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tokens: ( Schneider et al. 2017 ; Mapelli et al. 2017 ). Interestingly, Mapelli et al. (2017) predict that the distribution of metallicity of BH–BH merger progenitors peaks at around ∼ 0.1 Z ⊙ and declines towards lower Z , which conflicts with our Fig. 8 . The most likely explanation of this discrepancy is that we are overestimating the contribution from very low metallicities at close redshifts of star formation. The majority of the low-metallicity content of the recent Universe is locked in dwarf galaxies with little star formation, for which our simplification that the mean metallicity of the Universe ≈ mean metallicity of star formation is likely less accurate. This interpretation also agrees with the fact that Mapelli et al. (2017) did not find the clearly bimodal distribution of massive BH–BH merger birth redshifts as shown in the case of our simulations in Fig. 2 of B16, but rather only the single maximum around the peak of cosmic star formation at z ≈ 2. However, even if we were overpredicting the star formation in low metallicities Z 0.1 Z ⊙ at redshifts z 2 to the point at which we would have to discard all our mergers formed in these conditions, this would still not affect our predictions for the merger rates by more than a factor of 2. Although making predictions for CBM based on cosmological simulations as carried out by Schneider et al. (2017) or Mapelli et al. (2017 ; or, in a simplified way, also by Belczynski et al. 2018) may be burdened with additional biases, this ultimately seems to be the superior approach that hopefully will allow us to confront models with various galactic properties in the case of mergers with identified hosts (such as GW170817, Abbott et al. 2017d ). Finally, we wish to highlight that the small impact of IMF variations on merger rates (Sect. 6.2 ) is a general result that should also appear in the predictions based on cosmological simulations. Even though such simulations often provide SFR values as their output, changing the assumptions on the massive-star IMF without renormalizing the SFR make their results inconsistent with star formation tracers such as UV observations. 6.4. Comparison with LIGO/Virgo detection rates and other studies We show the merger (Gpc −3 yr −1 ) and detection rates (yr −1 ) from our simulations in Table A.1 . While our models perform relatively well in retrieving the BH–BH merger rate as constrained by the LIGO/Virgo observations R BHBH = 12–213 Gpc −3 yr −1 ( Abbott et al. 2017a ), they underpredict the rate of NS–NS mergers that was inferred after the recent detection of GW170817: ( Abbott et al. 2017d ) 6 . Recently, Chruslinska et al. (2018) analysed NS–NS merger rates within a large suite of S TAR T RACK models, varying multiple binary evolution assumptions, and found that it is, in fact, very difficult to obtain R NSNS consistent with the LIGO/Virgo constraints. The only three models marginally consistent with the reported NS–NS merger rates, which have R NSNS values of