Coronagraph deformable mirror actuator dead pixel interference analysis method and system

Abstract

The invention discloses a coronagraph deformable mirror actuator dead pixel interference analysis method and system. The method comprises the following steps: constructing a coronagraph high-contrast imaging system framework; measuring the obtained defective pixel position information of the deformable mirror actuator, and fixing the defective pixel position information in an actuator control matrix; establishing a phase modulation control system based on an SPGD algorithm; establishing a convolution model with interferences among the actuators; the dead pixels are introduced into a dark space optimization process, and when dark space optimization is carried out through an SPGD algorithm, a system monitors key indexes of each quadrant dark space and observes the influence degree of the dead pixels on the dark space optimization process. Amplitude modulation is achieved through the focal plane modulation optical filter, the contrast ratio of the image plane reaches 10 <-6 > magnitude, phase modulation, namely dark space optimization, is achieved through the thousand-unit deformable mirror, and the contrast ratio reaches 10 <-8 > magnitude through the SPGD modulation algorithm; the dead pixel influence is introduced into the dark space optimization process, and the coupling influence of the dead pixel on the adjacent actuator is analyzed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of astronomical optics, and particularly relates to a bad point interference analysis and compensation method for a deformable mirror actuator of a coronagraph based on a double modulation principle. BACKGROUND

[0002] Direct imaging detection of exoplanets is an important frontier of modern astronomy research, and the core challenge thereof lies in the need to separate a dark and weak planet signal from an extremely bright star halo. A coronagraph, as a key instrument for achieving this goal, forms a high-contrast dark region at the focal plane of an optical system to suppress starlight, thereby revealing planets around the star. As a core element of an adaptive optical system of the coronagraph, a deformable mirror (DM) achieves nanometer-level precision of wavefront phase modulation through the collaborative work of an array actuator, and is a key technical guarantee for achieving 10^-6 to 10^-8 ultra-high contrast imaging.

[0003] However, in a long-term operation process, part of the actuators of the deformable mirror will fail due to mechanical fatigue, aging of electronic elements or other reasons, forming "bad points" that cannot normally respond to control signals. The existence of these bad points will cause the loss of local wavefront correction capability, which will be propagated to the entire optical system through the spatial coupling effect between the actuators, and ultimately affect the contrast performance of the dark region optimization result. The existing technology lacks a systematic bad point interference analysis method, cannot accurately evaluate the specific influence mechanism of the bad points on the dark region optimization process, and also lacks an effective compensation strategy. Therefore, establishing a scientific and systematic actuator bad point interference analysis method has important theoretical significance and practical value for evaluating the fault tolerance capability of the system, optimizing the control algorithm and guiding the engineering design. SUMMARY

[0004] In view of the problem that the existing technology lacks a systematic bad point interference analysis method, the application provides a bad point interference analysis and compensation method and system for a deformable mirror actuator of a coronagraph based on a stochastic parallel gradient descent (SPGD) algorithm for phase modulation.

[0005] The technical solution adopted by the application is as follows:

[0006] The bad point interference analysis method for a deformable mirror actuator of a coronagraph provided by the application comprises the following steps:

[0007] Step 1: constructing a high-contrast imaging system framework of the coronagraph, modulating an amplitude distribution using a modulation filter, and optimizing a dark region through phase distribution modulation of the deformable mirror;

[0008] Step 2: obtaining actual distribution positions of bad points of the deformable mirror actuator through actual measurement, and fixing the measured bad point position information in an actuator control matrix;

[0009] Step 3: Establish a phase modulation control system based on the SPGD (Stochastic Parallel Gradient Descent) algorithm, and achieve dark area contrast gain (about 100 times) through iterative optimization; the performance function of the SPGD algorithm uses dark area light intensity as the optimization target, and the algorithm target is to minimize the dark area light intensity;

[0010] Step 4: Establish a convolution model of inter-actuator coupled interference, simulate actuator motion process, bad pixel actuator response, and the coupling effect of bad pixels on adjacent normal actuators;

[0011] Step 5: Introduce bad pixels into the dark area optimization process, and when optimizing the dark area through the SPGD algorithm, the system monitors key indicators including contrast changes, optimization convergence performance, and evaluation function decay trends in each quadrant dark area, observes the influence of bad pixels on the dark area optimization process, and compensates for the bad pixel interference of the deformable mirror actuator of the coronagraph.

[0012] Further, the method further comprises:

[0013] Establish a quantitative evaluation system for bad pixel interference, including:

[0014] First, by comparing the optimization results of each quadrant with and without bad pixels, establish the relationship between bad pixel distribution and dark area performance degradation, and quantitatively calculate the contrast loss rate;

[0015] Second, based on simulation analysis of different bad pixel distribution patterns, establish a correlation model between the number of bad pixels and the degree of system performance degradation;

[0016] Finally, by analyzing the dynamic decay process of the evaluation function, quantify the convergence speed change and final optimization performance difference caused by bad pixels; the influence of actuator bad pixels is diffused to the adjacent area through mechanical coupling effect between actuators, and the influence degree of different quadrant dark areas by bad pixel position exists;

[0017] Establish a comprehensive evaluation index system for bad pixel influence, including:

[0018] Evaluation function descent rate: by analyzing the change trend of the evaluation function in the optimization process, quantifying the system performance degradation speed caused by bad pixels;

[0019] Optimization convergence performance: evaluate the convergence speed and final performance difference of the SPGD algorithm with and without bad pixels;

[0020] System fault tolerance: establish a correlation model between the number of bad pixels and the degree of system performance degradation.

[0021] Further, in step 1, the specific construction of the coronagraph high-contrast imaging system comprises:

[0022] Filter modulation: Modulate the amplitude by precisely designed transmittance distribution, form initial dark zone on focal plane, contrast is 10^-6 order;

[0023] Deformable mirror modulation: Modulate the wavefront phase by continuously adjusting the deformable mirror surface shape through actuator array.

[0024] Further, the step 2, the specific implementation of the bad point position fixed includes:

[0025] Bad point position record: Establish a bad point distribution matrix to accurately record the spatial coordinate position of each bad point;

[0026] Control matrix fixed: Fix the wavefront phase value of the corresponding position of the bad point as the initial value.

[0027] Further, in the step 4, the modeling method of the bad point influence in the actuator control matrix includes:

[0028] Normal actuator response modeling: Establish the voltage-displacement response function of the normal working actuator;

[0029] Bad point actuator response characteristics: Model the bad point response function to characterize its inability to respond normally;

[0030] Control matrix correction: Integrate the actual measured bad point information into the system control matrix to form a control model containing bad point constraints;

[0031] Influence mechanism quantification: Quantitatively evaluate the influence degree of bad points on dark zone performance by comparing the system response difference with and without bad points.

[0032] The specific implementation of the convolution simulation method includes:

[0033] Actuator influence function modeling: Establish the influence function of a single actuator rising movement on the surrounding area;

[0034] Bad point through mechanical coupling restricts the numerical distribution of influence function on the modulation ability of adjacent actuators;

[0035] Actuator influence analysis:

[0036] .

[0037] Further, the method further includes a three-level analysis step of bad point influence:

[0038] First level influence: Analysis of the modulation characteristics of normal actuators, and establishment of the voltage-displacement response function under ideal conditions;

[0039] Second level influence: Analysis of spatial coupling effect, study the restriction of bad point on the modulation efficiency of adjacent actuators through mechanical coupling;

[0040] Third level of influence: final impact assessment of bad pixel actuators, quantifying the local phase error caused by the absence of wavefront correction capability at the bad pixel location.

[0041] The application also provides a coronagraph deformable mirror actuator bad pixel interference analysis system for realizing the method, comprising:

[0042] Coronagraph high-contrast imaging module: including a filter amplitude modulation unit and a deformable mirror phase modulation unit, realizing cooperative light field modulation;

[0043] SPGD control algorithm module: realizing phase optimization based on random parallel gradient descent, containing bad pixel constraint processing function;

[0044] Bad pixel detection and modeling module: establishing a bad pixel distribution model based on actual measurement data, realizing manual identification and fixing of bad pixels;

[0045] Convolution simulation calculation module: simulating the spatial coupling effect between actuators, analyzing the bad pixel influence mechanism;

[0046] Performance evaluation and analysis module: realizing quantitative evaluation of bad pixel influence and system performance analysis.

[0047] Further, the convolution simulation calculation module adopts a parallel computing architecture to simulate the spatial coupling effect of a large-scale actuator array in real time, supporting full-process simulation analysis from normal actuator motion influence to bad pixel coupling effect.

[0048] Further, the system can handle dark area optimization with a 100-fold gain from the 10^-6 order to the 10^-8 order, and still achieve high-contrast imaging performance in the presence of actual bad pixels.

[0049] The application further provides a computer readable storage medium having a computer program stored thereon, the program being executed by a processor to realize the coronagraph deformable mirror actuator bad pixel interference analysis method.

[0050] Compared with the prior art, the application has the following significant advantages:

[0051] 1. A systematic coronagraph high-contrast imaging simulation system is constructed, and the simulation of the dark area optimization (phase modulation) process is realized, providing a platform foundation for actuator bad pixel analysis.

[0052] 2. The implementation mechanism of the SPGD algorithm under the constraint condition of actual bad pixels is first systematically described, the robustness and convergence of the algorithm under non-ideal conditions are analyzed through the combination of bad pixel parameter fixing and adaptive optimization, thereby effectively dealing with the performance degradation caused by bad pixels.

[0053] 3. The method has good versatility and scalability, suitable for different sizes of actuator arrays and various bad pixel distribution cases. Experimental results show that even in the presence of actual bad pixels, the system can maintain a contrast level of 10^-8 order of magnitude, meeting the practical needs of exoplanet observation. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 The overall flowchart of the method of the present application shows the complete technical route from the construction of the dual modulation system to the analysis of bad pixel interference.

[0055] Figure 2 The optical schematic diagram of the high-contrast imaging system is shown, and the key optical elements such as filters and deformable mirrors and the optical path are labeled.

[0056] Figure 3 The deformable mirror actuator matrix is shown, with white areas representing actuator positions and gray squares representing damaged actuators.

[0057] Figure 4 The wavefront phase data used in the simulation is shown.

[0058] Figure 5 The optimized PSF image of the actual bad pixel is shown.

[0059] Figure 6 The contrast of each optimization result when the actuator is damaged is shown, with different colored curves corresponding to different optimization areas; (a) is the single quadrant optimization result, and (b) is the double quadrant optimization result.

[0060] Figure 7 The evaluation function change when the bad pixel is only in the center is shown. DETAILED DESCRIPTION

[0061] The present application will be further described in detail below with reference to the accompanying drawings.

[0062] The technical solutions of the present application will be further described in detail below with reference to specific embodiments. It should be understood that these embodiments are only used to illustrate the present application and not to limit the scope of the present application.

[0063] The star coronagraph deformable mirror actuator bad pixel interference analysis method of the present application, as shown in Figure 1 includes the following steps:

[0064] Step 1: Constructing the simulation system framework of the coronagraph high-contrast imaging, using the modulation filter to modulate the amplitude distribution, and optimizing the dark area through the deformable mirror to modulate the phase distribution; The system adopts the cooperative working mode of the focal plane filter and the deformable mirror: the focal plane filter realizes amplitude modulation through the precisely designed transmittance distribution, and forms an initial dark area with a contrast of 10^-6 order of magnitude on the focal plane; The deformable mirror is responsible for precise phase modulation, and through the SPGD algorithm, the contrast performance of the dark area is further improved to 10^-8 order of magnitude, realizing large dynamic range optimization from amplitude modulation to phase modulation.

[0065] The specific construction of the coronagraph high-contrast imaging system includes:

[0066] Filter modulation module: modulate the amplitude through the precisely designed transmittance distribution, form an initial dark area on the focal plane, and the contrast is 10^-6 order of magnitude;

[0067] Deformable mirror modulation module: continuously adjust the deformable mirror surface shape change through the actuator array to realize wavefront phase modulation optimization.

[0068] Step 2: Obtain the real distribution position of the deformable mirror actuator bad point through actual measurement, fix the measured bad point position information in the actuator control matrix, as shown in Figure 3

[0069] The specific implementation of the bad point position fixing includes:

[0070] Bad point position record: Establish a bad point distribution matrix to accurately record the spatial coordinate position of each bad point;

[0071] Control matrix fixing: Fix the wavefront phase value of the corresponding position of the bad point as the initial value.

[0072] Step 3: Establish a phase modulation control system based on the SPGD algorithm (Stochastic Parallel Gradient Descent Algorithm, which belongs to one of the gradient optimization algorithms), realize the dark area contrast gain (about 100 times) through iterative optimization; Implement adaptive phase optimization control based on the SPGD algorithm. The SPGD algorithm realizes model-free optimization through random probing and gradient estimation, and its core process includes: random disturbance generation, performance function evaluation, gradient estimation, control parameter update, and iterative optimization;

[0073] ​Step 4: Establish the convolution model of the inter-actuator coupled disturbance, simulate the actuator movement process, the response of the bad spot actuator, and the coupling effect of the bad spot on the adjacent normal actuator. Obtain and fix the actuator bad spot information, accurately identify the bad spot position in the deformable mirror through actuator response test, capacitance sensing or interference measurement, etc. Establish the measured bad spot distribution matrix. Lock the parameters of these bad spots in the corresponding position of the control matrix, so that they remain constant value in the SPGD iteration process and do not participate in adaptive update.

[0074] The modeling method of the bad spot influence in the actuator control matrix includes:

[0075] Normal actuator response modeling: Establish the voltage-displacement response function of the normal working actuator;

[0076] Bad spot actuator response characteristics: Model the bad spot response function to represent its inability to respond normally;

[0077] Control matrix correction: Integrate the actual measured bad spot information into the system control matrix to form a control model containing bad spot constraints;

[0078] Influence mechanism quantification: Quantitatively evaluate the influence degree of the bad spot on the dark area performance by comparing the system response difference with and without the bad spot.

[0079] The actuator interaction model based on convolution theory is established, and the surface deformation of the deformable mirror is represented as the linear superposition of the influence functions of each actuator. The specific implementation of the convolution simulation method includes:

[0080] Actuator influence function modeling: Establish the influence function of a single actuator on the surrounding area when it moves up

[0081] The constraint effect of the bad spot on the modulation ability of the adjacent actuator through mechanical coupling limits the numerical distribution of the influence function

[0082] Actuator influence analysis:

[0083] .

[0084] System analysis of the influence mechanism of the bad spot on the dark area modulation performance, and establishment of a three-level influence model:

[0085] First level: Modulation characteristics of normal actuators: Establish the voltage-displacement response function of the actuator under ideal conditions , analyze its modulation contribution to local and global wavefronts;

[0086] Second level: Spatial coupling effect: Study the constraint of the bad spot on the modulation efficiency of the adjacent normal actuator through mechanical coupling;

[0087] Third level: the final impact of bad pixel actuators: quantifying the local phase error caused by the absence of wavefront correction ability at the bad pixel location, the bad pixel response function is 0 or constant, evaluating its distortion effect on the point spread function (PSF), and finally analyzing the impact of bad pixels on imaging and dark area optimization.

[0088] Step 5: Introduce bad pixels into the dark area optimization process. When optimizing the dark area by the SPGD algorithm, the system monitors key indicators such as the change of dark area contrast in each quadrant, the convergence performance of optimization, and the decay trend of evaluation function, etc. to observe the influence of bad pixels on the dark area optimization process. Establish a quantitative evaluation system for bad pixel interference: first, by comparing the optimization results of each quadrant with and without bad pixels, Figure 6 ), establish the relationship between bad pixel distribution and dark area performance degradation, and quantitatively calculate the contrast loss rate; second, based on simulation analysis of different bad pixel distribution patterns, establish a correlation model between the number of bad pixels and the degree of system performance degradation; finally, by analyzing the dynamic decay process of the evaluation function, such as Figure 7 , quantifying the convergence speed change and final optimization performance difference caused by bad pixels. In-depth analysis of the impact of actuator bad pixels on dark area optimization: the impact of actuator bad pixels is spread to the adjacent area through the mechanical coupling effect between actuators, and there is a certain difference in the influence degree of different quadrant dark areas by the bad pixel location.

[0089] Among them, the unified description of the bad pixel interference mechanism is: the bad pixel prevents the actuator from rising, resulting in the absence of local wavefront correction ability caused by the bad pixel, which is transmitted to the entire optical system through the spatial coupling effect, and finally interferes with the dark area optimization process.

[0090] Among them, a comprehensive evaluation index system of bad pixel impact is established, taking 10^-8 order contrast as the benchmark, and through simulation verification, the influence law of different bad pixel distribution on the dark area optimization process is evaluated, including:

[0091] Evaluation function descent rate: by analyzing the change trend of the evaluation function in the optimization process, quantifying the system performance degradation speed caused by bad pixels;

[0092] Optimization convergence performance: evaluate the convergence speed and final performance difference of the SPGD algorithm with and without bad pixels;

[0093] System fault tolerance: establish a correlation model between the number of bad pixels and the degree of system performance degradation.

[0094] A mathematical model of the convolution simulation is established by the method of the application, specifically including a wavefront influence function of the actuator and a wavefront distribution of the whole system; a performance function of the SPGD algorithm uses dark area light intensity as an optimization target, and an algorithm target is to minimize the dark area light intensity; through analyzing an influence of an actual measurement bad point distribution on system performance, an engineering guiding principle of a bad point fault tolerance design is established, thereby providing a scientific basis for reliability design and maintenance strategy of the coronagraph system; an imaging model of the high contrast imaging system is obtained by Fourier transform of a pupil function of a system point spread function PSF; the performance function of the SPGD algorithm uses dark area light intensity definition as an evaluation standard; a descending rate of the evaluation function is quantified by analyzing a dynamic attenuation trend of the evaluation function in the optimization process, and a system performance degradation speed caused by the bad point is quantified.

[0095] A system for implementing the above method is shown in Figure 2 , and specifically includes:

[0096] A coronagraph high contrast imaging module: including a filter amplitude modulation unit and a deformable mirror phase modulation unit, to realize cooperative light field modulation;

[0097] An SPGD control algorithm module: to realize phase optimization based on random parallel gradient descent, containing a bad point constraint processing function;

[0098] A bad point detection and modeling module: to establish a bad point distribution model based on actual measurement data, to realize manual identification and fixing of the bad point;

[0099] A convolution simulation calculation module: to simulate spatial coupling effects among actuators, and to analyze a bad point influence mechanism; the module adopts a parallel computing architecture, and can simulate spatial coupling effects of a large-scale actuator array in real time, and support full-process simulation analysis from normal actuator motion influence to bad point coupling effects.

[0100] A performance evaluation and analysis module: to realize quantitative evaluation of bad point influence and system performance analysis.

[0101] The system can process dark area optimization under 100 times gain from 10^-6 order of magnitude to 10^-8 order of magnitude, and can still realize high contrast imaging performance under the condition of existing actual bad points.

[0102] Embodiment 1: Construction of a coronagraph high contrast imaging system

[0103] This embodiment constructs a coronagraph high contrast imaging simulation system working in a visible light waveband, and the core is a wavefront phase modulation technology based on a deformable mirror. The system uses measured wavefront phase data as initial input (the measured wavefront phase data is obtained by measuring a wavefront of a laser beam reflected by a mirror) Figure 4), combined with the focal plane filter amplitude transmittance function designed by numerical method, a continuous dark region is formed on the focal plane, and the initial contrast reaches the order of 10-6. The deformable mirror simulation model adopts a 32x32 actuator array (952 effective actuators), and the modulation area is optimized by the edge actuator shielding strategy. The optical simulation process consists of three key steps: first, the incident light is dynamically phase-modulated by the deformable mirror, then the amplitude modulation is completed at the focal plane filter, and finally the point spread function (PSF) image is generated by Fourier optical calculation, as shown in FIG. 2. Figure 5

[0104] Example 2: Implementation of SPGD algorithm

[0105] This embodiment details the simulation implementation process of dark region optimization using the SPGD algorithm. The algorithm parameter settings: the initial simulation wavefront data is used for initialization. Figure 4 For bad point processing, according to the set bad point position, the corresponding control parameters are fixed during the entire optimization process. The evaluation function is defined as the average contrast of the optimized dark region. The simulation results show that the system can improve the dark region contrast from the order of 10-6 to the order of 10-8 using the SPGD algorithm for dark region optimization.

[0106] Example 3: Analysis of interference characteristics of actual bad point distribution

[0107] Through simulation, it is set that two actuators numbered 721 and 811 in the deformable mirror are faulty, located in the edge region of the third quadrant. In the SPGD optimization process, the control parameters of these two bad points are fixed to zero: u_721 = 0, u_811 = 0. Based on the simulation analysis of the influence of actual bad points on system performance: according to the data in Table 1 below, the average contrast of each quadrant under the actual bad point condition is Q1: 3.32E-09, Q2: 3.24E-09, Q3: 5.20E-09, Q4: 3.65E-09, compared with the case without bad points (Q1: 4.16E-09, Q2: 3.43E-09, Q3: 3.66E-09, Q4: 2.83E-09), the contrast of the third quadrant has degraded but the overall performance has relatively small influence. The simulation results show that the current actual bad point distribution has weak influence on system performance, which can be effectively compensated by the optimization of the SPGD algorithm.

[0108] Table 1 Average contrast of each quadrant after optimization with and without bad points

[0109] Q1 Q2 Q3 Q4 No bad pixel 4.16E-09 3.43E-09 3.66E-09 2.83E-09 Actual bad pixel 3.32E-09 3.24E-09 5.20E-09 3.65E-09

[0110] Example 4: Quantitative modeling of actuator coupling effect

[0111] ​Based on the simulation analysis of the whole model, the mathematical model of the actuator is established. The influence of a single actuator on the surrounding area is described by a Gaussian distribution function, and the surface deformation of the deformable mirror is expressed as a linear superposition of the influence functions of each actuator. Simulation analysis shows that when the bad point actuator cannot respond to the control signal, the wavefront correction ability of this position is completely lost, and the effect of mechanical coupling between actuators spreads to the adjacent area, which requires more optimization steps to compensate. Physical mechanism analysis shows that the essence of the bad point affecting the wavefront modulation is the local lack of wavefront response matrix, which provides a theoretical basis for understanding the influence of bad points.

[0112] The present application establishes a complete actuator bad point interference analysis method system by combining theoretical modeling and simulation analysis. This method provides theoretical support for the development of high-contrast coronagraph imaging technology. Simulation results show that the method of the present application can accurately evaluate the influence of bad points on dark area optimization under non-ideal conditions with bad actuators, providing technical reference for the engineering application of direct imaging observation of exoplanets.

[0113] The present application analyzes the bad point interference of the deformable mirror actuator of the coronagraph. In view of the technical problem that the bad point of the deformable mirror actuator in the coronagraph system affects the dark area contrast optimization, the present application constructs a dark area optimization simulation system for high-contrast imaging of the coronagraph: amplitude modulation is realized through the focal plane modulation filter, so that the image contrast reaches the order of 10^-6, phase modulation is realized through the thousand-unit deformable mirror, i.e. dark area optimization, and the contrast reaches the order of 10^-8 through the SPGD modulation algorithm. The algorithm analyzes the actual actuator bad point position of the deformable mirror, establishes an actuator control model containing bad point influence, simulates normal actuator movement, bad point actuator response and the coupling effect of bad points on adjacent actuators. The influence of the bad point is analyzed by introducing the bad point influence into the dark area optimization process. The simulation results show that this analysis provides effective technical support for the engineering application of high-contrast imaging coronagraph systems.

[0114] The above-described embodiments only express several embodiments of the present application, which are described in detail and specifically, but should not be understood as limiting the scope of the present patent. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application.

Claims

1. A method for analyzing defect interference in the deformable mirror actuator of a coronagraph, characterized in that: Includes the following steps: Step 1: Construct the framework of the high-contrast imaging system for the coronagraph, use modulation filters to modulate the amplitude distribution, and optimize the dark area of ​​the phase distribution by modulating the phase distribution through deformable mirrors; Step 2: Obtain the actual distribution location of the defective points in the deformable mirror actuator through actual measurement, and fix the measured defective point location information in the actuator control matrix; Step 3: Establish a phase modulation control system based on the SPGD algorithm, and achieve dark area contrast gain through iterative optimization; the performance function of the SPGD algorithm uses dark area light intensity as the optimization target, and the algorithm objective is to minimize dark area light intensity; Step 4: Establish a convolutional model of interference between actuators, simulate the actuator motion process, the response of the defective actuator, and the coupling effect of the defective actuator on the adjacent normal actuator; Step 5: Introduce bad pixels into the dark area optimization process. When performing dark area optimization using the SPGD algorithm, the system monitors the key indicators of the dark area in each quadrant, observes the degree of influence of bad pixels on the dark area optimization process, and compensates for bad pixel interference in the deformable mirror actuator of the coronagraph.

2. The method according to claim 1, characterized in that: Also includes: Establish a quantitative evaluation system for dead pixel interference, including: First, by comparing the optimization results of each quadrant with and without dead pixels, the relationship between dead pixel distribution and dark area performance degradation is established, and the contrast loss rate is quantitatively calculated. Secondly, based on simulation analysis of different defect distribution patterns, a correlation model between the number of defective pixels and the degree of system performance degradation is established; Finally, by analyzing the dynamic decay process of the evaluation function, the changes in convergence speed and the differences in final optimized performance caused by bad points are quantified; the influence of actuator bad points spreads to the neighboring region through the mechanical coupling effect between actuators, and the degree of influence of bad point location varies in dark areas of different quadrants. Establish a comprehensive evaluation index system for the impact of bad pixels, including: Evaluation function decline rate: By analyzing the changing trend of the evaluation function during the optimization process, the rate at which system performance degrades due to bad points is quantified; Optimize convergence performance: Evaluate the difference in convergence speed and final performance of the SPGD algorithm with and without bad pixels; System fault tolerance: Establish a correlation model between the number of bad points and the degree of system performance degradation.

3. The method according to claim 1, characterized in that: In step 1, the specific construction of the high-contrast imaging system for the coronagraph includes: Filter modulation: The amplitude is modulated by a precisely designed transmittance distribution to form an initial dark area on the focal plane, with a contrast on the order of 10^-6. Deformable mirror modulation: Wavefront phase modulation optimization is achieved by continuously adjusting the shape of the deformable mirror through an actuator array.

4. The method according to claim 1, characterized in that: In step 2, the specific implementation of fixing the location of the defective pixel includes: Defect location recording: Establish a defect distribution matrix to accurately record the spatial coordinates of each defect; Control matrix fixed: The wavefront phase value at the location corresponding to the bad point is fixed to the initial value.

5. The method according to claim 1, characterized in that: In step 4, the modeling method for the impact of bad points in the actuator control matrix includes: Normal actuator response modeling: Establish the voltage-displacement response function of a normally operating actuator; Bad pixel actuator response characteristics: Modeling the bad pixel response function to characterize its inability to respond normally; Control matrix correction: Integrate the actual measured bad point information into the system control matrix to form a control model that includes bad point constraints; Quantitative analysis of the impact mechanism: By comparing the differences in system response with and without dead pixels, the degree of impact of dead pixels on dark area performance is quantitatively assessed; The specific implementations of the convolution simulation method include: Actuator influence function modeling: Establish the influence function of a single actuator on the surrounding area during its upward motion; The influence of bad pixels on the function's numerical distribution is limited by their mechanical coupling effect on the modulation capability of adjacent actuators. Actuator Influence Analysis: 。 6. The method according to claim 1, characterized in that: It also includes a three-level analysis step for the impact of bad pixels: First level of influence: Modulation characteristic analysis of normal actuators, and establishment of voltage-displacement response function under ideal conditions; Second-level impact: Spatial coupling effect analysis, studying the limiting effect of bad pixels on the modulation efficiency of adjacent actuators through mechanical coupling; The third level of impact: the final impact assessment of the bad pixel actuator, quantifying the local phase error caused by the lack of wavefront correction capability at the location of the bad pixel.

7. A system for analyzing dead pixel interference in a deformable mirror actuator of a coronagraph, implementing the method of any one of claims 1-6, characterized in that: include: The high-contrast imaging module of the star coronagraph includes a filter amplitude modulation unit and a deformable mirror phase modulation unit to achieve coordinated light field modulation. SPGD control algorithm module: Implements phase optimization based on stochastic parallel gradient descent, including bad pixel constraint handling function; Defect Pixel Detection and Modeling Module: Establishes a defect pixel distribution model based on actual measurement data to enable manual identification and fixation of defect pixels; Convolution simulation module: Simulates the spatial coupling effect between actuators and analyzes the mechanism of bad pixel impact; Performance evaluation and analysis module: Enables quantitative evaluation of the impact of dead pixels and system performance analysis.

8. The system according to claim 7, characterized in that: The convolution simulation calculation module adopts a parallel computing architecture to simulate the spatial coupling effect of a large-scale actuator array in real time, and supports the simulation analysis of the entire process from the influence of normal actuator movement to the coupling effect of bad points.

9. The system according to claim 8, characterized in that: The system is capable of handling dark area optimization from the 10^-6 level to the 10^-8 level with a 100x gain, and can still achieve high-contrast imaging performance even with actual dead pixels.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by the processor, the program implements the method for analyzing dead pixel interference of the deformable mirror actuator of the coronagraph as described in any one of claims 1-6.

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