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\xa0test 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\n .