Mass and metallicity requirement in stellar models for galactic chemical evolution applications

Abstract

We used a one-zone chemical evolution model to address the question of how many masses and metallicities are required in grids of massive stellar models in order to ensure reliable galactic chemical evolution predictions. We used a set of yields that includes seven masses between 13 and 30 M , 15 metallicities between 0 and 0.03 in mass fraction, and two different remnant mass prescriptions. We ran several simulations where we sampled subsets of stellar models to explore the impact of different grid resolutions. Stellar yields from low-and intermediate-mass stars and from Type Ia supernovae have been included in our simulations, but with a fixed grid resolution. We compared our results with the stellar abundances observed in the Milky Way for O, Na, Mg, Si, Ca, Ti, and Mn. Our results suggest that the range of metallicity considered is more important than the number of metallicities within that range, which only affects our numerical predictions by about 0.1 dex. We found that our predictions at [Fe/H] −2 are very sensitive to the metallicity range and the mass sampling used for the lowest metallicity included in the set of yields. Variations between results can be as high as 0.8 dex. At higher [Fe/H], we found that the required number of masses depends on the element of interest and on the remnant mass prescription. With a monotonic remnant mass prescription where every model explodes as a core-collapse supernova, the mass resolution induces variations of 0.2 dex on average. But with a remnant mass prescription that includes islands of non-explodability, the mass resolution can cause variations of about 0.2 to 0.7 dex depending on the choice of the lower limit of the metallicity range. With such a remnant mass prescription, explosive or non-explosive models can be missed if not enough masses are selected, resulting in over-or under-estimations of the mass ejected by massive stars.

The Impact of Modeling Assumptions in Galactic Chemical Evolution Models

“…The shape of numerical predictions highly depends on stellar yields (e.g., Romano et al 2010;Mollá et al 2015). Features such as the bumps seen in Figure 3 could completely disappear if different stellar models were selected in our set of yields (see Côté et al 2016b). …”

Neutron star mergers as sites of r-process nucleosynthesis and short gamma-ray bursts

“…We already noted that the large fluctuations in [Eu/Fe] are consistent with rare events and the large amounts of heavy r-process material observed in some low metallicity stars put a lower limit on the yield of each event (Macias & Ramirez-Ruiz, 2016). Depending on the origin of these low metallicity halo stars, this may require a short minimal time delay between the star formation and the first merger -an issue to which return later (Argast et al, 2004;Matteucci et al, 2014;Hirai et al, 2015;Shen et al, 2015;van de Voort et al, 2015;Ishimaru et al, 2015;Wehmeyer et al, 2015;Cescutti et al, 2015;Côté et al, 2016). Here, we focus on the late time chemical evolution of [Eu/Fe] of the Milky Way and consider that the abundances of europium and α-elements trace the r-process production and cc-SNe history, respectively.…”

“…The ratio of r-process elements to Fe, [Eu/Fe], declines for [Fe/H]> −1, where [X/Y] = log 10 (N X /N Y ) − log 10 (N X /N Y ) , N X is the abundance of an element X, and refers to the solar value. It has been questioned whether such a behavior is consistent with the expected merger history in the Milky Way (Côté et al, 2016;Komiya & Shigeyama, 2016).…”

Chempy: A flexible chemical evolution model for abundance fitting

“…Many of them are poorly constrained a priori (Côté et al 2016a), yet need to be marginalized out for simple astrophysical inferences. Perhaps the most important external or theoretical input for GCE models are nucleosynthetic yields for the various enrichment channels (Romano et al 2010;Côté et al 2016b). In the past, theoretical yields have produced mismatches with the observations, leading to the concept of "empirical yields" (François et al 2004;Henry et al 2010).…”