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Cluster number count is a major cosmological probe for the next generation of cosmological large scale-structure surveys like the one expected from the Euclid satellite mission. Cosmological constraints will be mainly limited by the understanding of the selection function (SF), which characterize the probability of detecting a cluster of a given mass and redshift. The SF can be estimated by injecting realistic simulated clusters into the survey and re-applying the detection procedure. In this paper, we use the galaxy clusters from The Three Hundred project to study this effect with their member galaxies. We further study possible resolution effects by comparing low and high resolution simulations. Finally, we present the density profiles of the member galaxies and discuss their evolution with cluster mass and redshift.

Our study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel’dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions about hydrostatic equilibrium. Our results indicate that the model successfully reconstructs unbiased total and gas mass profiles, with a scatter of approximately 10%. We analyse clusters in various dynamical states and mass ranges, finding that our method’s accuracy and precision are consistent. We verify the capabilities of our model by comparing it with the hydrostatic equilibrium technique, showing that it accurately recovers total mass profiles without any bias.

The new generation of upcoming deep photometric and spectroscopic surveys will allow us to measure the astrophysical properties of faint galaxies in massive clusters. This would demand to produce simulations of galaxy clusters with better mass resolution than the ones available today if we want to make comparisons between the upcoming observations and predictions of cosmological models. But producing full-physics hydrodynamical simulations of the most massive clusters is not an easy task. This would involve billions of computational elements to reliably resolve low mass galaxies similar to those measured in observations. On the other hand, dark matter only simulations of cluster size halos can be done with much larger mass resolution but at the cost of having to apply a model that populate galaxies within each of the subhalos in these simulations. In this paper we present the results of a new set of dark matter only simulations with different mass resolutions within the The Three Hundred project. We have generated catalogs of galaxies with stellar and luminosity properties by applying the Sage Semi-Analytical Model of galaxy formation. To obtain the catalogs consistent with the results from hydrodynamical simulations, the internal physical parameters of Sage were calibrated with the Particle Swarm Optimization method using a subset of full-physics runs with the same mass resolution than the dark matter only ones.

The study of the morphology of 2D projected maps of galaxy clusters is a suitable approach to infer, from real data, the dynamical state of those systems. We recently developed a new method to recover the morphological features in galaxy cluster maps which consists of an analytical modelling through the Zernike polynomials. The validation of this approach was done on a set of high-resolution mock maps of the Compton parameter y. These maps are from hydrodynamically simulated galaxy clusters in The Three Hundred project. After this step, we apply the Zernike modelling on y-maps of local (z < 0.1) galaxy clusters observed by the Planck satellite. With a single parameter collecting the main information of the Zernike modelling, we classify their morphology. A set of mock Planck-like y-maps, generated from The Three Hundred clusters, is also used to validate our indicator with a proper dynamical state classification. This approach allows us to test the efficiency of the Zernike morphological modelling in evaluating the dynamical population in the real Planck sample.

Galaxy clusters and their filamentary outskirts reveal useful laboratories to test cosmological models and investigate Universe composition and evolution. Their environment, in particular the filaments of the Cosmic Web to which they are connected, plays an important role in shaping the properties of galaxy clusters. In this project, we analyse the gas filamentary structures present in 324 regions of The Three Hundred hydrodynamical simulation extracted with the DisPerSE filament finder. We estimate the number of gas filaments globally connected to several galaxy clusters, i.e. the connectivity k, with a mass range of 1013 ≤ M200 h−1 M⊙ ≤ 1015 at redshift z = 0. We study the positive correlation between the connectivity and mass of galaxy clusters. Moreover, we explore the impact of filaments on the dynamical state of clusters, quantified by the degree of relaxation parameter χ.

The abundance of galaxy clusters with mass and redshift is a wellknown cosmological probe. The cluster mass is a key parameter for studies that aim to constrain cosmological parameters using galaxy clusters, making it critical to understand and properly account for the errors in its estimates. Subsequently, it becomes important to correctly calibrate scaling relations between observables like the integrated Compton parameter and the mass of the cluster. The NIKA2 Sunyaev-Zeldovich Large program (LPSZ) enables one to map the intracluster medium profiles in the mm–wavelength band with great details (resolution of 11 & 17″ at 1.2 & 2 mm, respectively) and hence, to estimate the cluster hydrostatic mass more precisely than previous SZ observations. However, there are certain systematic effects which can only be accounted for with the use of simulations. For this purpose, we employ The Three Hundred simulations which have been modelled with a range of physics modules to simulate galaxy clusters. The so-called twin samples are constructed by picking synthetic clusters of galaxies with properties close to the observational targets of the LPSZ. In particular, we use the Compton parameter maps and projected total mass maps of these twin samples along 29 different lines of sight. We investigate the scatter that projection induces on the total masses. Eventually, we consider the statistical values along different lines of sight to construct a kind of 3D scaling law between the integrated Compton parameter, total mass, and overdensity of the galaxy clusters to determine the overdensity that is least impacted by the projection effect.

Cluster number count is a major cosmological probe for the next generation of cosmological large scale-structure surveys like the one expected from the Euclid satellite mission. Cosmological constraints will be mainly limited by the understanding of the selection function (SF), which characterize the probability of detecting a cluster of a given mass and redshift. The SF can be estimated by injecting realistic simulated clusters into the survey and re-applying the detection procedure. For this purpose we intend to use The Three Hundreds project, a 324 cluster sample simulated with full-physics hydrodynamical re-simulations. In this paper we concentrate on the study of the distribution of member galaxies in the cluster sample. First, we study possible resolution effects by comparing low and high resolution simulations. Finally, accounting for the latter we derive the density profiles of the member galaxies and discuss their evolution with cluster mass and redshift.

The simulation database of The Three Hundred Project has been used to pick synthetic clusters of galaxies with properties close to the observational targets of the NIKA2 camera Sunyaev–Zeldovich (SZ) Large Program. Cross–matching of cluster parameters such as mass and redshift of the cluster in the two databases has been implemented to generate the so–called twin samples for the Large Program. This SZ Large Program is observing a selection of galaxy clusters at intermediate and high redshift (0:5 < z < 0:9), covering one order of magnitude in mass. These are SZ–selected clusters from the Planck and Atacama Cosmology Telescope catalogs, wherein the selection is based on their integrated Compton parameter values, Y500: the value of the parameter within the characteristics radius R500. The Three Hundred hydrodynamical simulations provide us with hundreds of clusters satisfying these redshift, mass, and Y500 requirements. In addition to the standard post-processing analysis of the simulation, mock observational maps are available mimicking X–ray, optical, gravitational lensing, radio, and SZ observations of galaxy clusters. The primary goal of employing the twin samples is to compare different cluster mass proxies from synthetic X–ray, SZ effect and optical maps (via the velocity dispersion of member galaxies and lensing κ-maps) of the clusters. Eventually, scaling laws between different mass proxies and the cluster mass will be cross–correlated to reduce the scatter on the inferred mass and the mass bias will be related to various physical parameters.

Galaxy clusters are composed of dark matter, gas and stars. Their dark matter component, which amounts to around 80% of the total mass, cannot be directly observed but traced by the distribution of diffused gas and galaxy members. In this work, we aim to infer the cluster’s projected total mass distribution from mock observational data, i.e. stars, Sunyaev-Zeldovich, and X-ray, by training deep learning models. To this end, we have created a multiview images dataset from The Three Hundred simulation that is optimal for training Machine Learning models. We further study deep learning architectures based on the U-Net to account for single-input and multi-input models. We show that the predicted mass distribution agrees well with the true one.

The Planck Collaboration has shown that the number of clusters as a function of their mass and redshift is an extremely powerful tool for cosmological analyses. However, the true cluster mass is not directly measurable. Among the possible approaches, clusters mass could be related to different observables via self similar scaling law. These observables are related to the baryonic components of which a cluster is composed. However, the theoretical relations that allow the use of these proxies often are affected by observational and physical biases, which impacts on the determination of the cluster mass. Fortunately, cosmological simulations are an extremely powerful tool to assess these problems. We present our calibration of the scaling relation between mass and velocity dispersion of galaxy members from the study of the simulated clusters of The Three Hundred project with mass above 1013M⊙. In order to investigate the presence of a redshift dependence, we analyzed 16 different redshifts between z = 0 and z = 2. Finally, we investigated the impact of different AGN feedback models.

The assumption of Hydrostatic equilibrium (HE) is often used in observations to estimate galaxy clusters masses. We use a set of almost 300 simulated clusters from The Three Hundred Project, to estimate the cluster HE mass and the bias deriving from it. We study the dependence of the bias on several dynamical state indicators across a redshift range from 0.07 to 1.3, finding no dependence between them. Moreover, we focus our attention on the evolution of the HE bias during the merger phase, where the bias even reaches negative values due to an overestimation of the mass with HE.

The role of baryon models in hydrodynamic simulations is still unclear. Future surveys that use cluster statistics to precisely constrain cosmological models require a better understanding of the baryonic effects. Using the hydro-simulated galaxy clusters from different baryon models (Gadget-MUSIC, Gadget-X and Gizmo-SIMBA) from the THREEHUNDRED project, we can address this question into more details. We find that the galaxy cluster mass change due to different baryon models is at a few per cent level. However, the mass changes can be positive or negative depending on the baryon models. Such a small mass change leaves a weak influence (slightly larger compared to the mass changes) on both the cumulative halo numbers and the differential halo mass function (HMF) above the mass completeness. Similarly to to the halo mass change, the halo mass or the HMF can be increased or decreased with respect to the dark-matter-only (DMO) run depending on the baryon models.

Several methods are used to evaluate, from observational data, the dynamical state of galaxy clusters. Among them, the morphological analysis of cluster images is well suited for this purpose. We report a new approach to the morphology, which consists in analytically modelling the images with a set of orthogonal functions, the Zernike polynomials (ZPs). We validated the method on mock high-resolution Compton parameter maps of synthetic galaxy clusters from The Three Hundred project. To classify the maps for their morphology we defined a single parameter, C, by combining the contribution of some ZPs in the modelling. We verify that C is linearly correlated with a combination of common morphological parameters and also with a proper 3D dynamicalstate indicator available for the synthetic clusters we used. We also show the early results of the Zernike modelling applied on Compton parameter maps of local clusters (z < 0:1) observed by the Planck satellite. At last, we report the preliminary results of this kind of morphological analysis on mock X-ray maps of The Three Hundred clusters.

Galaxy cluster masses can be inferred indirectly using measurements from X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical observations. Unfortunately, all of them are affected by some bias. Alternatively, we provide an independent estimation of the cluster masses from the Planck PSZ2 catalog of galaxy clusters using a machine-learning method. We train a Convolutional Neural Network (CNN) model with the mock SZ observations from The Three Hundred (the300) hydrodynamic simulations to infer the cluster masses from the real maps of the Planck clusters. The advantage of the CNN is that no assumption on a priory symmetry in the cluster’s gas distribution or no additional hypothesis about the cluster physical state are made. We compare the cluster masses from the CNN model with those derived by Planck and conclude that the presence of a mass bias is compatible with the simulation results.