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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., Comment: 18 pages, 14 figures, submitted to A&A

Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify stars in simulated galaxy clusters after subtracting the member galaxies. These dynamically different components are interpreted as the individual properties of the stars in the Brightest Cluster Galaxy (BCG) and IntraCluster Light (ICL). We employ matched stellar catalogues (built from the different dynamical properties of BCG and ICL) of 29 simulated clusters from the DIANOGA set to train and test the classifier. The input features are cluster mass, normalized particle cluster-centric distance, and rest-frame velocity. The model is found to correctly identify most of the stars, while the larger errors are exhibited at the BCG outskirt, where the differences between the physical properties of the two components are less obvious. We investigate the robustness of the classifier to numerical resolution, redshift dependence (up to $z=1$), and included astrophysical models. We claim that our classifier provides consistent results in simulations for $z<1$, at different resolution levels and with significantly different subgrid models. The phase-space structure is examined to assess whether the general properties of the stellar components are recovered: (i) the transition radius between BCG-dominated and ICL-dominated region is identified at $0.04$ \r200; (ii) the BCG outskirt ($> 0.1$ \r200) is significantly affected by uncertainties in the classification process. In conclusion, this work suggests the importance of employing Machine Learning to speed up a computationally expensive classification in simulations., Comment: 19 pages, accepted to MNRAS

Using the DIANOGA hydrodynamical zoom-in simulation set of galaxy clusters, we analyze the dynamics traced by stars belonging to the Brightest Cluster Galaxies (BCGs) and their surrounding diffuse component, forming the intracluster light (ICL), and compare it to the dynamics traced by dark matter and galaxies identified in the simulations. We compute scaling relations between the BCG and cluster velocity dispersions and their corresponding masses (i.e. $M_\mathrm{BCG}^{\star}$- $\sigma_\mathrm{BCG}^{\star}$, $M_{200}$- $\sigma_{200}$, $M_\mathrm{BCG}^{\star}$- $M_{200}$, $\sigma_\mathrm{BCG}^{\star}$- $\sigma_{200}$), we find in general a good agreement with observational results. Our simulations also predict $\sigma_\mathrm{BCG}^{\star}$- $\sigma_{200}$ relation to not change significantly up to redshift $z=1$, in line with a relatively slow accretion of the BCG stellar mass at late times. We analyze the main features of the velocity dispersion profiles, as traced by stars, dark matter, and galaxies. As a result, we discuss that observed stellar velocity dispersion profiles in the inner cluster regions are in excellent agreement with simulations. We also report that the slopes of the BCG velocity dispersion profile from simulations agree with what is measured in observations, confirming the existence of a robust correlation between the stellar velocity dispersion slope and the cluster velocity dispersion (thus, cluster mass) when the former is computed within $0.1 R_{500}$. Our results demonstrate that simulations can correctly describe the dynamics of BCGs and their surrounding stellar envelope, as determined by the past star-formation and assembly histories of the most massive galaxies of the Universe., Comment: 16 pages, 14 figures

Cosmological N-body simulations represent an excellent tool to study the formation and evolution of dark matter (DM) halos and the mechanisms that have originated the universal profile at the largest mass scales in the Universe. In particular, the combination of the velocity dispersion $\sigma_\mathrm{v}$ with the density $\rho$ can be used to define the pseudo-entropy $S(r)=\sigma_\mathrm{v}^2/\rho^{\,2/3}$, whose profile is well-described by a simple power-law $S\propto\,r^{\,\alpha}$. We analyze a set of cosmological hydrodynamical re-simulations of massive galaxy clusters and study the pseudo-entropy profiles as traced by different collisionless components in simulated galaxy clusters: DM, stars, and substructures. We analyze four sets of simulations, exploring different resolution and physics (N-body and full hydrodynamical simulations) to investigate convergence and the impact of baryons. We find that baryons significantly affect the inner region of pseudo-entropy profiles as traced by substructures, while DM particles profiles are characterized by an almost universal behavior, thus suggesting that the level of pseudo-entropy could represent a potential low-scatter mass-proxy. We compare observed and simulated pseudo-entropy profiles and find good agreement in both normalization and slope. We demonstrate, however, that the method used to derive observed pseudo-entropy profiles could introduce biases and underestimate the impact of mergers. Finally, we investigate the pseudo-entropy traced by the stars focusing our interest in the dynamical distinction between intracluster light (ICL) and the stars bound to the brightest cluster galaxy (BCG): the combination of these two pseudo-entropy profiles is well-described by a single power-law out to almost the entire cluster virial radius.