25 citations
- University of Science and Technology of ChinaCN11 papers
- Anqing Normal UniversityCN5 papers
- Chinese Academy of SciencesCN5 papers
- Dalian University of TechnologyCN4 papers
- Nanjing UniversityCN3 papers
- Shandong UniversityCN3 papers
- Shanghai Astronomical ObservatoryCN3 papers
- Shanghai Jiao Tong UniversityCN3 papers
- Chinese University of Hong KongHK2 papers
- National Astronomical ObservatoriesCN2 papers
- Scuola Internazionale Superiore di Studi AvanzatiIT2 papers
- Astronomy and SpaceAU1 paper
13 papers · 1 filter
The Low-Frequency Spectra of Radio Pulsars
Ting Yu, Zhongli Zhang, Hongyu Gong +1
Low-frequency spectral studies of radio pulsars represent a key method for uncovering their emission mechanisms, magnetospheric structure, and signal interactions with the surround…
LAMOST medium-resolution spectroscopic survey of Galactic Open Clusters (LAMOST-MRS-O): An overview of survey plan and preliminary results
Xi Zhang, Chengzhi Liu, Jing Zhong +7
As part of the LAMOST medium-resolution spectroscopic survey, the LAMOST-MRS-O is a non-time domain survey that aims to perform medium-resolution spectral observations for member s…
An efficient unsupervised classification model for galaxy morphology: Voting clustering based on coding from ConvNeXt large model
Guanwen Fang, Yao Dai, Zesen Lin +6
In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy mo…
Preparation for CSST: Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method
Shiliang Zhang, Guanwen Fang, Jie Song +6
Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supe…
Blind Search of The Solar Neighborhood Galactic Disk within 5kpc: 1,179 new Star clusters found in Gaia DR3
Huanbin Chi, Feng Wang, Wenting Wang +2
Studying open clusters (OCs) is essential for a comprehensive understanding of the structure and evolution of the Milky Way. Many previous studies have systematically searched for…
Automatic morphological classification of galaxies: convolutional autoencoder and bagging-based multiclustering model
C. C. Zhou, Y. Z. Gu, G. W. Fang +1
In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can…