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The China Space Station Telescope (CSST) is a forthcoming space-based optical telescope designed to co-orbit with the Chinese Space Station. With a planned slitless spectroscopic survey spanning a broad wavelength range of $255-1000$nm and…

The upcoming imaging survey of the Chinese Space-station Survey Telescope (CSST) will deliver high-resolution imaging of an unprecedented number of galaxies for galaxy studies. To understand CSST's capability, and to support the preparation…

星系天体物理 · 物理学 2026-04-28 Ziqi Ma , Si-Yue Yu , Taotao Fang , Jinyi Shangguan , Zhao-Yu Li , Luis C. Ho

The advancement in sensitivity and field of view of next-generation wide-field survey telescopes requires astrometric measurements with high precision, even in the presence of significant geometric distortions. To address this challenge, we…

Large sky spectroscopic surveys have reached the scale of photometric surveys in terms of sample sizes and data complexity. These huge datasets require efficient, accurate, and flexible automated tools for data analysis and science…

The China Space Station Telescope (CSST) is the next-generation Stage~IV survey telescope. It can simultaneously perform multi-band imaging and slitless spectroscopic wide- and deep-field surveys in ten years and an ultra-deep field (UDF)…

Accurate estimation of photometric redshifts (photo-$z$s) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting methods and machine learning techniques, each with its own…

Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification…

太阳与恒星天体物理 · 物理学 2020-01-08 Kaushal Sharma , Ajit Kembhavi , Aniruddha Kembhavi , T. Sivarani , Sheelu Abraham , Kaustubh Vaghmare

The China Space Station Telescope (CSST) will conduct wide-field multiband photometric imaging and slitless spectroscopic surveys, advancing cosmology and galaxy evolution studies. Achieving CSST's cosmological goals requires precise…

The internal structure of dark matter halos on sub-galactic scales remains a key open question, particularly in the context of the core-cusp problem. Ultra-faint dwarf galaxies (UFDs), owing to their extreme dark matter dominance, provide a…

星系天体物理 · 物理学 2026-04-24 Yixi Tao , Haijun Tian , Bin Yue , Jorge Peñarrubia

Superluminous supernovae (SLSNe) are a class of intense celestial events that can be standardized for measuring cosmological parameters, bridging the gap between type Ia supernovae and the cosmic microwave background. In this work, we…

高能天体物理现象 · 物理学 2024-07-01 Xuan-Dong Jia , Jian-Ping Hu , Fa-Yin Wang , Zi-Gao Dai

Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it's impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of…

天体物理仪器与方法 · 物理学 2022-03-09 Ben Henghes , Connor Pettitt , Jeyan Thiyagalingam , Tony Hey , Ofer Lahav

We demonstrate the potential of Deep Learning methods for measurements of cosmological parameters from density fields, focusing on the extraction of non-Gaussian information. We consider weak lensing mass maps as our dataset. We aim for our…

宇宙学与河外天体物理 · 物理学 2017-07-19 Jorit Schmelzle , Aurelien Lucchi , Tomasz Kacprzak , Adam Amara , Raphael Sgier , Alexandre Réfrégier , Thomas Hofmann

Neutron star (NS) has many extreme physical conditions, and one may obtain some important informations about NS via accreting neutron star binary (ANSB) systems. The upcoming Chinese Space Station Telescope (CSST) provides an opportunity to…

太阳与恒星天体物理 · 物理学 2024-09-06 Hao Shen , Shun-Yi Lan , Xiang-Cun Meng

The measurement of atmospheric parameters is fundamental for scientific research using stellar spectra. The Chinese Space Station Telescope (CSST), scheduled to be launched in 2024, will provide researchers with hundreds of millions of…

太阳与恒星天体物理 · 物理学 2024-08-21 JiaRui Rao , HaiLiang Chen , JianPing Xiong , LuQian Wang , YanJun Guo , JiaJia Li , Chao Liu , ZhanWen Han , XueFei Chen

Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent analyses. In this work, we investigate two major forms of…

天体物理仪器与方法 · 物理学 2022-06-15 Q. Lin , D. Fouchez , J. Pasquet , M. Treyer , R. Ait Ouahmed , S. Arnouts , O. Ilbert

The upcoming Chinese Space Station Telescope (CSST) slitless spectroscopic survey poses a challenge of flux calibration, which requires a large number of flux-standard stars. In this work, we design an uncertainty-aware residual attention…

天体物理仪器与方法 · 物理学 2024-01-25 Lin Yang , Haibo Yuan , Fuqing Duan , Ruoyi Zhang , Bowen Huang , Kai Xiao , Shuai Xu , Jinming Zhang

Optical spectra of galaxies and quasars from large cosmological surveys are used to measure redshifts and infer distances. They are also rich with information on the intrinsic properties of these astronomical objects. However, their…

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that…

天体物理学 · 物理学 2008-11-26 Nicholas M. Ball , Robert J. Brunner , Adam D. Myers , David Tcheng

Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification…

天体物理仪器与方法 · 物理学 2023-09-26 Zihan Kang , Yanxia Zhang , Jingyi Zhang , Changhua Li , Minzhi Kong , Yongheng Zhao , Xue-Bing Wu

Galaxy-galaxy strong gravitational lensing (GGSL) is a powerful probe for the formation and evolution of galaxies and cosmology, while the sample size of GGSLs leads to considerable uncertainties and potential bias. The China Space Station…

星系天体物理 · 物理学 2024-07-31 Xiaoyue Cao , Ran Li , Nan Li , Rui Li , Yun Chen , Keyi Ding , Huanyuan Shan , Hu Zhan , Xin Zhang , Wei Du , Shuo Cao