Empirical tests of evolutionary synthesis models

Details Empirical tests of evolutionary synthesis models Empirical tests of evolutionary synthesis models

Apr 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010iaus..262..337g&link_type=abstract

Stellar Populations – Planning for the Next Decade, Proceedings of the International Astronomical Union, IAU Symposium, Volume 2

Statistics

Applications

Galaxies: Evolution, Galaxies: Synthesis Models, Galaxies: Stellar Content

Scientific paper

Spectral synthesis of stellar populations has proven to be one of the most powerful methods to decompose the different mixtures of stellar contributions in galaxies, and applications of this technique routinely appear in the literature nowadays. Our group, for instance, the SEAGal (Semi Empirical Analysis of Galaxies) collaboration, has derived the star formation history of all galaxies in the SDSS with the starlight code, obtaining various results of astrophysical interest. As any other fossil method, the results rely heavily on high spectral resolution evolutionary synthesis models. To test this model dependence we run starlight on samples of star-forming and passive galaxies from the SDSS using different sets of models.
We explore models using “Padova 1994” and modified “Padova” evolutionary tracks with a different receipt for the asymptotic giant branch phase, as well as different stellar libraries (STELIB versus MILES+Granada). We then compare derived properties such as mean age, mean metallicity, extinction, star-formation and chemical histories. Despite a broad brush agreement, systematic differences emerge from this comparison. The different evolutionary tracks used lead to essentially the same results, at least insofar as optical spectra are concerned. Different stellar libraries, on the other hand, have a much bigger impact. The newer models produce quantifiably better fits and eliminate some pathologies (like suspicious combinations of base elements, systematical spectral residuals in some windows, and, sometimes, negative extinction) of fits derived with STELIB-based models, but there are still some caveats. These empirical tests provide useful feedback for model makers.

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