Cluster membership analysis with supervised learning and N-body simulations

¹ Department of Physics, Xi’an Jiaotong-Liverpool University, 111 Ren’ai Road, Dushu Lake Science and Education Innovation District, Suzhou 215123, Jiangsu Province, PR China
² Energetic Cosmos Laboratory, Nazarbayev University, 53 Kabanbay Batyr Ave., 010000 Astana, Kazakhstan
³ Heriot-Watt University Aktobe Campus, 263 Zhubanov Brothers Str, 030000 Aktobe, Kazakhstan
⁴ Heriot-Watt International Faculty, K. Zhubanov Aktobe Regional University, 263 Zhubanov Brothers Str, 030000 Aktobe, Kazakhstan
⁵ Fesenkov Astrophysical Institute, 23 Observatory Str., 050020 Almaty, Kazakhstan
⁶ Faculty of Physics and Technology, Al-Farabi Kazakh National University, 71 Al-Farabi Ave, 050020 Almaty, Kazakhstan
⁷ Department of Physics, School of Sciences and Humanities, Nazarbayev University, 53 Kabanbay Batyr Ave., 010000 Astana, Kazakhstan
⁸ Shanghai Key Laboratory for Astrophysics, Shanghai Normal University, 100 Guilin Road, Shanghai 200234, PR China
⁹ Nicolaus Copernicus Astronomical Centre Polish Academy of Sciences, ul. Bartycka 18, 00-716 Warsaw, Poland
¹⁰ Konkoly Observatory, HUN-REN Research Centre for Astronomy and Earth Sciences, Konkoly Thege Miklós út 15–17, 1121 Budapest, Hungary
¹¹ Main Astronomical Observatory, National Academy of Sciences of Ukraine, 27 Akademika Zabolotnoho St., 03143 Kyiv, Ukraine

Received: 29 February 2024
Accepted: 27 July 2024

Abstract

Context. Membership analysis is an important tool for studying star clusters. There are various approaches to membership determination, including supervised and unsupervised machine-learning (ML) methods.

Aims. We perform membership analysis using the supervised ML approach.

Methods. We trained and tested our ML models on two sets of star cluster data: snapshots from N-body simulations, and 21 different clusters from the Gaia Data Release 3 data.

Results. We explored five different ML models: random forest (RF), decision trees, support vector machines, feed-forward neural networks, and K-nearest neighbors. We find that all models produce similar results, and the accuracy of RF is slightly better. We find that a balance of classes in the datasets is optional for a successful learning. The classification accuracy strongly depends on the astrometric parameters. The addition of photometric parameters does not improve the performance. We find no strong correlation between the classification accuracy and the cluster age, mass, and half-mass radius. At the same time, models trained on clusters with a larger number of members generally produce better results.