Image restoration methods are commonly used to improve the quality of astronomical images. In recent years, developments of deep neural networks and increments of the number of astronomical images have evoked a lot of data–driven image restoration methods. However, most of these methods belong to supervised learning algorithms, which require paired images either from real observations or simulated data as training set. For some applications, it is hard to get enough paired images from real observations and simulated images are quite different from real observed ones. In this paper, we propose a new data–driven image restoration method based on generative adversarial networks with option–driven learning. Our method uses several high resolution images as references and applies different learning strategies when the number of reference images is different. For sky surveys with variable observation conditions, our method can obtain very stable image restoration results, regardless of the number of reference images.

Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently. The next-generation large-scale sky imaging surveys are expected to discover thousands of cluster-scale strong lenses, which would lead to unprecedented opportunities for applying cluster-scale strong lenses to solve astrophysical and cosmological problems. However, the large data set challenges astronomers to identify and extract strong-lensing signals, particularly strongly lensed arcs, because of their complexity and variety. Hence, we propose a framework to detect cluster-scale strongly lensed arcs, which contains a transformer-based detection algorithm and an image simulation algorithm. We embed prior information of strongly lensed arcs at cluster scale into the training data through simulation and then train the detection algorithm with simulated images. We use the trained transformer to detect strongly lensed arcs from simulated and real data. Results show that our approach could achieve 99.63% accuracy rate, 90.32% recall rate, 85.37% precision rate, and 0.23% false-positive rate in detection of strongly lensed arcs from simulated images and could detect almost all strongly lensed arcs in real observation images. Besides, with an interpretation method, we have shown that our method could identify important information embedded in simulated data. Next, to test the reliability and usability of our approach, we will apply it to available observations (e.g., DESI Legacy Imaging Surveys 6 6 https://www.legacysurvey.org/ ) and simulated data of upcoming large-scale sky surveys, such as Euclid 7 7 https://www.euclid-ec.org/ and the China Space Station Telescope. 8 8 https://nao.cas.cn/csst/

The digital twin of optical systems can imitate its response to outer environments through connecting outputs from data–driven optical element models with numerical simulation methods, which could be used for system design, test and troubleshooting. Data-driven optical element models are essential blocks in digital twins. It can not only transform data obtained from sensors in real optical systems to states of optical elements in digital twins, but also simulate behaviors of optical elements with real measurements as prior conditions. For ground based optical telescopes, the digital twin of atmospheric turbulence phase screens is an important block to be developed. The digital twin of atmospheric turbulence phase screens should be able to generate phase screens with infinite length and high similarities to real measurements. In this paper, we propose a novel method to build the digital twin of atmospheric turbulence phase screens. Our method uses two deep neural networks to learn mapping functions between the space of parameters and the space of phase screens and vice versa. Meanwhile, a forecasting deep neural network is proposed to generate parameters for the next phase screen according to parameters extracted from a previous phase screen. The method proposed in this paper could be used to directly produce phase screens with infinite length and of any temporal or spatial power spectral density that follows statistical distributions of real measurements, which makes it an appropriate block in digital twins of ground based optical systems.

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