SIMBAD references

Aims. We studied the star formation rate (SFR) in cosmological hydrodynamical simulations of galaxy (proto-)clusters in the redshift range 0<z<4, comparing them to recent observational studies; we also investigated the effect of varying the parameters of the star formation model on galaxy properties such as SFR, star-formation efficiency, and gas fraction.
Methods. We analyse a set of zoom-in cosmological hydrodynamical simulations centred on 12 clusters. The simulations are carried out with the GADGET-3 Tree-PM smoothed-particle hydro-dynamics code which includes various subgrid models to treat unresolved baryonic physics, including AGN feedback.
Results. Simulations do not reproduce the high values of SFR observed within protocluster cores, where the values of SFR are underpredicted by a factor ≥4 both at z∼2 and z∼4. The difference arises as simulations are unable to reproduce the observed starburst population and is greater at z∼2 because simulations underpredict the normalisation of the main sequence (MS) of star forming galaxies (i.e. the correlation between stellar mass and SFR) by a factor of ∼3. As the low normalisation of the MS seems to be driven by an underestimated gas fraction, it remains unclear whether numerical simulations miss starburst galaxies due to overly underpredicted gas fractions or overly low star formation efficiencies. Our results are stable against varying several parameters of the star formation subgrid model and do not depend on the details of AGN feedback.
Conclusions. The subgrid model for star formation, introduced to reproduce the self-regulated evolution of quiescent galaxies, is not suitable to describe violent events like high-redshift starbursts. We find that this conclusion holds, independently of the parameter choice for the star formation and AGN models. The increasing number of multi-wavelength high-redshift observations will help to improve the current star formation model, which is needed to fully recover the observed star formation history of galaxy clusters.