Poster Open Access

The metallicity of M dwarfs: photometric calibrations with Markov Chain Monte Carlo and Bayesian inference

Duque-Arribas, Christian; Montes, David; Tabernero, Hugo M.; Marfil, Emilio; Caballero, Jose Antonio


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  <dc:contributor>Wolk, Scott</dc:contributor>
  <dc:creator>Duque-Arribas, Christian</dc:creator>
  <dc:creator>Montes, David</dc:creator>
  <dc:creator>Tabernero, Hugo M.</dc:creator>
  <dc:creator>Marfil, Emilio</dc:creator>
  <dc:creator>Caballero, Jose Antonio</dc:creator>
  <dc:date>2021-02-25</dc:date>
  <dc:description>In our on-going effort to characterize M dwarfs, we have carried out multi-band photometry calibrations of metallicity for early and intermediate M dwarfs using Gaia EDR3, 2MASS and AllWISE. These catalogs, combined with a sample of 5435 M dwarfs with additional parameters determined by APOGEE high-resolution spectroscopy, allow us to study the effect of the chemical composition in color-color and color-magnitude diagrams. We train the calibrations using Bayesian statistics and Markov Chain Monte Carlo (MCMC) techniques and derive several photometric calibrations aplicable to M dwarfs with metallicities of -0.4 ≤ [Fe/H] ≤ +0.4, obtaining estimations reliable to 0.10 dex. Lastly, we compare our results with previous photometric studies of metallicity for an additional sample of FGK+M common-proper-motion systems, finding a great predictive performance.</dc:description>
  <dc:identifier>https://zenodo.org/record/4562444</dc:identifier>
  <dc:identifier>10.5281/zenodo.4562444</dc:identifier>
  <dc:identifier>oai:zenodo.org:4562444</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.4562443</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/coolstars20half</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Cool Stars on the main sequence</dc:subject>
  <dc:title>The metallicity of M dwarfs: photometric calibrations with Markov Chain Monte Carlo and Bayesian inference</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePoster</dc:type>
  <dc:type>poster</dc:type>
</oai_dc:dc>
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