Speaker
Description
Constraining cosmology with galaxy clusters requires knowledge of sample incompleteness. We used a large set of mock observations of the eRASS1 survey and we processed simulated data identically to the real eRASS1 events. From there, we trained a series of models to build selection functions suited to cosmological analyses. Our results reveal the surface brightness characteristics that appear most relevant to cluster selection in the eRASS1 sample, in particular the ambiguous role of central surface brightness at the scale of the instrument resolution. We demonstrate that our selection function for bright sources well reproduces the catalogue matches with external datasets (e.g. eFEDS in X-rays, and SPT via the S-Z effect) and successfully passes internal validation tests, thereby enabling its use in the first eRASS1 cosmological analysis and in sample studies of eRASS1 cluster and groups.
