Open clusters identifying by multi-scale density feature learning

Abstract

Open clusters (OCs) are important objects in exploring the structure and history of the Milky Way. Large amount of sky survey data can be used to detect OCs. However, analyzing these data manually has become a bottleneck of OC identification. This study proposes a multi-scale density feature learning (MSDFL), which includes the open cluster kernel density map to visualize the features of OCs; and open cluster identifying network, which is a deep learning model used to perform identifying with the maps. A test set and experimental region are utilized to evaluate the effectiveness of our method. For OCs that stand out as significant overdensities, experimental results show that the MSDFL method can achieve the accuracy of 94%. Lastly, the proposed method can successfully identify real OCs in the experimental sky region. The code is available at: https://gitee.com/colab_worker/cluster_search.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61806022, in part by State Key Laboratory of Geo-Information Engineering, No. SKLGIE2018-M-3-4, and the Fundamental Research Funds for the Central Universities, CHD, Nos. 300102269103, 300102269304, and 300102269205, in part by National Key R&D Program of China No. 2019YFA0405501. We also thank the fund of Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory. This work has made use of data from the European Space Agency mission Gaia (www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.

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Correspondence to Jiangbo Xi or Zhengyi Shao.

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Xiang, Y., Xi, J., Shao, Z. et al. Open clusters identifying by multi-scale density feature learning. Astrophys Space Sci 366, 17 (2021). https://doi.org/10.1007/s10509-021-03923-9

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Keywords

  • Open cluster
  • Multi-scale
  • Kernel density estimation
  • Machine learning