r-process Nucleosynthesis from Three-dimensional Magnetorotational Core-collapse Supernovae
We investigate r-process nucleosynthesis in 3D general-relativistic magnetohydrodynamic simulations of rapidly rotating strongly magnetized core collapse. The simulations include a microphysical finite-temperature equation of state and a leakage scheme that captures the overall energetics and lepton number exchange due to postbounce neutrino emission and absorption. We track the composition of the ejected material using the nuclear reaction network SkyNet. Our results show that the 3D dynamics of magnetorotational core-collapse supernovae (CCSN) are important for their nucleosynthetic signature. We find that production of r-process material beyond the second peak is reduced by a factor of 100 when the magnetorotational jets produced by the rapidly rotating core undergo a kink instability. Our results indicate that 3D magnetorotationally powered CCSNe are robust r-process sources only if they are obtained by the collapse of cores with unrealistically large precollapse magnetic fields of the order of 10^(13) G. Additionally, a comparison simulation that we restrict to axisymmetry results in overly optimistic r-process production for lower magnetic field strengths.
Additional Information© 2018 The American Astronomical Society. Received 2017 December 30; revised 2018 July 24; accepted 2018 July 24; published 2018 September 13. The authors would like to thank D. Kasen, E. Quataert, and D. Radice for discussions. This research was partially supported by NSF grants AST-1212170, CAREER PHY-1151197, OAC-1550514, and OCI-0905046. P.M. acknowledges support by NASA through Einstein Fellowship grant PF5-160140. This work was enabled in part by the NSF under Grant No. PHY-1430152 (JINA Center for the Evolution of the Elements). The simulations were carried out on XSEDE resources under allocation TG-AST160049 and on NSF/NCSA BlueWaters under NSF award PRAC OCI-0941653. This paper has been assigned Yukawa Institute for Theoretical Physics report number YITP-17-129 and LANL Report number LA-UR-17-31278. Software: Einstein Toolkit (Löffler et al. 2012; Mösta et al. 2014a), SkyNet (Lippuner & Roberts 2017), REACLIB (Cyburt et al. 2010), Matplotlib (Hunter 2007).
Published - Mösta_2018_ApJ_864_171.pdf