In the eighth data release (DR8) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, more than 318,740 low-resolution stellar spectra with types from B to early F and signal-to-noise ratios >50 were released. With this large volume of the early-type stars, we tried machine-learning algorithms to search for class-one and class-two chemical peculiars (CP1 and CP2), and to detect spectral features to distinguish the two classes in low-resolution spectra. We selected the XGBoost algorithm after comparing the classification efficiency of three machine-learning ensemble algorithms. Using XGBoost followed by the visual investigation, we presented a catalog of 20,694 sources, including 17,986 CP1 and 2708 CP2, in which 6917 CP1 and 1652 CP2 are newly discovered. We also list the spectral features to separate CP1 from CP2 discovered through XGBoost. The stellar parameters (including effective temperature (T _eff), surface gravity (log g), metallicity [Fe/H]), the spatial distribution in Galactic coordinates, and the color magnitude were provided for all of the entries of the catalog. The T _eff for CP1 distributes from ∼6000 to ∼8500 K, while for CP2 it distributes from ∼7000 to ∼13,700 K. The log g of CP1 ranges from 2.8 to 4.8 dex, peaking at 4.5 dex, and of CP2 it ranges from 2.0 to 5.0 dex, peaking at 3.6 dex, respectively. The [Fe/H] of CP1 and CP2 are from −1.4 to 0.4 dex, and the [Fe/H] of CP1 are on average higher than that of CP2. Almost all of the targets in our sample locate around the Galactic plane.

The lack of a large-aperture optical/infrared telescope has seriously affected the development of astronomy in China. In 2016, the authors published their concept study and suggestions for a 12-m telescope optical system. This article presents the authors' further research and some new results. Considering that this telescope should be a general-purpose telescope for a wide range of scientific goals and could be used for frontier scientific research in the future, the authors studied and designed a variety of 12-m telescope optical systems for comparison and final decision-making. In general, we still adopt our previous configuration, but the Nasmyth and prime-focus corrector systems have been greatly improved. In this article, the adaptive optics is given special attention. Ground-layer adaptive optics (GLAO) is adopted. It has a 14-arcmin field of view. The secondary mirror is used as the adaptive optical deformable mirror. Obviously, not all the optical systems in this telescope configuration will be used or constructed at the same stage. Some will be for the future and some are meant for research rather than for construction.

This is an optical/infrared telescope. It is a general purpose telescope. A segmented primary mirror is adopted with diameter 12 m and f-ratio 1.6. This telescope has prime focus, Cassegrain, Nasmyth and coudé systems. The prime focus system has a 1$ $_{.}^{\circ}$$5 field-of-view corrector with excellent image quality. It will be used for multi-object fibre spectroscopic observation and CCD photography. In this 12-m telescope, all systems except the prime focus system share the same secondary mirror; the Nasmyth and coudé systems are formed by adding relay mirrors; and the method of moving a secondary mirror is used to enhance image quality. All these features originate from the innovative optical system of the Chinese 2.16-m telescope. At the Cassegrain focus, a dark object spectrograph, for example, can be installed without any corrector; and in case a field of view is needed, a corrector may be added. Both the Nasmyth and coudé systems have exceptionally excellent image quality and the exchange between them occurs very conveniently. Many instruments in visible and infrared wavebands, some with an adaptive optics system, will be installed at the Nasmyth platform. Coudé system will be used for interferometry and for those instruments which require high stability. This configuration has a nearly full range of systems but it is not complex, and it can even be adopted by 20–40-m class telescopes.

With new artificial intelligence (AI) technologies and application scenarios constantly emerging, AI technology has become widely used in astronomy and has promoted notable progress in related fields. A large number of papers have reviewed the application of AI technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status of and research hotspots in telescope intelligence from these papers. This paper combines the development history of AI technology and difficulties with critical telescope technologies, comprehensively introduces the development of and research hotspots in telescope intelligence, conducts a statistical analysis of various research directions in telescope intelligence, and defines the merits of these research directions. A variety of research directions are evaluated, and research trends in each type of telescope intelligence are indicated. Finally, according to the advantages of AI technology and trends in telescope development, potential future research hotspots in the field of telescope intelligence are given.

In this paper, co-phasing errors of a segmented primary mirror tiled by hexagonal segments are successfully calculated for the 12-meter Large aperture Optical/infrared Telescope (LOT). Co-phasing errors including out-of-plane errors are simulated separately and comprehensively based on several software simulation platforms. PAOLA simulation results show that the Strehl Ratio (SR) of LOT is larger than 0.8 when the RMS value of tip-tilt obeying a normal distribution is less than 0.018 arcsec, and the SR of LOT is larger than 0.8 when the RMS value of piston obeying a normal distribution is less than 40 nm. Besides, simulation results of Zemax show that the SR of LOT is larger than 0.8 when the RMS value of tip-tilt obeying a normal distribution is less than 0.02 arcsec, and the SR of LOT is larger than 0.8 when the RMS value of piston obeying a normal distribution is less than 40 nm. These simulation results successfully lay a solid foundation for LOT (especially the segmented primary mirror with active optics).

Shack–Hartmann wavefront sensors (SHWFSs) are crucial for detecting distortions in adaptive optics systems, but the accuracy of wavefront reconstruction is often hampered by low guide star brightness or strong atmospheric turbulence. This study introduces a new method of using the Vision Transformer model to process image information from SHWFSs. Compared with previous traditional methods, this model can assign a weight value to each subaperture by considering the position and image information of each subaperture of this sensor, and it can process to obtain wavefront reconstruction results. Comparative evaluations using simulated SHWFS light intensity images and corresponding deformable mirror command vectors demonstrate the robustness and accuracy of the Vision Transformer under various guide star magnitudes and atmospheric conditions, compared to convolutional neural networks (CNNs), represented in this study by Residual Neural Network (ResNet), which are widely used by other scholars. Notably, normalization preprocessing significantly improves the CNN performance (improving Strehl ratio by up to 0.2 under low turbulence) while having a varied impact on the Vision Transformer, improving its performance under a low turbulence intensity and high brightness (Strehl ratio up to 0.8) but deteriorating under a high turbulence intensity and low brightness (Strehl ratio reduced to about 0.05). Overall, the Vision Transformer consistently outperforms CNN models across all tested conditions, enhancing the Strehl ratio by an average of 0.2 more than CNNs.