Detecting Galaxy Groups and AGNs populating the local Universe in the eROSITA era

Abstract

The eROSITA will deliver an unprecedented volume of X-ray survey observations, 20-30 times more sensitive than ROSAT in the soft band (0.5-2 keV) and for the first time imaging in the hard band (2-10 keV) including galaxy clusters and groups along with obscured and unobscured AGNs. This calls for a powerful theoretical effort to control the systematics and biases that may affect the data analysis. We investigate the detection technique and selection effects in the galaxy group and AGN populations of a mock eROSITA survey at the depth of eRASS:4. We create a $30\times 30$ deg${}^2$ mock observation based on the cosmological hydrodynamical simulation Magneticum Pathfinder within z=0-0.2. We combine a physical background extracted from the real eFEDS background analysis with realistic simulations of X-ray emission for the hot gas, AGNs and X-ray binaries. We apply a detection procedure equivalent to the reduction done on eRASS data and evaluate the completeness and contamination to reconstruct the luminosity functions of the extended and point sources in the catalogue. We assess the completeness of extended detections as a function of the input X-ray flux and halo. We achieve full recovery of the brightest (most massive) clusters and AGNs. However, a significant fraction of galaxy groups remains undetected. Examining the gas properties between the detected and undetected galaxy groups at fixed halo mass, we observe that the detected population exhibits, on average, higher X-ray brightness compared to the undetected ones. Moreover, we find that X-ray luminosity primarily correlates with the hot gas fraction, rather than temperature or metallicity. Our simulation suggests the presence of a systematic selection effect in current surveys, resulting in X-ray survey catalogues predominantly composed of the lowest-entropy, gas-richest, and highest surface brightness halos on galaxy group scales.

18 pages, 14 figures, submitted to A&A

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