The SRG/eROSITA all-sky survey: The morphologies of clusters of galaxies

X-ray-selected surveys of clusters of galaxies have been reported to contain more regular cool core clusters compared to samples selected using the Sunyaev-Zel’dovich (SZ) effect. Morphology population studies on X-ray-selected clusters will be biased without taking into account selection, as cool cores are more easily detected at low redshifts, but can be mistaken for point sources at high redshifts. eROSITA, aboard Spectrum Roentgen Gamma (SRG), found over 12 000 optically identified clusters of galaxies in its first survey, eRASS1. Taking account of the selection function obtained from simulations, using a Bayesian framework we obtained the intrinsic distribution of morphological parameters, including the concentration, central density, cuspiness, ellipticity, and slosh. We constructed scaling relations for the parameters as a function of redshift and luminosity, and studied their distribution within redshift or luminosity bins. We find that the concentration in a scaled aperture evolves positively with luminosity, similarly to the central scaled density, and negatively with redshift. When using a fixed aperture, its evolution with luminosity is lower, but also dependent on the choice of cluster centre. The mean cluster ellipticity does not significantly evolve with redshift or luminosity. eRASS1 clusters show indications of higher concentrations compared to SZ-selected objects, even after taking the X-ray selection into account. This suggests that if our selection function model is correct, SZ-selected clusters may also suffer from morphological selection effects. We compared different models for the parameter distribution in bins of redshift and luminosity. The distribution of concentration and ellipticity is generally consistent with a normal one, but other parameters such as the central density and cuspiness strongly favour more complex distributions. However, modelling of all clusters as a single population generally shows a preference for non-normal distributions.