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

Abstract

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. Here, we present the new version of the code SEVN, which overcomes this issue by interpolating the main stellar properties from a set of pre-computed evolutionary tracks. We describe the new interpolation and adaptive time-step algorithms of SEVN, and the main upgrades on single and binary evolution. With SEVN, we evolved $1.2\times 10^9$ binaries in the metallicity range $0.0001\le Z\le 0.03$, 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 (${\lambda }_{\mathrm{ov}}=0.4,0.5$) 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.

Accepted for publication in MNRAS. The SEVN code is available at https://gitlab.com/sevncodes/sevn.git. All the data underlying this article are available in Zenodo at the link https://doi.org/10.5281/zenodo.7794546. All the Jupyter notebooks used to produce the plots in the paper are available in the gitlab repository https://gitlab.com/iogiul/iorio22_plot.git

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