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Published April 1, 2024 | Published
Journal Article Open

The POKEMON Speckle Survey of Nearby M Dwarfs. III. The Stellar Multiplicity Rate of M Dwarfs within 15 pc

  • 1. ROR icon Jet Propulsion Lab
  • 2. ROR icon NASA Exoplanet Science Institute
  • 3. ROR icon California Institute of Technology
  • 4. ROR icon Lowell Observatory
  • 5. ROR icon Southern Connecticut State University
  • 6. ROR icon Bridgewater State University
  • 7. ROR icon Harvard-Smithsonian Center for Astrophysics
  • 8. ROR icon NOIRLab
  • 9. ROR icon Gemini North Observatory

Abstract

M dwarfs are ubiquitous in our Galaxy, and the rate at which they host stellar companions, and the properties of these companions, provide a window into the formation and evolution of the star(s), and of any planets that they may host. The Pervasive Overview of "Kompanions" of Every M dwarf in Our Neighborhood (POKEMON) speckle survey of nearby M dwarfs is volume limited from M0V through M9V out to 15 pc, with additional targets at larger distances. In total, 1125 stars were observed, and 455 of these are within the volume-limited, 15 pc sample of M-dwarf primaries. When we combine the speckle observations with known companions from the literature, we find that the stellar multiplicity rate of M dwarfs within 15 pc is 23.5% ± 2.0%, and that the companion rate is 28.8% ± 2.1%. We also find that the projected separation distribution for multiples that are known to host planets peaks at 198 au, while the distribution for multiples that are not yet known to host planets peaks at 5.57 au. This result suggests that the presence of close-in stellar companions inhibits the formation of M-dwarf planetary systems, similar to what has been found for FGK stars.

Copyright and License

© 2023. California Institute of Technology. Government sponsorship acknowledged.

Acknowledgement

We are so grateful to the multitude of folks who have contributed to the POKEMON survey, and in particular to Brian Mason and Frederick Hahne. We also thank the anonymous reviewer, Davy Kirkpatrick, and Eric Mamajek for their contributions to this manuscript.

This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). This research was supported by NSF grant No. AST-1616084 awarded to G.T.v.B. and NASA grant 18-2XRP18_2-0007 awarded to D.R.C.

This work presents results from the European Space Agency (ESA) space mission Gaia. Gaia data are being processed by the Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www.cosmos.esa.int/gaia. The Gaia archive website is https://archives.esac.esa.int/gaia.

This work has used data products from the Two Micron All Sky Survey (Skrutskie et al. 2019), which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center at the California Institute of Technology, funded by NASA and NSF.

This research has made use of the Exoplanet Follow-up Observation Program (ExoFOP, doi:10.26134/ExoFOP5) website, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program.

This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program.

Information was collected from several additional large database efforts: the Simbad database and the VizieR catalog access tool, operated at CDS, Strasbourg, France; NASA's Astrophysics Data System; and the Washington Double Star Catalog maintained at the US Naval Observatory.

Software References

Software: Astropy (Astropy Collaboration et al. 2013), Astroquery (Ginsburg et al. 2019), IPython (Pérez & Granger 2007), Matplotlib (Hunter 2007), NumPy (Harris et al. 2020), Pandas (McKinney et al. 2010), and SciPy (Virtanen et al. 2020).

Facilities

Exoplanet Archive

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Additional details

Created:
September 17, 2024
Modified:
September 17, 2024