Machine-learning investigation of the open cluster M67

DOI

In this paper, we use a machine-learning method, random forest (RF), to identify reliable members of the old (4Gyr) open cluster M67 based on the high-precision astrometry and photometry taken from the second Gaia data release (Gaia-DR2). The RF method is used to calculate membership probabilities of 71117 stars within 2.5{deg} of the cluster center in an 11-dimensional parameter space, the photometric data are also taken into account. Based on the RF membership probabilities, we obtain 1502 likely cluster members (>=0.6), 1361 of which are high-probability cluster members (>=0.8). Based on high-probability memberships with high-precision astrometric data, the mean parallax (distance) and proper-motion of the cluster are determined to be 1.1327+/-0.0018mas (883+/-1pc) and (,)=(-10.9378+/-0.0078,-2.9465 +/-0.0074)mas/yr, respectively. We find the cluster to have a mean radial velocity of +34.06+/-0.09km/s, using 74 high-probability cluster members with precise radial-velocity measures. We investigate the spatial structure of the cluster, the core and limiting radius are determined to be 4.80'+/-0.11' (~1.23+/-0.03pc) and 61.98'+/-1.50' (~15.92+/-0.39pc), respectively. Our results reveal that an escaped member with high membership probability (~0.91) is located at a distance of 77' (~20pc) from the cluster center. Furthermore, our results reveal that at least 26.4% of the main-sequence stars in M67 are binary stars. We confirm that significant mass segregation has taken place within M67.

Cone search capability for table J/ApJ/869/9/table3 (A catalog of fundamental information for 1502 likely cluster members with P_RF_>=0.6)

Cone search capability for table J/ApJ/869/9/table4 (*Fundamental parameters of the 45 likely escapers)

Identifier
DOI http://doi.org/10.26093/cds/vizier.18690009
Source https://dc.g-vo.org/rr/q/lp/custom/CDS.VizieR/J/ApJ/869/9
Related Identifier https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/869/9
Related Identifier http://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJ/869/9
Metadata Access http://dc.g-vo.org/rr/q/pmh/pubreg.xml?verb=GetRecord&metadataPrefix=oai_b2find&identifier=ivo://CDS.VizieR/J/ApJ/869/9
Provenance
Creator Gao X.
Publisher CDS
Publication Year 2019
Rights https://cds.unistra.fr/vizier-org/licences_vizier.html
OpenAccess true
Contact CDS support team <cds-question(at)unistra.fr>
Representation
Resource Type Dataset; AstroObjects
Discipline Astrophysics and Astronomy; Galactic and extragalactic Astronomy; Natural Sciences; Observational Astronomy; Physics; Stellar Astronomy