Gravitational wave distribution in Milky Way-like galaxies of the Auriga magneto-hydrodynamical simulation suite

In this thesis, we perform a subset of Auriga’s simulations by including the module ArepoGW in on-the-fly mode, which produces a catalog of the gravitational wave (GW) events generated by the merging of binary stellar compact objects. The Auriga Project is a suite of magneto-hydrodynamical zoom-in simulations of Milky Way-like galaxies, based on the advanced hydrodynamic moving mesh code Arepo, which is expanded by employing a full physics galaxy formation model. Our aim is to predict the distribution of compact binary mergers of some of Auriga’s galaxies, selected to be representative of the different star formation histories (SFHs) and stellar properties of the Auriga sample. We investigate whether systematic differences in this distribution emerge between different types of progenitors, and whether these correlate with the host galaxy’s evolutionary history, by studying dependence of the merger rate of each channel on time, on progenitors’ metallicity and on the projected radial distance in the plane of the disk. A full color composite photometric Mollweide projection of the present-time stellar emission is reported for each simulated galaxy, and the resulting GW distribution is superimposed. Among the most relevant results, we find that for all the studied galaxies, except one, it is possible to convert the star formation rate into the total binary merger rate by means of a factor 2-3 × 10^−5 M⊙^−1, which is independent of the particular galaxy and approximately constant in time for most of the galaxy history. Furthermore, we see that the probability of detecting a GW signal of the analyzed types of progenitors from within our galaxy during human-scale observational campaigns is extremely low, with an estimated rate of 1 event every 10000 years in the best cases. Future developments, including higher-resolution simulations and improved modelling of galaxy formation physics or binary evolution, may further enhance the predictive power of this approach.