Expanding the Catalog: Considering the Importance of Carbon, Magnesium, and Neon in the Evolution of Stars and Habitable Zones

Abstract

Building on previous work, we have expanded our catalog of evolutionary models for stars with variable composition; here we present models for stars of mass 0.5 - 1.2 Msol, at scaled metallicities of 0.1 - 1.5 Zsol, and specific C/Fe, Mg/Fe, and Ne/Fe values of 0.58 - 1.72 C/Fe_sol, 0.54 - 1.84 Mg/Fe_sol and 0.5 - 2.0 Ne/Fe_sol, respectively. We include a spread in abundance values for carbon and magnesium based on observations of their variability in nearby stars; we choose an arbitrary spread in neon abundance values commensurate with the range seen in other low Z elements due to the difficult nature of obtaining precise measurements of neon abundances in stars. As indicated by the results of Truitt et al. (2015), it is essential that we understand how differences in individual elemental abundances, and not just the total scaled metallicity, can measurably impact a star's evolutionary lifetime and other physical characteristics. In that work we found that oxygen abundances significantly impacted the stellar evolution; carbon, magnesium, and neon are potentially important elements to individually consider due to their relatively high (but also variable) abundances in stars. We present 528 new stellar main sequence models, and we calculate the time-dependent evolution of the associated habitable zone boundaries for each based on mass, temperature, and luminosity. We also reintroduce the 2 Gyr "Continuously Habitable Zone" as a useful tool to help gauge the habitability potential for a given planetary system.

December 2016, Accepted for publication in the Astrophysical Journal. 32 pages, 8 Figures, 8 Tables. arXiv admin note: text overlap with arXiv:1504.02145 -- resubmitted to arXiv on Dec. 17, 2016 to correct small error in References

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Cited by in corpus (2)

• A Flexible Bayesian Framework for Assessing Habitability with Joint Observational and Model Constraints
• Continuous Habitable Zones: Using Bayesian Methods to Prioritize Characterization of Potentially Habitable Worlds