{
  "id": "2103.05826",
  "version": "v1",
  "published": "2021-03-10T02:19:24.000Z",
  "updated": "2021-03-10T02:19:24.000Z",
  "title": "Membership of Stars in Open Clusters using Random Forest with Gaia Data",
  "authors": [
    "Md Mahmudunnobe",
    "Priya Hasan",
    "Mudasir Raja",
    "S N Hasan"
  ],
  "comment": "Accepted for publication in The European Physical Journal ST, Special Issue on Modeling Machine Learning and Astronomy",
  "categories": [
    "astro-ph.SR",
    "astro-ph.GA"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-03-10T02:19:24.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "open clusters",
      "gaia data",
      "color magnitude diagrams",
      "supervised random forest model",
      "gaia dr2 data"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}