Modeling Mid-Ultraviolet Spectra. I. Temperatures of Metal-Poor Stars

Astronomy and Astrophysics – Astrophysics

Scientific paper

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Proof revision -Minor changes to revised version submitted to Astrophysical Journal May 1, 2001. 29 pages, 4 figures (Fig. 3 w

Scientific paper

10.1086/322294

Determining the properties of old stellar systems using evolutionary population synthesis requires a library of reliable model stellar fluxes. Empirical libraries are limited to spectra of stars in the solar neighborhood, with nearly solar abundances and abundance ratios. We report here a first step towards providing a flux library that includes nonsolar abundances, based on calculations from first principles that are calibrated empirically. We have started with main-sequence stars, whose light dominates the mid-ultraviolet spectrum of an old stellar system. We have calculated mid-ultraviolet spectra for the Sun and nine nearby, near-main-sequence stars spanning metallicities from less than 1/100 solar to greater than solar, encompassing a range of light-element abundance enhancements. We first determined temperatures of eight of the stars by analyzing optical echelle spectra together with the mid-ultraviolet. Both could be matched at the same time only when models with no convective overshoot were adopted, and only when an approximate chromosphere was incorporated near the surface of relatively metal-rich models. Extensive modifications to mid-UV line parameters were also required, notably the manual assignment of approximate identifications for mid-UV lines missing from laboratory linelists. Without recourse to additional missing opacity, these measures suffice to reproduce in detail almost the entire mid-UV spectrum of solar-temperature stars up to one-tenth solar metallicity, and the region from 2900A to 3100A throughout the entire metallicity range. Ramifications for abundance determinations in individual metal-poor stars and for age-metallicity determinations of old stellar systems are briefly discussed, with emphasis on the predictive power of the calculations.

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