Stochastic chemical enrichment in metal-poor systems II. Abundance ratios and scatter

Astronomy and Astrophysics – Astrophysics

Scientific paper

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19 pages, 20 figures, accepted for publication in Astronomy & Astrophysics

Scientific paper

10.1051/0004-6361:20042168

A stochastic model of the chemical enrichment of metal-poor systems by core-collapse supernovae is used to study the scatter in stellar abundance ratios. The resulting scatter in abundance ratios, e.g. as functions of the overall metallicity, is demonstrated to be crucially dependent on the as yet uncertain supernovae yields. The observed abundance ratios and their scatters therefore have diagnostic power as regards the yields. The relatively small star-to-star scatter observed in many chemical abundance ratios, e.g. by Cayrel et al. (2004) for stars down to [Fe/H] = -4, is tentatively explained by the averaging of a large number of contributing supernovae and by the cosmic selection effects favoring contributions from supernovae in a certain mass range for the most metal-poor stars. The scatter in observed abundances of alpha-elements is understood in terms of observational errors only, while additional spread in yields or sites of nucleosynthesis may affect the odd-even elements Na and Al. For the iron-group elements we find systematically too high predicted Cr/Fe and Cr/Mg ratios, as well as differences between the different sets of yields, both in terms of predicted abundance ratios and scatter. The semi-empirical yields recently suggested by Francois et al. (2004) are found to lead to scatter in abundance ratios significantly greater than observed, when applied in the inhomogeneous models. "Spurs", very narrow sequences in abundance-ratio diagrams, may disclose a single-supernova origin of the elements of the stars on the sequence. Verification of the existence of such features, called single supernova sequences (SSSs), is challenging. This will require samples of several hundred stars with abundance ratios observed to accuracies of 0.05 dex or better.

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