Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN [HEAP]

http://arxiv.org/abs/2211.11774


Population-synthesis codes are an unique tool to explore the parameter space of massive binary star evolution and binary compact object (BCO) formation. Most population-synthesis codes are based on the same stellar evolution model, limiting our ability to explore the main uncertainties. Our code SEVN overcomes this issue by interpolating the main stellar properties from a set of pre-computed evolutionary tracks. With SEVN, we evolved $1.2\times10^9$ binaries in the metallicity range $0.0001\leq Z \leq 0.03$, exploring a number of models for electron-capture, core-collapse and pair-instability supernovae, different assumptions for common envelope, stability of mass transfer, quasi-homogeneous evolution and stellar tides. We find that stellar evolution has a dramatic impact on the formation of single and binary compact objects. Just by slightly changing the overshooting parameter ($\lambda_{\rm ov}=0.4,0.5$) and the pair-instability model, the maximum mass of a black hole can vary from $\approx{60}$ to $\approx{100}\ \mathrm{M}_\odot$. Furthermore, the formation channels of BCOs and the merger efficiency we obtain with SEVN show significant differences with respect to the results of other population-synthesis codes, even when the same binary-evolution parameters are used. For example, the main traditional formation channel of BCOs is strongly suppressed in our models: at high metallicity ($Z\gtrsim{0.01}$) only $<20$% of the merging binary black holes and binary neutron stars form via this channel, while other authors found fractions $>70$%. The local BCO merger rate density of our fiducial models is consistent with the most recent estimates by the LIGO–Virgo–KAGRA collaboration.

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G. Iorio, G. Costa, M. Mapelli, et. al.
Wed, 23 Nov 22
36/71

Comments: Submitted to MNRAS, comments welcome! The SEVN code is available at this https URL All the data underlying this article are available in Zenodo at the link this https URL All the Jupyter notebooks used to produce the plots in the paper are available in the gitlab repository this https URL