A method for evaluating the correction quality of surface adaptive optics systems with large field of view

Abstract

This invention discloses a method for evaluating the correction quality of a large-view-rate surface adaptive optics system. The method includes: proposing a correction quality evaluation factor and evaluation criteria for the performance of the surface adaptive optics system based on this factor. The evaluation process involves: determining contribution weight factors based on scientific needs; evaluating the correction quality of the large-view-rate surface adaptive optics system around system parameters using the correction quality evaluation factor; identifying the optimal system configuration corresponding to the smallest correction quality evaluation factor, with its optimization parameter tolerance range not exceeding 1.01 times numerically; and prioritizing system configurations with simpler structures when system performance is similar (correction quality evaluation factor numerical difference less than 1%). This invention can flexibly and effectively guide the design and optimization of large-view-rate surface adaptive optics systems, thereby providing more reliable images for solar magnetic field research.

Description

Technical Field

[0001] This invention relates to the field of surface adaptive optics technology, and in particular to a method for evaluating the correction quality of a surface adaptive optics system with a large field of view. Background Technology

[0002] Solar physics research and space weather forecasting require observing the magnetic characteristics of active regions within a few arcminutes. Obtaining uniform compensated images over a large field of view is crucial for analyzing and predicting magnetic field morphology (Molodij G, Aulanier G. Large Field-of-View Spectropolarimetric Observations with a Large Aperture Telescope[J]. Solar Physics, 2012, 276(1):451-477). In particular, accurate magnetic field topology studies require compensation for ground-level turbulence to achieve good spatial resolution and uniform imaging over a large field of view. Adaptive Optics (AO) has become an indispensable technology for achieving high-resolution imaging with large-aperture ground-based telescopes. Classical Adaptive Optics (CAO) can improve atmospheric seeing and achieve diffraction-limited observations. The non-isohalic nature of atmospheric turbulence limits the correction efficiency of CAO systems, resulting in a small imaging field of view, typically a few arcseconds (Close L MA review of astronomical science with visible light adaptive optics[J]. Adaptive Optics Systems V, 2016, 9909: 483-500). However, scientifically significant feature areas on the solar surface are usually much larger. Ground-layer adaptive optics (GLAO) has been proposed to overcome this bottleneck. Since most turbulence is concentrated near the surface, GLAO uses a single deformable mirror to compensate for distortions caused by surface turbulence, thus providing a relatively uniform image quality improvement over a large field of view (Rigaut F. Ground conjugate wide field adaptive optics for the ELTs[C] / / EuropeanSouthern Observatory Conference and Workshop Proceedings. 2002, 58: 11). This relatively uniform large field of view correction broadens the application areas of AO modes.

[0003] There is a trade-off between the size of the effectively compensated field of view and the thickness of the atmosphere. The size of the compensated field of view is limited because wavefronts in different directions experience different turbulence conditions. A larger compensated field of view corresponds to a relatively thinner turbulent layer. If the field of view is too large, the sampled wavefront from the guide star (GS) will be compensated more accurately, leading to inhomogeneity in the overall field-of-view compensation. This inhomogeneity can result in the loss of important information and complicate post-processing techniques (Zhong L, Zhang L, Shi Z, et al. Wide field-of-view, high-resolution Solar observation in combination with ground layer adaptive optics and speckle imaging[J]. Astronomy & Astrophysics, 2020, 637: A99). Different GS sampled wavefronts will alter the isoplanar regions of the atmosphere (Roddier F. Adaptive Optics in Astronomy: Historical context. 1999). Therefore, the GSS layout can affect the uniformity of the entire field point spread function (PSF) and is often mentioned in GLAO performance analysis.

[0004] The shape of the PSF within a wide field of view is an important criterion for evaluating the performance of a GLAO system. Tokovinin et al. proposed a linear estimation model based on spatial frequency filtering theory to obtain the corrected residual PSF (Tokovinin A. Seeing Improvement with Ground-Layer Adaptive Optics[J]. Publications of the Astronomical Society of the Pacific, 2004, 116(824):941). Compared with the Monte Carlo model, this model has a smaller computational cost and can more flexibly explore the influence of different parameters and atmospheric conditions on system performance (Andersen D R, Stoesz J, Morris S, et al. Performance Modeling of a Wide-Field Ground-Layer Adaptive Optics System[J]. Publications of the Astronomical Society of the Pacific, 2006, 118(849):1574). The performance gain of GLAO is usually measured using Full Width at Half-Maximum (FWHM) and Encircled Energy (EE) (Tokovinin A. Performance and error budget of a GLAO system[C] / / Adaptive optics systems.SPIE,2008,7015:599-608; Flicker R. NGAO trade study report: GLAO for Non-NGAO Instruments KAON 472(WBS 3.1.2.1.7)[J]. 2007). However, the above indicators ignore the scientific requirement of uniform imaging over a large field of view, and therefore cannot fully characterize the performance of the GLAO system. He Bin et al. proposed a residual wavefront estimation error factor to optimize the position of the laser guide star. This evaluation method is more suitable for the CAO model because it focuses on optimal compensation rather than uniformity (He B, HuL F, Li DY, et al. A high precision phase reconstruction algorithm for multi-laser guide stars adaptive optics[J]. Chinese Physics B, 2016, 25(9): 094214).Jia Peng et al. proposed an evaluation factor related to FOV, which weights the full field-of-view correction effect based on the area of ​​the field-of-view ring (Jia P, Basden A, Osborn J. Ground-layer adaptive-optics system modelling for the Chinese large optical / infrared telescope[J]. Monthly Notices of the Royal Astronomical Society, 2018, 479(1):829-843). This evaluation method improves the uniformity of correction within a large field of view by paying more attention to the correction outside the field of view. However, its uniformity can only be analyzed by artificially weighing the difference between the best and worst gains. Therefore, there is currently a lack of a complete evaluation method to describe the imaging compensation capability of the GLAO system. Summary of the Invention

[0005] The technical problem solved by this invention is:

[0006] This invention addresses the aforementioned problem of evaluating the correction quality of surface adaptive optics systems by proposing a method for evaluating the correction quality of surface adaptive optics systems with a large field of view, thereby providing a more comprehensive evaluation of the performance of surface adaptive optics systems.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A method for evaluating the correction quality of a large field-of-view surface adaptive optics system includes a correction evaluation factor (CEF) and an evaluation criterion for the performance of the GLAO system based on the evaluation factor.

[0009] The method includes:

[0010] Step 1: Based on the imaging resolution and uniformity requirements within the large field of view, assign the required weights to the correction quality evaluation factors;

[0011] Step 2: Evaluate the performance of the GLAO system around the system parameters using the corrected quality evaluation factors;

[0012] Step 3: The minimum value of the correction quality evaluation factor corresponding to the optimal system configuration, and the tolerance range of its optimization parameters shall not exceed 1.01 times in value;

[0013] Step 4: When the calibration quality is similar (the difference in calibration quality evaluation factor values ​​is less than 1%), the system configuration with the simplest structure should be given priority.

[0014] The definition principle of the calibration quality evaluation factor mentioned in step two is as follows:

[0015] a) Image sharpness factor E is obtained based on residual FWHM within a large field of view:

[0016]

[0017] Where f(x,y) represents the FWHM value at the field of view position (x,y), S is the field of view area; E describes the average compensation effect of image quality within a large field of view, and the smaller the value, the better the average correction effect within a large field of view.

[0018] b) Obtain the PSF spatial stability factor, V, based on the residual FWHM within the large field of view:

[0019]

[0020] V describes the spatial fluctuation characteristics of PSF at different locations within a large field of view. The smaller the value, the higher the uniformity of the correction quality within the large field of view. According to Kolmogorov's turbulence theory, atmospheric turbulence is homogeneous and isotropic under certain conditions. Assuming the target field of view is within an isohalo region, the correction quality should also satisfy uniformity in all directions at the same distance from the center of the field of view, i.e., V≈0. However, in general, scientific requirements necessitate a field of view larger than this isohalo region, and the turbulent environment above the observation site cannot meet this condition. Therefore, by limiting the value of V, higher imaging consistency can be obtained.

[0021] c) Obtain the calibration quality evaluation factors:

[0022] CEF=α·E+β·V(α,β≥0) (3)

[0023] Among them, α and β depend on the scientific requirements between image sharpness and its spatial stability;

[0024] Beneficial effects:

[0025] This invention proposes a large field-of-view GLAO correction quality evaluation method based on scientific needs. This method can effectively and flexibly evaluate the performance of GLAO systems; the smaller the value, the higher the quality of the compensated image that the GLAO system can acquire within a large field of view, and vice versa. Using this invention, the performance of GLAO systems can be flexibly evaluated according to different observation requirements, thereby better guiding the design and optimization of large field-of-view GLAO systems. This enables GLAO systems to provide uniform high-resolution images within a large field of view, thus providing more reliable images for solar magnetic field research, especially providing unprecedented high-resolution materials for the study of solar active regions and solar eruption phenomena. Attached Figure Description

[0026] Figure 1 This is a flowchart of the method for evaluating the correction quality of a large field-view surface adaptive optics system according to the present invention;

[0027] Figure 2 This is a layout diagram of the GLAO system guide star in an embodiment of the present invention;

[0028] Figure 3 This is a GLAO full-field residual FWHM distribution map in an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0030] Figure 1 The flowchart below illustrates the method for evaluating the correction quality of the large field-of-view surface adaptive optics system described in this invention. In this embodiment, the telescope aperture is set to 0.98m; the imaging field of view is φ60arcsec; the sampling wavelength is 0.55um; the imaging wavelength is 0.705um; the number of guiding stars is 9; the deformable mirror actuators are arranged in a 13×13 configuration; and the atmospheric turbulence model is a 7-layer turbulence model fitted from measured data at the Fuxian Lake solar observatory in Yunnan Province, with parameters for each layer shown in Table 1.

[0031] Table 1 Turbulence Model at the Fuxian Lake Solar Observation Site in Yunnan

[0032] Altitude / km 0 1.47 2.94 5.00 7.36 8.83 10.11 Weight / % 43 16 7 9 7 7 11

[0033] Step 1: Obtain the PSF at different locations within the field of view of the GLAO system. Based on spatial spectrum filtering theory, obtain the system's optical transfer function T(k):

[0034]

[0035] Where k is the image spatial frequency, and T0(k) is the diffraction-limited optical transfer function. The residual average structure function (SF) for the pupil surface:

[0036]

[0037] Where i represents the turbulent layer number, λ is the imaging wavelength, and D ∈,i (r) represents the SF of the i-th layer, N is the total number of turbulent layers, and J i Let be the turbulence intensity of the i-th layer. The SF values ​​for each layer are:

[0038]

[0039] Where L0 is the atmospheric turbulence external dimension, f is the pupil spatial frequency, f is the modulus of f, r is the pupil offset, L0 is the atmospheric turbulence external scale, and |G(f)| 2 i The uncorrected portion of the i-th layer is related to the guide star layout, DM driver arrangement, and turbulence height h:

[0040]

[0041] Where K is the number of guiding stars, and the position of the k-th guiding star in the field of view is a. k The position of the k'th guiding star in the field of view is a. k′ 'a' represents the target's field of view position, and A(f) represents the Airy function. Considering the uniform and symmetrical imaging requirements of GLAO, the guide star layout should be symmetrically distributed. The guide star position is set as follows: Figure 2 There are 12 types of multi-ring structures in the examples; M(f) is a low-pass filter related to the spatial resolution of DM, and its specific form is:

[0042] M(f)=1,f≤1 / 2d (8)

[0043] Where d is the driver pitch.

[0044] Using equations (4)-(8), the PSF at different positions in the large field of view can be obtained; further, its FWHM can be calculated.

[0045] Step 2: Based on the full field-of-view FWHM data under 12 guiding star configurations, E is obtained using equation (1);

[0046] Step 3: Based on the full field-of-view FWHM data under 12 guiding star configurations, obtain V using equation (2);

[0047] Step 4: Considering both image sharpness and spatial stability, i.e., α = β = 1; Based on E and V data under 12 guide star configurations, use equation (3) to obtain CEF;

[0048] Step 5: Compare and analyze the CEF values ​​under 12 guide star layouts, and evaluate the optimal guide star layout under the current GLAO system configuration: each guide star is uniformly distributed in a single ring within a 52.8 arcsec field of view.

[0049] To better illustrate the effectiveness of CEF, we evaluated the above 12 guide star layouts using the E factor and field-of-view correlation factor (Jia P, Basden A, Osborn J. Ground-layer adaptive-optics system modelling for the Chinese large optical / infrared telescope[J]. Monthly Notices of the Royal Astronomical Society, 2018, 479(1):829-843). The optimal layouts obtained were: one in the center, with the rest evenly distributed in a single ring within a 43.8 arcsec field of view; and a single ring evenly distributed in a 43.8 arcsec field of view.

[0050] The residual FWHM distribution of GLAO across the entire field of view under the three layouts is as follows: Figure 3 As shown, the E and V values ​​are 0.2189 arcsec and 0.013 arcsec, 0.2084 arcsec and 0.0354 arcsec, and 0.2106 arcsec and 0.0286 arcsec, respectively. Compared to factor E, which is more suitable for CAO evaluation, the optimal guiding star layout obtained by evaluating CEF and field-of-view correlation factors significantly improves the spatial imaging uniformity of the GLAO system, increasing it by 63% and 19%, respectively, while sacrificing only 5% and 1% in average sharpness. Compared to the field-of-view correlation factor, CEF sacrifices only 4% of average sharpness while bringing a 44% gain in uniformity. Therefore, this invention can accurately and flexibly evaluate the performance of the GLAO system according to scientific needs.

[0051] The above are specific embodiments disclosed in this invention. Parts not described in detail belong to well-known technologies in the art. However, the scope of protection of this invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in this invention should be included within the scope of protection of this invention.

Claims

1. A method for evaluating the correction quality of a surface adaptive optics system with a large field of view, characterized in that, The method includes a correction quality evaluation factor and an evaluation criterion for the performance of surface adaptive optics systems based on this evaluation factor. The evaluation method includes the following steps: Step 1: Based on the imaging resolution and uniformity requirements within the large field of view, assign the required weights to the correction quality evaluation factors; Step 2: Evaluate the performance of the surface adaptive optics system by using calibration quality evaluation factors around the system parameters; Step 3: The minimum value of the correction quality evaluation factor corresponding to the optimal system configuration, and the tolerance range of its optimization parameters shall not exceed 1.01 times in value; Step 4: When the system performance is similar, i.e., the difference in the value of the corrected quality evaluation factor is less than 1%, the system configuration with the simplest structure should be selected first. The definition principle of the correction quality evaluation factor is as follows: a) Obtain the image sharpness factor, E, based on the full width at half maximum (FWHM) of the residual point spread function within a large field of view. （1） in, The field of view position is indicated as Half-width at half-height (FWHM), denoted as the field of view area; E describes the average compensation effect on image quality within a large field of view. The smaller the value, the better the average correction effect within a large field of view. b) Obtain the spatial stability factor V of the point spread function based on the full width at half maximum (FWHM) of the residual point spread function within a large field of view: （2） V describes the spatial fluctuation characteristics of the point spread function at different locations within a large field of view. The smaller the value, the higher the uniformity of the correction quality within the large field of view. According to Kolmogorov's turbulence theory, under certain conditions, atmospheric turbulence is homogeneous and isotropic. Assuming the target field of view is within an isohalo region, the correction quality should also satisfy consistency in all directions at the same distance from the center of the field of view, i.e., V≈0. However, in general, scientific requirements dictate that the field of view is larger than this isohalo region, and the turbulent environment above the observation site cannot meet this condition. Therefore, by limiting the value of V, higher imaging consistency can be obtained. c) Obtain the calibration quality evaluation factors: （3） in, and It depends on the scientific requirements between image sharpness and its spatial stability.

2. The method for evaluating the correction quality of a surface adaptive optics system with a large field of view according to claim 1, characterized in that, The parameters of the evaluation point diffusion function used in the definition of the correction quality evaluation index include, but are not limited to, the full width at half maximum (FWHM).

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