Atmospheric seeing, a crucial astronomical meteorological parameter, directly affects the imaging quality of astronomical telescopes. Establishing a reliable mechanism for predicting atmospheric seeing is vital for enabling flexible scheduling of telescope observations and enhancing observational efficiency. This study aims to develop a forecasting mechanism for atmospheric seeing over both short timescales (one to two hours) and long timescales (up to three days), based on a combination of the mesoscale meteorological model Weather Research and Forecasting (WRF) and Recurrent Neural Networks (RNN). The WRF model predicts meteorological parameters for a given future period at the target astronomical site, which, when coupled with an atmospheric seeing analytical model, facilitates seeing forecasts for a long-time scale. Concurrently, the RNN establishes a relationship between observed meteorological parameters and seeing, enabling short time-scale predictions of atmospheric seeing at the site. Experiments conducted at target astronomical observatory demonstrate the reliability of our proposed forecasting strategy.
Adaptive optics (AO) systems are trending towards miniaturization and cost reduction, with wavefront sensorless adaptive optics systems (WFSless AOSs) emerging as a field of interest due to their simple structure and application versatility. The advent of deep learning has propelled the use of convolutional neural networks (CNNs) to extract aberration information from CCD images. Nevertheless, CNNs often fail to focus on the regions of images where effective information is concentrated, which limits the accuracy in aberration extraction. This paper introduces a novel Swin-UNet-based model for WFSless AOSs based on point source that employs an attention mechanism to target relevant areas within CCD light intensity images, thus addressing CNN shortcomings. Furthermore, the proposed model fuses in-focus and out-of- focus image information to directly output the reconstructed wavefront image, enhancing the overall wavefront reconstruction process. Our simulations across various D/r0 ratios reveal significant improvements with the Swin-UNet-based model: a reduction in rms wavefront error from 0.0219 to 0.0061 wavelengths at D/r0=1, from 0.0806 to 0.03825 at D/r0=6, and from 0.1241 to 0.0991 at D/r0=11. Correspondingly, the Strehl ratio improved from 0.9950 to 0.9988, from 0.8380 to 0.9567, and from 0.6814 to 0.7522, indicating enhanced image quality post-correction. Compared with existing CNN-based technology, our Swin-UNet approach can more effectively concentrate on the relevant areas of the image and mitigate the influence of invalid regions on the analysis, thereby substantially improving the effectiveness and robustness of the correction performance in WFSless AOSs.
With the great development of solar telescope, astronomers are always trying to achieve higher guiding precision, which is one of the most important technologies for the solar observation. At present, the most popular solar guiding depends on the imaging mapping, correlation procession and capture the solar image by CCD directly. In this paper, a new full disk solar guiding method based on post-focus lens obstruction are proposed. A simple structural modification has been used to improve our solar guiding measurement and even for other extended sources. It has been proved that our new method is correct and feasible after building a simulated experiment system and running lots of experiment tests.
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