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The SRG/eROSITA All-Sky Survey: Constraints on AGN Feedback in Galaxy Groups
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The SRG/eROSITA All-Sky Survey: Constraints on AGN Feedback in Galaxy Groups
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We investigate the impact of AGN feedback, on the entropy and characteristic temperature measurements of galaxy groups detected in the SRG/eROSITA's first All-Sky Survey (eRASS1) to shed light on the characteristics of the feedback mechanisms. We analyze deeper eROSITA observations of 1178 galaxy groups detected in eRASS1. We divide the sample into 271 subsamples and extract average thermodynamic properties, including electron density, temperature, and entropy at three characteristic radii along with the integrated temperature by jointly analyzing X-ray images and spectra following a Bayesian approach. We present the tightest constraints on the impact of AGN feedback through our average entropy and characteristic temperature measurements of the largest group sample used in X-ray studies, incorporating major systematics in our analysis. We find that entropy shows an increasing trend with temperature in the form of a power-law-like relation at the higher intra-group medium temperatures, while for the low mass groups, a slight flattening is observed on the average entropy. Overall, the observed entropy measurements agree well with the earlier measurements in the literature. The comparisons with the state-of-the-art cosmological hydrodynamic simulations (MillenniumTNG, Magneticum, OWL simulations) after the applications of the selection function calibrated for our galaxy groups reveal that observed entropy profiles in the cores are below the predictions of simulations. At the mid-region, the entropy measurements agree well with the Magneticum simulations, whereas the predictions of MillenniumTNG and OWL simulations fall below observations. At the outskirts, the overall agreement between the observations and simulations improves, with Magneticum simulations reproducing the observations the best. Our measurements will pave the way for more realistic AGN feedback implementations in simulations.
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