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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.

Numerous metrics exist to quantify the dynamical state of galaxy clusters, both observationally and within simulations. Many of these correlate strongly with one another, but it is not clear whether all of these measures probe the same intrinsic properties. In this work, we use two different statistical approaches – principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) – to investigate which dynamical properties of a cluster are in fact the best descriptors of its dynamical state. We use measurements taken directly from The Three Hundred suite of galaxy cluster simulations, as well as morphological properties calculated using mock X-ray and SZ maps of the same simulated clusters. We find that four descriptions of dynamical state naturally arise, and although correlations exist between these, a given cluster can be 'dynamically relaxed' according to all, none, or some of these four descriptions. These results demonstrate that it is highly important for future observational and theoretical studies to consider in which sense clusters are dynamically relaxed. Cluster dynamical states are complex and multidimensional, and so it is not meaningful to classify them simply as 'relaxed' and 'unrelaxed' based on a single linear scale. [ABSTRACT FROM AUTHOR]

In the current proceedings, we summarise the results presented during the mm Universe@NIKA2 conference, taken from our main results in [1]. We test the Degenerate higher-order scalar-tensor(DHOST) theory as a generalised platform for scalar-tensor theory at galaxy cluster scales to predict in such static systems small scale modification to the gravitational potential. DHOST theory is not only a good alternative to ΛCDM for the background evolution but also predicts small-scale modification to the gravitational potential in static systems such as galaxy clusters. With a sample of 12 clusters with accurate Xray Intra Cluster Medium (ICM) data (X-COP project) and Sunyaev-Zel’dovich (SZ) ICM pressure (Planck satellite), we place preliminary constraints on the DHOST parameter (Ξ1) defining the deviation from GR. Moreover, we also collect a few supplementary analyses we have performed during the course: i) Gaussian process reconstruction without parametric assumptions, ii) PSZ-only data analysis not aided by the X-ray data. Finally, we present possible extensions to the current work which may benefit from future high sensitivity and spatial resolution observations.

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.

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.

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.

A galaxy cluster as the most massive gravitationally bound object in the Universe, is dominated by dark matter, which unfortunately can only be investigated through its interaction with the luminous baryons with some simplified assumptions that introduce an un-preferred bias. In this work, we, for the first time , propose a deep learning method based on the U-Net architecture, to directly infer the projected total mass density map from idealized observations of simulated galaxy clusters at multiwavelengths. The model is trained with a large data set of simulated images from clusters of the three hundred project. Although machine learning (ML) models do not depend on the assumptions of the dynamics of the intracluster medium, our whole method relies on the choice of the physics implemented in the hydrodynamic simulations, which is a limitation of the method. Through different metrics to assess the fidelity of the inferred density map, we show that the predicted total mass distribution is in very good agreement with the true simulated cluster. Therefore, it is not surprising to see the integrated halo mass is almost unbiased, around 1 per cent for the best result from multiview, and the scatter is also very small, basically within 3 per cent. This result suggests that this ML method provides an alternative and more accessible approach to reconstructing the overall matter distribution in galaxy clusters, which can complement the lensing method. [ABSTRACT FROM AUTHOR]

The NIKA2 Sunyaev-Zel’dovich Large Program (SZLP) is focused on mapping the thermal SZ signal of a representative sample of selected Planck and ACT clusters spanning the redshift range 0.5 < z < 0.9. Hydrodynamical N-body simulations prove to be a powerful tool to endorse NIKA2 capabilities for estimating the impact of IntraCluster Medium (ICM) disturbances when re- covering the pressure radial profiles. For this goal we employ a subsample of objects, carefully extracted from the catalog Marenostrum MUltidark SImulations of galaxy Clusters (MUSIC), spanning equivalent redshift and mass ranges as the LPSZ. The joint analysis of real observations of the tSZ with NIKA2 and Planck enables to validate the NIKA2 pipeline and to estimate the ICM pressure profiles. Moreover, the possibility to identify a priori the dynamical state of the selected synthetic clusters allows us to verify the impact on the recovered ICM profile shapes and their scatters. Morphological analysis of maps of the Compton parameter seems to be a way to observationally segregate the sample based on the dynamical state in relaxed and disturbed synthetic clusters.

Using the data set of The Three Hundred project, i.e. 324 hydrodynamical resimulations of cluster-sized haloes and the regions of radius 15 |${{h^{-1}\, {\rm Mpc}}}$| around them, we study galaxy pairs in high-density environments. By projecting the galaxies' 3D coordinates onto a 2D plane, we apply observational techniques to find galaxy pairs. Based on a previous theoretical study on galaxy groups in the same simulations, we are able to classify the observed pairs into 'true' or 'false', depending on whether they are gravitationally bound or not. We find that the fraction of true pairs (purity) crucially depends on the specific thresholds used to find the pairs, ranging from around 30 to more than 80 per cent in the most restrictive case. Nevertheless, in these very restrictive cases, we see that the completeness of the sample is low, failing to find a significant number of true pairs. Therefore, we train a machine learning algorithm to help us identify these true pairs based on the properties of the galaxies that constitute them. With the aid of the machine learning model trained with a set of properties of all the objects, we show that purity and completeness can be boosted significantly using the default observational thresholds. Furthermore, this machine learning model also reveals the properties that are most important when distinguishing true pairs, mainly the size and mass of the galaxies, their spin parameter, gas content, and shape of their stellar components. [ABSTRACT FROM AUTHOR]

We use a set of about 300 simulated clusters from the three hundred Project to calculate their hydrostatic masses and evaluate the associated bias by comparing them with the true cluster mass. Over a redshift range from 0.07 to 1.3, we study the dependence of the hydrostatic bias on redshift, concentration, mass growth, dynamical state, mass, and halo shapes. We find almost no correlation between the bias and any of these parameters. However, there is a clear evidence that the scatter of the mass-bias distribution is larger for low-concentrated objects, high mass growth, and more generically for disturbed systems. Moreover, we carefully study the evolution of the bias of 12 clusters throughout a major-merger event. We find that the hydrostatic-mass bias follows a particular evolution track along the merger process: to an initial significant increase of the bias recorded at the begin of merger, a constant plateaus follows until the end of merge, when there is a dramatic decrease in the bias before the cluster finally become relaxed again. This large variation of the bias is in agreement with the large scatter of the hydrostatic bias for dynamical disturbed clusters. These objects should be avoided in cosmological studies because their exact relaxation phase is difficult to predict, hence their mass bias cannot be trivially accounted for. [ABSTRACT FROM AUTHOR]

We introduce a new parameter λ DS to quantify the dynamical state of galaxy clusters and test it using simulations from The Three Hundred cluster zoom suite. λ DS is a combination of three previously used dynamical state measures, namely virial ratio, centre of mass offset, and substructure mass fraction, crafted to assume a double-Gaussian distribution, thereby yielding a natural division between relaxed and unrelaxed clusters where the Gaussians cross. Using dark matter-only simulations, we identify the optimal separator to be λ DS  = 3.424. We test this same criterion on two sets of fully hydrodynamical The Three Hundred runs (Gadget-X and GIZMO-SIMBA), and find only a weak dependence on the input baryonic physics. We correlate the evolution of λ DS with the mass accretion history and find that halo mass changes of |$\frac{\Delta M_{200}}{M_{200}} \lesssim 0.12$| do not typically alter the dynamical state. We examine the relaxation period, defined as the time taken to return to relaxation after becoming disturbed, and find a correlation between this relaxation period and the strength of halo mass change |$\frac{\Delta M_{200}}{M_{200}}$|⁠. By fitting this correlation, we show that the relaxation period can be estimated from |$\frac{\Delta M_{200}}{M_{200}}$| (even for multiple mass accretion events) with good accuracy. [ABSTRACT FROM AUTHOR]

Close pairs of galaxies have been broadly studied in the literature as a way to understand galaxy interactions and mergers. In observations, they are usually defined by setting a maximum separation in the sky and in velocity along the line of sight, and finding galaxies within these ranges. However, when observing the sky, projection effects can affect the results, by creating spurious pairs that are not close in physical distance. In this work, we mimic these observational techniques to find pairs in the three hundred simulations of clusters of galaxies. The galaxies' 3D coordinates are projected into 2D, with Hubble flow included for their line-of-sight velocities. The pairs found are classified into 'good' or 'bad' depending on whether their 3D separations are within the 2D spatial limit or not. We find that the fraction of good pairs can be between 30 and 60 per cent depending on the thresholds used in observations. Studying the ratios of observable properties between the pair member galaxies, we find that the likelihood of a pair being 'good' can be increased by around 40, 20, and 30 per cent if the given pair has, respectively, a mass ratio below 0.2, metallicity ratio above 0.8, or colour ratio below 0.8. Moreover, shape and stellar-to-halo mass ratios, respectively, below 0.4 and 0.2 can increase the likelihood by 50 to 100 per cent. These results suggest that these properties can be used to increase the chance of finding good pairs in observations of galaxy clusters and their environment. [ABSTRACT FROM AUTHOR]

We introduce gizmo-simba , a new suite of galaxy cluster simulations within The Three Hundred project. The Three Hundred consists of zoom re-simulations of 324 clusters with |$M_{200}\gtrsim 10^{14.8}\, \mathrm{M}_\odot$| drawn from the MultiDark-Planck N -body simulation, run using several hydrodynamic and semi-analytical codes. The gizmo-simba suite adds a state-of-the-art galaxy formation model based on the highly successful Simba simulation, mildly re-calibrated to match |$z$|  = 0 cluster stellar properties. Comparing to The Three Hundred zooms run with gadget-x , we find intrinsic differences in the evolution of the stellar and gas mass fractions, BCG ages, and galaxy colour–magnitude diagrams, with gizmo-simba generally providing a good match to available data at |$z$|  ≈ 0. gizmo-simba 's unique black hole growth and feedback model yields agreement with the observed BH scaling relations at the intermediate-mass range and predicts a slightly different slope at high masses where few observations currently lie. Gizmo-Simba provides a new and novel platform to elucidate the co-evolution of galaxies, gas, and black holes within the densest cosmic environments. [ABSTRACT FROM AUTHOR]

Using the data set of the three hundred project, i.e. a suite of 324 hydrodynamical resimulations of cluster-sized haloes, we study galaxy cluster mergers and their effect on colour and luminosity changes of their brightest cluster galaxies (BCG). We track the main progenitor of each halo at z = 0 and search for merger situations based on its mass accretion history, defining mergers as very rapid increases in the halo mass. Based upon the evolution of the dynamical state of the cluster we define a pre- and post-merger phase. We create a list of all these events and statistically study their mass ratio and time-scales, with the former verifying that all instances are in fact major mergers. By comparing to a control sample of clusters without mergers, we study the effect mergers have on the stellar component of the BCG. Analysing the mass, age, and metallicity of the BCG stellar particles, we find that the stellar content of BCGs grows significantly during mergers and, even though the main growth mechanism is the accretion of older stars, there is even a burst in star formation induced by the merger. In our simulations, BCGs in mergers form in median around 70 per cent more stars than those normally growing, although this depends on the radius considered for defining the BCG. Regarding observable properties, we see an increase in SDSS- u luminosity of 20 per cent during mergers, accompanied by a slightly slower increase of the galaxy g − r colour as compared to the control sample. [ABSTRACT FROM AUTHOR]

We study the connection between morphology and dynamical state of the simulated galaxy clusters in z ∈ [0, 1.031] from The Three Hundred project. We quantify cluster dynamical state using a combination of dynamical indicators from theoretical measures and compare this combined parameter, χ, with the results from morphological classifications. The dynamical state of the cluster sample shows a continuous distribution from dynamically relaxed, more abundant at lower redshift, to hybrid and disturbed. The dynamical state presents a clear dependence on the radius, with internal regions more relaxed than outskirts. The morphology from multiwavelength mock observation of clusters in X-ray, optical, and Sunyaev–Zel'dovich (SZ) effect images is quantified by M – a combination of six parameters for X-ray and SZ maps and the offsets between the optical position of the brightest cluster galaxy (BCG) and the X-ray/SZ centroids. All the morphological parameters are highly correlated with each other, while they show a moderately strong correlation with the dynamical χ parameter. The X-ray or SZ peaks are less affected by the dynamical state than centroids, which results in reliable tracers of the cluster density peak. The principal source of contamination in the relaxed cluster fraction, inferred from morphological parameters, is due to dynamically hybrid clusters. Compared to individual parameters, which consider only one aspect of cluster property (e.g. only clumping or asymmetry), the combined morphological and dynamical parameters (M and χ) collect more information and provide a single and more accurate estimation of the cluster dynamical state. [ABSTRACT FROM AUTHOR]

The knowledge of the dynamical state of galaxy clusters allows to alleviate systematics when observational data from these objects are applied in cosmological studies. Evidence of correlation between the state and the morphology of the clusters is well studied. The morphology can be inferred by images of the surface brightness in the X-ray band and of the thermal component of the Sunyaev–Zel'dovich (tSZ) effect in the millimetre range. For this purpose, we apply, for the first time, the Zernike polynomial decomposition, a common analytical approach mostly used in adaptive optics to recover aberrated radiation wavefronts at the telescopes pupil plane. With this novel way, we expect to correctly infer the morphology of clusters and so possibly their dynamical state. To verify the reliability of this new approach, we use more than 300 synthetic clusters selected in the three hundred project at different redshifts ranging from 0 up to 1.03. Mock maps of the tSZ, quantified with the Compton parameter, y -maps, are modelled with Zernike polynomials inside R 500, the cluster reference radius. We verify that it is possible to discriminate the morphology of each cluster by estimating the contribution of the different polynomials to the fit of the map. The results of this new method are correlated with those of a previous analysis made on the same catalogue, using two parameters that combine either morphological or dynamical-state probes. We underline that instrumental angular resolution of the maps has an impact mainly when we extend this approach to high-redshift clusters. [ABSTRACT FROM AUTHOR]

Clusters of galaxies are useful tools to constrain cosmological parameters, only if their masses can be correctly inferred from observations. In particular, X-ray and Sunyaev–Zeldovich (SZ) effect observations can be used to derive masses within the framework of the hydrostatic equilibrium. Therefore, it is crucial to have a good control of the possible mass biases that can be introduced when this hypothesis is not valid. In this work, we analysed a set of 260 synthetic clusters from the MUSIC simulation project at redshifts 0 ≤ z ≤ 0.82. We estimate the hydrostatic mass of the MUSIC clusters from X-ray only (temperature and density) and from X-ray and SZ (density and pressure). Then, we compare them with the true 3D dynamical mass. The biases are of the order of 20 per cent. We find that using the temperature instead of the pressure leads to a smaller bias, although the two values are compatible within 1σ. Non-thermal contributions to the total pressure support, arising from bulk motion and turbulence of the gas, are also computed and show that they are sufficient to account for this bias. We also present a study of the correlation between the mass bias and the dynamical state of the clusters. A clear correlation is shown between the relaxation state of the clusters and the bias factor. We applied the same analysis on a subsample of 32 objects, already selected for supporting the NIKA2 SZ Large Program. [ABSTRACT FROM AUTHOR]

We use TheThreeHundred project, a suite of 324 resimulated massive galaxy clusters embedded in a broad range of environments, to investigate (i) how the gas content of the surrounding haloes correlates with the phase-space position at |$z$|  = 0 and (ii) the role that ram pressure plays in this correlation. By stacking all 324 normalized phase-space planes containing 169 287 haloes and subhaloes, we show that the halo gas content is tightly correlated with the phase-space position. At |${\sim }1.5\hbox{--}2\, {R}_{\text{200}}$| of the cluster dark matter halo, we find an extremely steep decline in the halo gas content of infalling haloes and subhaloes irrespective of cluster mass, possibly indicating the presence of an accretion shock. We also find that subhaloes are particularly gas-poor, even in the cluster outskirts, which could indicate active regions of ongoing pre-processing. By modelling the instantaneous ram pressure experienced by each halo and subhalo at |$z$|  = 0, we show that the ram pressure intensity is also well correlated with the phase-space position, which is again irrespective of cluster mass. In fact, we show that regions in the phase-space plane with high differential velocity between a halo or subhalo and its local gas environment are almost mutually exclusive with high halo gas content regions. This suggests a causal link between the gas content of objects and the instantaneous ram pressure they experience, where the dominant factor is the differential velocity. [ABSTRACT FROM AUTHOR]

The masses of galaxy clusters are a key tool to constrain cosmology through the physics of large-scale structure formation and accretion. Mass estimates based on X-ray and Sunyaev–Zel'dovich measurements have been found to be affected by the contribution of non-thermal pressure components, due, e.g. to kinetic gas energy. The characterization of possible ordered motions (e.g. rotation) of the intra-cluster medium could be important to recover cluster masses accurately. We update the study of gas rotation in clusters through the maps of the kinetic Sunyaev–Zel'dovich effect, using a large sample of massive synthetic galaxy clusters ($$M_{\rm vir} \gt 5\times 10^{14}\, h^{-1}\, {\rm M}_\odot$$ at z  = 0) from MUSIC high-resolution simulations. We select few relaxed objects showing peculiar rotational features, as outlined in a companion work. To verify whether it is possible to reconstruct the expected radial profile of the rotational velocity, we fit the maps to a theoretical model accounting for a specific rotational law, referred to as the vp2b model. We find that our procedure allows to recover the parameters describing the gas rotational velocity profile within two standard deviations, both with and without accounting for the bulk velocity of the cluster. The amplitude of the temperature distortion produced by the rotation is consistent with theoretical estimates found in the literature, and it is of the order of 23 per cent of the maximum signal produced by the cluster bulk motion. We also recover the bulk velocity projected on the line of sight consistently with the simulation true value. [ABSTRACT FROM AUTHOR]

The determination of the morphology of galaxy clusters has important repercussions for cosmological and astrophysical studies of them. In this paper, we address the morphological characterization of synthetic maps of the Sunyaev-Zel'dovich (SZ) effect for a sample of 258 massive clusters (Mvir >5×1014 h-1M⊙ at z=0), extracted from theMUSIC hydrodynamical simulations. Specifically, we use five known morphological parameters (which are already used in X-ray) and two newly introduced ones, and we combine them in a single parameter. We analyse two sets of simulations obtained with different prescriptions of the gas physics (non-radiative and with cooling, star formation and stellar feedback) at four red shifts between 0.43 and 0.82. For each parameter, we test its stability and efficiency in discriminating the true cluster dynamical state, measured by theoretical indicators. The combined parameter is more efficient at discriminating between relaxed and disturbed clusters. This parameter had a mild correlation with the hydrostatic mass (~0.3) and a strong correlation (~0.8) with the offset between the SZ centroid and the cluster centre of mass. The latter quantity is, thus, the most accessible and efficient indicator of the dynamical state for SZ studies. [ABSTRACT FROM AUTHOR]

We present a study on the coherent rotation of the intracluster medium and dark matter components of simulated galaxy clusters extracted from a volume-limited sample of the MUSIC project. The set is re-simulated with three different recipes for the gas physics: (i) non-radiative, (ii) radiative without active galactic nuclei (AGN) feedback and (iii) radiative with AGN feedback. Our analysis is based on the 146 most massive clusters identified as relaxed, 57 per cent of the total sample. We classify these objects as rotating and non-rotating according to the gas spin parameter, a quantity that can be related to cluster observations. We find that 4 per cent of the relaxed sample is rotating according to our criterion. By looking at the radial profiles of their specific angular momentum vector, we find that the solid body model is not a suitable description of rotational motions. The radial profiles of the velocity of the dark matter show a prevalence of the random velocity dispersion. Instead, the intracluster medium profiles are characterized by a comparable contribution from the tangential velocity and the dispersion. In general, the dark matter component dominates the dynamics of the clusters, as suggested by the correlation between its angular momentum and the gas one, and by the lack of relevant differences among the three sets of simulations. [ABSTRACT FROM AUTHOR]