Solar Adaptive Optics Research Articles

SolarAccel: FPGA accelerated 2D cross-correlation of digital images: Application to solar adaptive optics

SolarAccel: FPGA accelerated 2D cross-correlation of digital images: Application to solar adaptive optics

Reference optical turbulence characteristics at the Large Solar Vacuum Telescope site

Abstract Large ground-based solar telescopes are equipped with adaptive optics systems to correct wavefront distortions induced in the turbulent atmosphere. The design of the adaptive optics system strongly depends on the vertical profiles of the optical turbulence. In particular, the characteristics of the optical turbulence determine the design of tomographic adaptive optics systems, which provide image correction within a wide field of view. In the article, a new method to estimate reference optical turbulence characteristics from Era-5 reanalysis assimilated data is presented. This method is based on the dependence of the air refractive index structure constant $C_n^2$ on the vertical shears of wind speed as well as the outer scale of turbulence L0. The L0 parameter is estimated by minimization of the dispersion between the modeled and measured values of the refractive index structure constant $C_n^2$ within the surface layer. For the first time, parametrization coefficients and reference profiles of optical turbulence averaged for the period 1940–2022 are calculated for the Large Solar Vacuum Telescope (LSVT) site. The calculated optical turbulence profiles are representative; these profiles correspond to typical changes of the measured values of the Fried parameter, the isoplanatic angle, and the outer scale of turbulence at the LSVT site. The model turbulence profiles are verified taking into account the Shack–Hartmann wavefront sensor measurements at the LSVT. The higher accuracy of estimation of the optical turbulence characteristics makes it possible to refine parameters relevant to the LSVT adaptive optics system. The obtained results can be used in order to develop high-resolution solar adaptive optics technologies as applied to ground-based telescopes including those using the principles of atmospheric tomography.

Extended scene deep-phase-retrieval Shack-Hartmann wavefront sensors

Context. Strong atmospheric turbulence has been a challenge for high-resolution imaging of solar telescopes. Adaptive optics (AO) systems are capable of improving the quality of imaging by correcting partial aberrations. Thus, the performance of Shack-Hartmann sensors in measuring aberrations generally determines the upper performance bound of AO systems. In solar AO, classic correlation Shack-Hartmann sensors only correct a small number of modal aberrations. Moreover, strong aberrations are difficult to measure stably by correlation Shack-Hartmann. In this context, the improvement in the performance of Shark-Hartmann sensors promises to enable higher-resolution imaging of extended objects for ground-based telescopes or Earth observation. Aims. We propose a new extended scene deep-phase-retrieval Shack-Hartmann wavefront sensing approach to improve the image quality of solar telescopes. It is capable of achieving high-accuracy measurements of high-spatial-resolution wavefronts on extended scene wavefront sensing. Moreover, it has great generalization when observing unknown objects from different fields of view of the telescope. Methods. Our proposed approach can extract features resembling the sub-aperture point spread function (PSF) from a Shack-Hartmann sensor image without any prior information. Then a convolutional neural network is used to establish a nonlinear mapping between the feature image and the wavefront modal coefficients. The extracted feature greatly eliminates the shape information of the extended object while maintaining more information related to aberrations. We verified the performance of the proposed method through simulations and experiments. Results. In the indoor experiment on the ground layer adaptive optics (GLAO) of the 1 m New Vacuum Solar Telescope, compared to the Shack-Hartmann correlation method, the proposed method reduces the correction errors by more than one third. When observing objects from different fields of view in the GLAO that differ from the object in the training data, the relative errors fluctuate within the range of 20% to 26%. The AO system with the proposed wavefront measurement method can obtain higher-resolution focal images of the simulated solar granulation after a round of offline correction. The average latency of the proposed method is about 0.6 ms.

Solar adaptive optics systems for the New Vacuum Solar Telescope at the Fuxian Lake Solar Observatory

Solar adaptive optics systems for the New Vacuum Solar Telescope at the Fuxian Lake Solar Observatory

Accuracy Analysis for Shack–Hartmann Wave Front Sensing with Extended Sources

Correlating the Shack–Hartmann wave front sensor (SH-WFS) with extended targets is widely used in solar adaptive optics systems. This paper aims to introduce a theoretical analysis that evaluates the accuracy of the SH-WFS on extended sources, with a specific focus on the implementation of the Normalized Cross-correlation (NCC) algorithm. To obtain an accurate error description, we utilized the calculation formula of the NCC algorithm to directly express the coordinates of the maximum value in the correlation function matrix. Furthermore, we determined the variance of the centroid position through the error transfer function, which quantifies the measurement error. In comparison with the previous findings of Michau et al., our result exhibits a coefficient disparity, specifically obtaining results 1.5 times higher than their work. The extensive solar granulation simulation and experimental results validate the theoretical error formulas. These error formulas can effectively estimate the accuracy of the SH-WFS, providing a theoretical foundation for the design of optical systems.

Enhanced-resolution Shack-Hartmann wavefront sensing for extended objects.

Adaptive optics systems for large-aperture solar telescopes, especially multiconjugate adaptive optics systems, suffer from a fundamental trade-off between wavefront sampling rate and sub-aperture resolution. We introduce an enhanced-resolution Shack-Hartmann wavefront sensing method that decouples sub-aperture resolution from the desired wavefront sampling rate. We experimentally verified the validity of this method. Results show that by synthesizing multiple low-spatial samplings, this method is capable to sense higher-frequency aberrations beyond any low-spatial sampling involved in the synthesis, and it allows higher sub-aperture resolution and higher operating bandwidths, which can better fulfill the needs of solar adaptive optics.

Resolving 3D Coronal Loop Physics with Spectropolarimetry and Off-limb Solar Adaptive Optics

Resolving 3D Coronal Loop Physics with Spectropolarimetry and Off-limb Solar Adaptive Optics

Performance Estimation of Solar Imagery Using Different Types of Atmospheric Turbulence Models

This paper proposed several approaches for estimating the optical turbulence of the Earth’s atmosphere and their effect on solar images’ resolution using ground-based telescopes based on von Kárman, Kolmogorov, and modified von Kárman PSDs models. The results showed a strong correlation coefficient for the modified von Kármán model of atmospheric representation. As can be seen in the case where solar adaptive optics have been properly designed, they typically decrease aberration considerably and provide greatly improved imagery.

Cascaded Temporal and Spatial Attention Network for solar adaptive optics image restoration

Context. Atmospheric turbulence severely degrades the quality of images observed through a ground-based telescope. An adaptive optics (AO) system only partially improves the image quality by correcting certain level wavefronts, making post-facto image processing necessary. Several deep learning-based methods have recently been applied in solar AO image post-processing. However, further research is still needed to get better images while enhancing model robustness and using inter-frame and intra-frame information. Aims. We propose an end-to-end network that can better handle solar adaptive image anisoplanatism by leveraging attention mechanisms, pixel-wise filters, and cascaded architecture. Methods. We developed a cascaded attention-based deep neural network named Cascaded Temporal and Spatial Attention Network (CTSAN) for solar AO image restoration. CTSAN consists of four modules: optical flow estimation PWC-Net for inter-frame explicit alignment, temporal and spatial attention for dynamic feature fusion, temporal sharpness prior for sharp feature extraction, and encoder-decoder architecture for feature reconstruction. We also used a hard example mining strategy to create a loss function in order to focus on the regions that are difficult to restore, and a cascaded architecture to further improve model stability. Results. CTSAN and the other two state-of-the-art (SOTA) supervised learning methods for solar AO image restoration are trained on real 705 nm photospheric and 656 nm chromospheric AO images supervised by corresponding Speckle images. Then all the methods are quantitatively and qualitatively evaluated on five real testing sets. Compared to the other two SOTA methods, CTSAN can restore clearer solar images, and shows better stability and generalization performance when restoring the lowest contrast AO image.

Image Quality Specification for Solar Telescopes

Modern large ground-based solar telescopes are invariably equipped with adaptive optics systems to enhance the high angular resolution imaging and spectroscopic capabilities in the presence of the Earth's atmospheric turbulence. The quality of the images obtained from these telescopes can not be quantified with the Strehl ratio or other metrics that are used for nighttime astronomical telescopes directly. In this paper, we propose to use the root mean square (rms) granulation contrast as a metric to quantify the image quality of ground-based solar telescopes. We obtain semi-logarithmic plots indicating the correspondence between the Strehl ratio and the rms granulation contrast for most practical values of the telescope diameters (D) and the atmospheric coherence diameters ($ r_0$), for various levels of adaptive optics compensation. We estimate the efficiency of a few working solar adaptive optics systems by comparing the results of our simulations with the Strehl ratio and rms granulation contrast published by these systems. Our results can be used in conjunction with a plausible 50 system efficiency to predict the lower bound on the rms granulation contrast expected from ground-based solar telescopes.

Region-correlation algorithm with improved dynamic range and reconstruction accuracy for extended object wavefront sensing.

The correlation Shack-Hartmann wavefront sensor (SHWFS) is widely used in many fields in addition to solar adaptive optics. The requirement for the SHWFS dynamic range increases with the diameter of the telescope, which means a larger detector array is needed. However, the size of the detector would be restricted by the high frame rate needed for the solar observation. To solve this problem, a new, to the best of our knowledge, method called the region-correlation algorithm (RCA) is proposed. In this method, the sub-image array is divided into several regions, and the slopes of sub-apertures are calculated by referring to a selected sub-image in each region. Note that the final slope over a sub-aperture is obtained by combining the relative slopes between the selected sub-image in different regions. The dynamic range in each region is similar to the conventional correlation algorithm, and the final dynamic range of the RCA would be accumulated from those of the regions. The reconstruction accuracy under large aberration would also be improved due to the extended dynamic range. Meanwhile, the RCA does not require any extra device and the increase in calculation time resulting from the RCA is acceptable. The results of numerical simulation and experiment, compared with conventional correlation algorithm, confirm the advantages in the performance of the RCA as well.

Comparative study of Shack-Hartmann configurations for atmospheric turbulence reconstructions in solar adaptive optics

The correction of the phase variations induced by the atmospheric turbulence is one of the most complex problems that an Adaptive Optics (AO) System must deal with, as it must calculate the properties of all the atmosphere traversed by the light from several measures taken by ground-based telescopes. Traditional reconstructors systems used in AO are based on computational algorithms where its reconstruction quality improves with the number of measures made by the telescopes’ sensors. That means that sensors are getting greater and greater with their corresponding higher financial expense. Artificial Intelligence (IA) has become in recent years a real alternative to traditional computational methods as reconstructors for AO systems. Fully-convolutional neural networks (FCNs) specifically have shown great performances working in Solar AO, demonstrating their ability to obtain a lot of valuable information from the recorded images for the wavefront phase evaluation. Along this research, the influence of the properties of the telescope's sensors and of the observations in the reconstructions made by the FCNs’ is measured, to obtain the configuration that best suits the performance of artificial neural networks (ANN). The presented results determine the way forward for the future sensors for telescopes with reconstruction systems based on ANNs, to obtain higher quality reconstructions employing fewer economic resources.

Fully Convolutional Approaches for Numerical Approximation of Turbulent Phases in Solar Adaptive Optics

Information on the correlations from solar Shack–Hartmann wavefront sensors is usually used for reconstruction algorithms. However, modern applications of artificial neural networks as adaptive optics reconstruction algorithms allow the use of the full image as an input to the system intended for estimating a correction, avoiding approximations and a loss of information, and obtaining numerical values of those correlations. Although studied for night-time adaptive optics, the solar scenario implies more complexity due to the resolution of the solar images potentially taken. Fully convolutional neural networks were the technique chosen in this research to address this problem. In this work, wavefront phase recovery for adaptive optics correction is addressed, comparing networks that use images from the sensor or images from the correlations as inputs. As a result, this research shows improvements in performance for phase recovery with the image-to-phase approach. For recovering the turbulence of high-altitude layers, up to 93% similarity is reached.

Vibration mitigation experiment on the Chinese Large Solar Telescope based on the linear quadratic Gaussian control

Vibrations caused by thermal control system severely affect the Chinese Large Solar Telescope (CLST). The linear quadratic Gaussian (LQG) controller has been tested on the solar adaptive optics (SAO) system equipped in the CLST to mitigate these vibrations. An automatic identification method based on the particle swarm optimization (PSO) algorithm is used to compute the parameters of the LQG controller and verified on the CLST. Experimental results are presented in this paper, which show a significant reduction in vibrations in both X and Y axes, illustrating the superior performance of the LQG controller compared to the conventional proportional-integral (PI) controller.

Dome C coherence time statistics from DIMM data

ABSTRACT We present a reanalysis of several years of DIMM data at the site of Dome C, Antarctica, to provide measurements of the coherence time τ0. Statistics and seasonal behaviour of τ0 are given at two heights above the ground, 3 and 8 m, for the wavelength λ = 500 nm. We found an annual median value of 2.9 ms at the height of 8 m. A few measurements could also be obtained at the height of 20 m and give a median value of 6 ms during the period June–September. For the first time, we provide measurements of τ0 in daytime during the summer, which appears to show the same time dependence as the seeing with a sharp maximum at 5 pm local time. Exceptional values of τ0 above 10 ms are met at this particular moment. The continuous slow variations of turbulence conditions during the day offers a natural test bed for a solar adaptive optics system.

A low-cost and duplicable portable solar adaptive optics system based on LabVIEW hybrid programming

Abstract We have developed a portable solar adaptive optics (PSAO) for diffraction-limited imaging based on today’s multi-core personal computer. Our PSAO software is written in LabVIEW code, which features block-diagram function based programming and can dramatically speed up the software development. The PSAO can achieve a ~1000 Hz open-loop correction speed with a Shack–Hartmann Wave-front Sensor (SH-WFS) in 11 × 11 sub-aperture configuration. The image shift measurements for solar wave-front sensing are the most time-consuming computations in a solar adaptive optics (AO) system. Since our current LabVIEW program does not fully support multi-core techniques for the image shift measurements, it cannot fully take advantage of the multi-core computer’s power for parallel computation. In order to accelerate the AO system’s running speed, a dedicated message passing interface/open multi-processing parallel programming technique is developed for our LabVIEW-based AO program, which fully supports multi-core parallel computation in LabVIEW programming. Our experiments demonstrate that the hybrid parallel technique can significantly improve the running speed of the solar AO system, and this work paves the way for the applications of a low-cost and duplicable PSAO system for large solar telescopes.

Solar GLAO and TAO: performance comparisons

Multi-conjugate adaptive optics (MCAO), consisting of several deformable mirrors (DMs), can significantly increase the adaptive optics (AO) correction field of view. Current MCAO can be realized by either star-oriented or layer-oriented approaches. For solar AO, ground-layer adaptive optics (GLAO) can be viewed as an extreme case of layer-oriented MCAO in which the DM is conjugated to the ground, while solar tomography adaptive optics (TAO) that we proposed recently can be viewed as star-oriented MCAO with only one DM. Solar GLAO and TAO use the same hardware as conventional solar AO, and therefore it will be important to see which method can deliver better performance. In this article, we compare the performance of solar GLAO and TAO by using end-to-end numerical simulation software. Numerical simulations of TAO and GLAO with different numbers of guide stars are conducted. Our results show that TAO and GLAO produce the same performance if the DM is conjugated to the ground, but TAO can only generate better performance when the DM is conjugated to the best height. This result has important application in existing one-DM solar AO systems.

Generating artificial reference images for open loop correlation wavefront sensors

Shack-Hartmann wavefront sensors for both solar and laser guide star adaptive optics (with elongated spots) need to observe extended objects. Correlation techniques have been successfully employed to measure the wavefront gradient in solar adaptive optics systems and have been proposed for laser guide star systems. In this paper we describe a method for synthesising reference images for correlation Shack-Hartmann wavefront sensors with a larger field of view than individual sub-apertures. We then show how these supersized reference images can increase the performance of correlation wavefront sensors in regimes where large relative shifts are induced between sub-apertures, such as those observed in open-loop wavefront sensors. The technique we describe requires no external knowledge outside of the wavefront-sensor images, making it available as an entirely "software" upgrade to an existing adaptive optics system. For solar adaptive optics we show the supersized reference images extend the magnitude of shifts which can be accurately measured from 12% to 50% of the field of view of a sub-aperture and in laser guide star wavefront sensors the magnitude of centroids that can be accurately measured is increased from 12% to 25% of the total field of view of the sub-aperture.

Artificial guide stars for adaptive optics using unmanned aerial vehicles

Astronomical adaptive optics systems are used to increase effective telescope resolution. However, they cannot be used to observe the whole sky since one or more natural guide stars of sufficient brightness must be found within the telescope field of view for the AO system to work. Even when laser guide stars are used, natural guide stars are still required to provide a constant position reference. Here, we introduce a technique to overcome this problem by using rotary unmanned aerial vehicles (UAVs) as a platform from which to produce artificial guide stars. We describe the concept, which relies on the UAV being able to measure its precise relative position. We investigate the adaptive optics performance improvements that can be achieved, which in the cases presented here can improve the Strehl ratio by a factor of at least 2 for a 8~m class telescope. We also discuss improvements to this technique, which is relevant to both astronomical and solar adaptive optics systems.

Adaptive optics-corrected solar image quality assessment based on image power spectrum and human visual system

In ground-based large aperture solar telescopes, the speckle image reconstruction technique combined with adaptive optics (AO) correction is generally used to get near diffraction-limited solar images. In order to select the high quality images from the AO-corrected high resolution solar image sequence, an automated no-reference image quality assessment (IQA) is needed. According to the noise characteristics of solar AO images, an IQA metric based on an image power spectrum and human visual system is developed. By the incorporation of noise masking and shifting the spatial frequency range of summation, our IQA metric could select sharper images with less noise than previous works based on the image power spectrum, even if there is image scaling. Compared with existing general-purpose IQA metrics and previous metrics specifically designed for solar IQA, experimental results verify that the proposed metric gains better performance whether on robustness to blur and noise, or on selecting high quality frames containing solar granulations, sunspots, or both of them. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)