal results.

基于图像复原的高光谱图像前向像移补偿

To improve the image resolution of a hyper spectral image,an image motion compensation algorithm was proposed. The mechanisms of image degradation caused by forward image motion were analyzed and a digital Image Motion Compensation( IMC) was adopted to restore the blurred image. Then,the Point Spread Function( PSF) and noise power were estimated,and the improved algorithm of Wiener filtering was selected to restore the blurred image. By taking the average absolute error and the peak signal to noise ratio as the evaluation criteria,the proved experiments were performed. Experimental results under the known quality of PSF and noise power show that the absolute average error of the image for improved Wiener filtering algorithm decreases by 9. 31% and the peak signal to noise ratio increases by 13. 98% as compared to the traditional Wiener filter. It is concluded that the quality of the hyper spectral image can be improved effectively by the proposed algorithm.