The Chinese Space Station Telescope (CSST) spectroscopic survey aims to deliver high-quality low-resolution ($R > 200$) slitless spectra for hundreds of millions of targets down to a limiting magnitude of about 21 mag, distributed within a large survey area (17500 deg$^2$) and covering a wide wavelength range (255-1000 nm by 3 bands GU, GV, and GI). As slitless spectroscopy precludes the usage of wavelength calibration lamps, wavelength calibration is one of the most challenging issues in the reduction of slitless spectra, yet it plays a key role in measuring precise radial velocities of stars and redshifts of galaxies. In this work, we propose a star-based method that can monitor and correct for possible errors in the CSST wavelength calibration using normal scientific observations, taking advantage of the facts that i) there are about ten million stars with reliable radial velocities now available thanks to spectroscopic surveys like LAMOST, ii) the large field of view of CSST enables efficient observations of such stars in a short period of time, and iii) radial velocities of such stars can be reliably measured using only a narrow segment of CSST spectra. We demonstrate that it is possible to achieve a wavelength calibration precision of a few $\mathrm{km}\,\mathrm{s}^{-1}$ for the GU band, and about 10 to 20 $\mathrm{km}\,\mathrm{s}^{-1}$ for the GV and GI bands, with only a few hundred velocity standard stars. Implementations of the method to other surveys are also discussed.
Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；Science Center for China Space Station Telescope, National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台中国空间站望远镜科学中心）；University of Chinese Academy of Sciences（中国科学院大学）
Magnetic activity of ultracool dwarfs in the LAMOST DR11
Yunnan Observatories, Chinese Academy of Sciences（云南天文台，中国科学院）；Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences（天文物理重点实验室，中国科学院）；International Centre of Supernovae, Yunnan Key Laboratory（超新星国际中心，云南重点实验室）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（天文与空间科学学院，中国科学院大学）
Mock Observations of Multiple Stellar Populations in Tidal Streams of Palomar 5 for the Chinese Space Station Survey Telescope
School of Physics and Astronomy, Sun Yat-sen University（中山大学物理学院）；CSST Science Center for the Guangdong-Hong Kong-Macau Greater Bay Area（中国空间站巡天望远镜粤港澳大湾区科学中心）
Predicting the detection yields of giant planets and brown dwarfs with CSST astrometry
Tsung-Dao Lee Institute, Shanghai Jiao Tong University（上海交大李政道研究所）；School of Physics and Astronomy, Shanghai Jiao Tong University（上海交通大学物理与天文学院）；Shanghai Astronomical Observatory, Chinese Academy of Sciences（中国科学院上海天文台）；School of Astronomy and Space Sciences, University of Chinese Academy of Sciences (UCAS)（中国科学院大学天文学与空间科学学院）；School of Physics and optoelectronic engineering, Anhui University（安徽大学物理与光电工程学院）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）
CSST-PSFNet: A Point Spread Function Reconstruction Model for the CSST Based on Deep Learning
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Institute of Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学前沿交叉科学研究院）；Zhejiang Lab（浙江实验室）；Center For Astrophysics, Guangzhou University（广州大学天体物理中心）
Characterization of the CSST Survey Camera CCDs: I. basic electro-optical performance
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；University of Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）
MCI: Multi-Channel Imager on the Chinese Space Station Survey Telescope
Shanghai Astronomical Observatory, Chinese Academy of Sciences（中国科学院上海天文台）；Shanghai Institute of Technical Physics, Chinese Academy of Sciences（中国科学院上海技术物理研究所）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心）；Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所）；School of Astronomy and Space Science, Nanjing University（南京大学天文与空间科学学院）；Department of Astronomy, Xiamen University（厦门大学天文学系）；School of Physics and Astronomy, Sun Yat-Sen University（中山大学物理学院）；Yunnan Observatories, Chinese Academy of Sciences（中国科学院云南天文台）；Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）；South-Western Institute for Astronomy Research, Yunnan University（云南大学西南天文研究所）；CSST Science Center for the Yangtze River Delta Region（CSST长三角科学中心）
Performance Calibration of the Wavefront Sensor's EMCCD Detector for the Cool Planets Imaging Coronagraph Aboard CSST
Nanjing Institute of Astronomical Optics & Technology, Chinese Academy of Sciences（中国科学院南京天文光学技术研究所）；CAS Key Laboratory of Astronomical Optics & Technology, Nanjing Institute of Astronomical Optics & Technology（中国科学院天文光学与技术重点实验室，中国科学院南京天文光学技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Stellar Density Classification and Regression for CSST Multi-color Imaging Using Deep Learning
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学天文与前沿科学研究院）；School of Physics and Astronomy, China West Normal University（西华师范大学物理与天文学学院）
Mock Observations for the CSST Mission: Integral Field Spectrograph--GEHONG: A Package for Generating Ideal Datacubes
Hebei Normal University（河北师范大学）；Guoshoujing Institute of Astronomy, Hebei Normal University（河北师范大学郭守敬天文研究所）；Shanghai Astronomical Observatory, Chinese Academy of Sciences（中国科学院上海天文台）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）
A Robust Geometric Distortion Solution for Main Survey Camera of CSST
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；University of Chinese Academy of Sciences（中国科学院大学）；Institute for Frontiers in Astronomy and Astrophysics of Beijing Normal University（北京师范大学前沿天文与天体物理研究所）；Zhejiang Lab（浙江实验室）；Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所）
Estimating stellar atmospheric parameters and elemental abundances using fully connected residual network
CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories（中国科学院光学天文重点实验室，国家天文台）；University of Chinese Academy of Sciences（中国科学院大学）；School of Physics, Astronomy and Mathematics, University of Hertfordshire（赫特福德大学物理、天文与数学学院）
Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey
Yunnan Observatories, Chinese Academy of Sciences（中国科学院云南天文台）；Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences（中国科学院天体结构与演化重点实验室）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）
我们将变分自动编码器（VAE）应用于LAMOST-K2低分辨率光谱，以探测K2场中恒星的磁活动。在对选定的不活跃恒星的光谱进行训练之后，VAE模型可以有效地生成谱相减过程所需的合成参考模板，而无需知道任何恒星参数。然后，我们在我们的样品中检测到一些特殊的光谱特征，如色球发射、强星云发射和锂吸收。我们测量的色球活动指标，H $α$和钙II红外三线（IRT）线的排放量，以量化恒星的磁活动。活动星的H $α$和Ca II IRT线的过量发射与K 2测光得到的光变曲线的自转周期和振幅有很好的相关性。我们对LAMOST光谱进行了降阶处理，模拟了中国空间站望远镜（CSST）的无缝光谱，并对模拟数据进行了VAE处理。对于冷的活动星，我们揭示了两种分辨率的光谱得到的H $α$线的等效宽度（EW）之间的良好一致性。结果表明，在未来的CSST调查，这将提供一个前所未有的大数据库的低分辨率光谱，以及同时多波段测光的恒星磁活动星的能力。
CPI-C: Cool Planet Imaging Coronagraph on Chinese Space Station Survey Telescope
Nanjing Institute of Astronomical Optics \& Technology, Chinese Academy of Sciences, Nanjing 210042, China；CAS Key Laboratory of Astronomical Optics \& Technology, Nanjing Institute of Astronomical Optics \& Technology, Nanjing 210042,China Received 20xx month day
