Аннотация
The implementation of star formation and stellar feedback in cosmological
simulations plays a critical role in shaping the properties of model galaxies.
In the first paper of the series, we presented a new method to model star
formation as a collection of star clusters. Here, we improve the algorithm by
eliminating accretion gaps, increasing spatial resolution, and boosting
momentum feedback. We also introduce the sub-grid initial bound fraction,
$f_i$, that distinguishes cluster mass from stellar particle mass. We perform
simulations with different star formation efficiency per free-fall time
$\epsilon_ff$ and intensity of supernova momentum feedback $f_\rm
boost$. We find that the star formation history of a Milky Way-sized galaxy is
sensitive to $f_boost$, which allows us to constrain its value, $f_\rm
boost\approx5$. On the other hand, changing $\epsilon_ff$ from a few
percent to 200% has little effect on the global galaxy properties. However, on
smaller scales, the properties of star clusters are very sensitive to
$\epsilon_ff$. We find that $f_i$ increases with $\epsilon_ff$ and
cluster mass. Through the dependence on $f_i$, the shape of the cluster initial
mass function also varies strongly with $\epsilon_ff$. The galactic
environment controls the formation of massive clusters: the fraction of
clustered star formation and maximum cluster mass increase with the star
formation rate surface density, with the normalization of both relations
dependent on $\epsilon_ff$. The cluster formation timescale
systematically decreases with increasing $\epsilon_ff$. Local variations
in the gas accretion history lead to a one-sigma scatter of 0.25 dex for the
integral efficiency of cluster formation. Joint constraints from all the
observables prefer the runs that produce the median integral efficiency of 16%.
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