The challenge of simultaneously matching the observed diversity of chemical abundance patterns in cosmological hydrodynamical simulations
With the advent of large spectroscopic surveys the amount of high quality chemo-dynamical data in the Milky Way (MW) increased tremendously. Accurately and correctly capturing and explaining the detailed features in the high-quality observational data is notoriously difficult for state-of-the-art numerical models. In order to keep up with the quantity and quality of observational datasets, improved prescriptions for galactic chemical evolution need to be incorporated into the simulations. Here we present a new, flexible, time resolved chemical enrichment model for cosmological simulations. Our model allows to easily change a number of stellar physics parameters such as the shape of the initial mass function (IMF), stellar lifetimes, chemical yields or SN Ia delay times. We implement our model into the Gasoline2 code and perform a series of cosmological simulations varying a number of key parameters, foremost evaluating different stellar yield sets for massive stars from the literature. We find that total metallicity, total iron abundance and gas phase oxygen abundance are robust predictions from different yield sets and in agreement with observational relations. On the other hand, individual element abundances, especially $\alpha$-elements show significant differences across different yield sets and none of our models can simultaneously match constraints on the dwarf and MW mass scale. This offers a unique way of observationally constraining model parameters. For MW mass galaxies we find for most yield tables tested in this work a bimodality in the $[\alpha$/Fe] vs. [Fe/H] plane of rather low intrinsic scatter potentially in tension with the observed abundance scatter.
- Pub Date:
- March 2021
- Astrophysics - Astrophysics of Galaxies;
- Astrophysics - Cosmology and Nongalactic Astrophysics;
- Astrophysics - Solar and Stellar Astrophysics
- main text 19 pages and 11 figures, 4 pages of appendix, 23 pages total, python code and data at https://github.com/TobiBu/chemical_enrichment.git. submitted to MNRAS, comments very welcome