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February 5, 20260 citations

Inferring stellar compositions

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AKAndreas Korn

Key Points

  • This research aims to improve methods for inferring stellar surface compositions by addressing biases in current models.
  • Analyzed the limitations of 1D+LTE models in stellar composition inference.
  • Discussed challenges of modeling numerous stellar spectra from large surveys.
  • Investigated the effects of internal mixing on surface composition changes.
  • Identified significant biases associated with using classical hydrostatic assumptions.
  • Demonstrated the need for more physically realistic modeling approaches.
  • Highlighted the complexities in connecting surface abundances to overall stellar composition.

Abstract

Stars are not hydrostatic spheres and their atmospheric layers are not in local thermodynamic equilibrium. Inferring stellar surface abundances from such 1D+LTE models is subject to significant biases. We are now at a stage of maturity of quantitative stellar spectroscopy where these classical assumptions can be superseded by physically realistic modelling. However, doing so for hundreds of thousands or even millions of stellar spectra (as produced by the current and upcoming large spectroscopic surveys around the world) remains a challenge. The other challenge lies in the connection between the inferred surface abundances and the composition of the star as a whole. Stars evolve through different stages and internal mixing will alter the surface composition of specific, sometimes of all, elements. We know the effects at play, but cannot generally model them from first principles in the framework of quasi-hydrostatic stellar-evolution models. Inferring accurate birth-cloud compositions as input for chemical-evolution models thus remains challenging.

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Cite This Study

Andreas Korn (2025) studied this question.

synapsesocial.com/papers/69843451f1d9ada3c1fb258bhttps://doi.org/10.1051/epjconf/202533101004/pdf
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