Galactic r-process enrichment by neutron star mergers in cosmological simulations of a Milky Way-mass galaxy
We quantify the stellar abundances of neutron-rich r-process nuclei in cosmological zoom-in simulations of a Milky Way-mass galaxy from the Feedback In Realistic Environments project. The galaxy is enriched with r-process elements by binary neutron star (NS) mergers and with iron and other metals by supernovae. These calculations include key hydrodynamic mixing processes not present in standard semi-analytic chemical evolution models, such as galactic winds and hydrodynamic flows associated with structure formation. We explore a range of models for the rate and delay time of NS mergers, intended to roughly bracket the wide range of models consistent with current observational constraints. We show that NS mergers can produce [r-process/Fe] abundance ratios and scatter that appear reasonably consistent with observational constraints. At low metallicity, [Fe/H] ≾ −2, we predict there is a wide range of stellar r-process abundance ratios, with both supersolar and subsolar abundances. Low metallicity stars or stars that are outliers in their r-process abundance ratios are, on average, formed at high redshift and located at large galactocentric radius. Because NS mergers are rare, our results are not fully converged with respect to resolution, particularly at low metallicity. However, the uncertain rate and delay time distribution of NS mergers introduce an uncertainty in the r-process abundances comparable to that due to finite numerical resolution. Overall, our results are consistent with NS mergers being the source of most of the r-process nuclei in the Universe.
© 2014 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. Accepted 2014 November 12. Received 2014 September 29; in original form 2014 July 25. First published online December 12, 2014. We would like to thank Dan Kasen, Patrick Fitzpatrick, and Brian Metzger for useful conversations and the anonymous referee for helpful comments. This work was supported in part by NASA grant NNX10AJ96G, NSF grant AST-1206097, the David and Lucile Packard Foundation, and by a Simons Investigator Award from the Simons Foundation to EQ. DK is supported in part by Hellman Fellowship at UCSD and NSF grant AST-1412153. CAFG is supported by NASA through Einstein Postdoctoral Fellowship Award number PF3-140106 and by NSF through grant number AST-1412836. The simulations here used computational resources granted by the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number OCI-1053575, specifically allocations TG-AST120025 (PI Keres), TG-AST130039 (PI Hopkins).
Submitted - 1407.7039v2.pdf
Published - MNRAS-2015-van_de_Voort-140-8.pdf