Digital twin of atmospheric turbulence phase screens based on deep neural networks

Jia, Peng; Ning, Runyu; Xue, Xiaolei

doi:10.1364/oe.460244

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Additionally, we simulate the impact of complexbackground noise on these images, introducing large-scale nonuniformities. We adopt the approach proposed by Jia et al (2015Jia et al ( , 2022 to generate the distribution of complex-background noise. The gray-scale values of these phase screens are normalized to a range of 0-1, representing the coverage levels of complex-background noise.…”

Section: Performance Evaluation Using Simulated Images With Complex-b...mentioning

confidence: 99%

An Image Quality Evaluation and Masking Algorithm Based On Pretrained Deep Neural Networks

Jia,

Song,

et al. 2024

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With the growing amount of astronomical data, there is an increasing need for automated data processing pipelines, which can extract scientific information from observation data without human interventions. A critical aspect of these pipelines is the image quality evaluation and masking algorithm, which evaluate image qualities based on various factors such as cloud coverage, sky brightness, scattering light from the optical system, point-spread-function size and shape, and read-out noise. Occasionally, the algorithm requires masking of areas severely affected by noise. However, the algorithm often necessitates significant human interventions, reducing data processing efficiency. In this study, we present a deep-learning-based image quality evaluation algorithm that uses an autoencoder to learn features of high quality astronomical images. The trained autoencoder enables automatic evaluation of image quality and masking of noise affected areas. We have evaluated the performance of our algorithm using two test cases: images with point spread functions of varying full width half magnitude, and images with complex backgrounds. In the first scenario, our algorithm could effectively identify variations of the point spread functions, which can provide valuable reference information for photometry. In the second scenario, our method could successfully mask regions affected by complex regions, which could significantly increase the photometry accuracy. Our algorithm can be employed to automatically evaluate image quality obtained by different sky surveying projects, further increasing the speed and robustness of data processing pipelines.

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Section: Performance Evaluation Using Simulated Images With Complex-b...mentioning

confidence: 99%

An Image Quality Evaluation and Masking Algorithm Based On Pretrained Deep Neural Networks

Jia,

Song,

et al. 2024

Self Cite

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show abstract

“…Besides, we could not control the outer environment, which would reduce the training efficiency. Thanks to recent developments in numerical simulation and digital twin technologies (Makoviychuk et al 2021;Jia et al 2022a;Huang et al 2022;Rojas et al 2022), we could build a digital twin of the real world with many highly optimized blocks. With a digital twin of the real world, DRL algorithms could be developed and used with faster speed in real applications.…”

Section: Introductionmentioning

confidence: 99%

Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning

Jia

Jiang

et al. 2023

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Time-domain astronomy is an active research area now, which requires frequent observations of the whole sky to capture celestial objects with temporal variations. In the optical band, several telescopes in different locations could form a distributed telescope array to capture images of celestial objects continuously. However, there are millions of celestial objects to observe each night, and only limited telescopes could be used for observation. Besides, the observation capacity of these telescopes would be affected by different effects, such as the sky background or the seeing condition. It would be necessary to develop an algorithm to optimize the observation strategy of telescope arrays according to scientific requirements. In this paper, we propose a novel framework that includes a digital simulation environment and a deep reinforcement learning algorithm to optimize observation strategy of telescope arrays. Our framework could obtain effective observation strategies given predefined observation requirements and observation environment information. To test the performance of our algorithm, we simulate a scenario that uses distributed telescope arrays to observe space debris. Results show that our algorithm could obtain better results in both discovery and tracking of space debris. The framework proposed in this paper could be used as an effective strategy optimization framework for distributed telescope arrays, such as the Sitian project or the TIDO project.

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A simulation framework for telescope array and its application in distributed reinforcement learning-based scheduling of telescope arrays

Jia

Jiang

et al. 2023

Astronomy and Computing

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Digital twin of atmospheric turbulence phase screens based on deep neural networks

Cited by 6 publications

References 17 publications

An Image Quality Evaluation and Masking Algorithm Based On Pretrained Deep Neural Networks

An Image Quality Evaluation and Masking Algorithm Based On Pretrained Deep Neural Networks

Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning

A simulation framework for telescope array and its application in distributed reinforcement learning-based scheduling of telescope arrays

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