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Membership of stars in open clusters using random forest with gaia data

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Abstract

Membership of stars in open clusters is one of the most crucial parameters in studies of star clusters. Gaia opened a new window in the estimation of membership because of its unprecedented 6-D data. In the present study, we used published membership data of nine open star clusters as a training set to find new members from Gaia DR2 data using a supervised random forest model with a precision of around 90%. The number of new members found is often double the published number. Membership probability of a larger sample of stars in clusters is a major benefit in determination of cluster parameters like distance, extinction and mass functions. We also found members in the outer regions of the cluster and found sub-structures in the clusters studied. The color magnitude diagrams are more populated and enriched by the addition of new members making their study more promising.

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Data Availability Statement

This manuscript has no associated data or the data will not be deposited. [Authors’ comment: Requests for access to membership data should be made to http://priya.hasan@gmail.com.]

Notes

  1. Right Ascension (RA) is denoted by \(\alpha \) and declination (Dec) by \(\delta \). Proper motion in RA is pmra (\(\mu _{\alpha }\)) and proper motion in declination is pmdec (\(\mu _{\delta }\)) and both are in units of milli-arcseconds per year (mas/yr). Parallax is denoted by \(\omega \) and has units of (mas).

  2. The combined membership data for all the nine clusters can be requested from PH.

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Acknowledgements

This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC,https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Virtual observatory tools like Aladin, Vizier and TOPCAT have been used in the analysis. Astropy, Scikit Learn, Seaborn, Numpy and Pandas packages in python have been used in the analysis and visualizations. MM is very thankful to Dr. Rohan Sekhar, Associate Professor of Computational Sciences in Minerva Schools at KGI for providing necessary support and insights on the RF model.

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Authors and Affiliations

  1. Minerva Schools at KGI, San Francisco, CA, 94103, USA

    Md Mahmudunnobe

  2. Maulana Azad National Urdu University, Gachibowli, Hyderabad, 500 032, India

    Priya Hasan, Mudasir Raja & S. N. Hasan

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  1. Md Mahmudunnobe
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  2. Priya Hasan
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  4. S. N. Hasan
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Correspondence to Priya Hasan.

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Mahmudunnobe, M., Hasan, P., Raja, M. et al. Membership of stars in open clusters using random forest with gaia data. Eur. Phys. J. Spec. Top. 230, 2177–2191 (2021). https://doi.org/10.1140/epjs/s11734-021-00205-x

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