arXiv subjects

Antonio Ferragamo

Publications and source records attributed to Antonio Ferragamo.

17 recordsLinked to original sources

arXiv2026

The Three Hundred project: Low Gas Fraction Galaxy Clusters properties and their environment

Galaxy cluster samples based on X-ray and Sunyaev-Zel'dovich (SZ) observations are affected by selection biases. These catalogs preferentially include systems with high gas content and surface brightness. Excluding objects with depleted gas content, low-gas-fraction clusters (LGFCs), could lead to an incomplete sampling. We aim to investigate the abundance and the properties of the LGFCs population using The Three Hundred hydrodynamical simulations, focusing on the Gadget-X code. In particular, we study outliers in the $f_{\mathrm{g},500} - M_{500}$ relation, environmental influences, and their behavior in key scaling relations, with a focus on the Compton-Y observable. We analyze a sample of $N_{\mathrm{tot}} = 9858$ simulated objects from The Three Hundred, in the redshift band $z \in [0;0.817]$. LGFCs are selected statistically as outliers of the $f_{\mathrm{g},500}-M_{500}$ relation. To analyze environmental effects, we compare the gas density and temperature radial profiles of LGFCs against the No-LGFCs population. Finally, we study how the temperature, entropy, and spherical Compton parameter scaling relations are affected by the inclusion of LGFCs. We find that LGFCs are preferentially found at the low-mass end and their abundance increases toward low redshift. Radial profiles of LGFCs show lower gas concentrations in the core regions and higher temperatures, suggesting a more diffuse and heated ICM. This behavior is also reflected in the entropy scaling relation, where LGFCs are extreme positive outliers. Contrary to observations, the $Y_{\mathrm{sph},500}$ values of LGFCs show no significant deviation from the general population. Nevertheless, we cannot rule out that these differences are partly driven by the mass incompleteness at the low-mass end and the environmental bias of our simulated sample.

astro-ph.GA↗

arXiv2026

The Three Hundred project: cosmic web identification from 2D gas and Compton-$y$ maps of galaxy clusters outskirts

Galaxy clusters are located at the nodes of the filamentary network known as the cosmic web. A more comprehensive understanding of galaxy clusters can be achieved by considering their environment, in particular, the filamentary structures to which they are connected. In this work, we aim to assess the reliability of the cosmic web reconstruction from mock observational data. In particular, we aim to quantify the effects of the 2D projection relative to the underlying 3D network and the impact of using the Sunyaev-Zel'dovich (SZ) effect as a tracer of the cosmic web. We reconstruct the filamentary networks in the outskirts of The Three Hundred simulated clusters with the filament finder DisPerSE. First, we extract the networks from the 2D gas distribution and evaluate their purity and completeness with respect to the 3D networks projected along the line of sight. We also compute the distances between the corresponding skeletons. Moreover, we identify filaments from simulated Compton-$y$ maps of the clusters at redshift $z=0$, and we compare them with the 2D gas network. The skeletons extracted from 2D maps provide good representations of the underlying 3D ones, both in terms of critical points and filaments. We find a median distance between the spines of the 2D and projected 3D networks of approximately $0.22 \, h^{-1}$ Mpc, although the connectivity derived from the 2D networks is slightly underestimated. We observe a good spatial agreement between the gas and SZ networks, with a median distance of $\approx 0.24 \, h^{-1}$ Mpc. Finally, we show that gas outside galaxy clusters is preferentially located in filamentary structures, which contribute $\sim 80\%$ of the integrated Compton-$Y$ parameter of clusters' outskirts.

astro-ph.CO↗

arXiv2025

CHEX-MATE: exploring the kinematical properties of Planck galaxy clusters

We analyse the kinematical properties of the CHEX-MATE (Cluster HEritage project with XMM-Newton - Mass Assembly and Thermodynamics at the Endpoint of structure formation) galaxy cluster sample. [...] We derive cluster mass profiles for 75 clusters using the \textsc{MG-MAMPOSSt} procedure, which recovers the gravitational potential and the anisotropy profiles from line-of-sight velocities and projected positions of galaxy members. The standard NFW and the Burkert models with flatter cores than NFW both adequately fit the kinematic data, with only marginal statistical preference for one model over the other. An estimation of the mass bias $(1-B_1) = M^{SZ}_{500}/M^{M}_{500} $ is performed from the comparison with SZ-X-ray-calibrated mass estimates, resulting in a value of $ 0.54 \pm 0.11$ when four evidently disturbed clusters are removed from the sample. We assess the dynamical state of the clusters by inferring the Anderson-Darling coefficient $(A^2)$ and the fraction of galaxies in substructures ($f_\text{sub}$). Except for a few cases, we found relatively low values for $A^2$, suggesting that CHEX-MATE clusters are not too far from relaxation. Moreover, no significant trends emerge among $A^2,\,f_\text{sub}$ and the difference between the log-masses estimated by \textsc{MG-MAMPOSSt} and by SZ-X-ray. We study the concentration-mass relation for the sample; despite the large scatter, we observe signs of an increasing trend for large-mass clusters, in agreement with recent theoretical expectations. Finally, the analysis of radial anisotropy profiles of member galaxies - stacked in five bins of mass and redshift - reveals that orbits tend to be isotropic at the center and more radial towards the edge, as already found in previous studies. A slight trend of increasing radial orbits at $r_{200}$ is observed in clusters with larger velocity dispersion

astro-ph.CO↗

arXiv2025

Inference of morphology and dynamical state of nearby $Planck$-SZ galaxy clusters with Zernike polynomials

We analyse the maps of the Sunyaev-Zel'dovich (SZ) signal of local galaxy clusters ($z<0.1$) observed by the $Planck$ satellite in order to classify their dynamical state through morphological features. To study the morphology of the cluster maps, we apply a method recently employed on mock SZ images generated from hydrodynamical simulated galaxy clusters in THE THREE HUNDRED (THE300) project. Here, we report the first application on real data. The method consists in modelling the images with a set of orthogonal functions defined on circular apertures, the Zernike polynomials. From the fit we compute a single parameter, $\mathcal{C}$, that quantifies the morphological features present in each image. The link between the morphology of 2D images and the dynamical state of the galaxy clusters is well known, even if not obvious. We use mock $Planck$-like Compton parameter maps generated for THE300 clusters to validate our morphological analysis. These clusters, in fact, are properly classified for their dynamical state with the relaxation parameter, $χ$, by exploiting 3D information from simulations. We find a mild linear correlation of $\sim 38\%$ between $\mathcal{C}$ and $χ$ for THE300 clusters, mainly affected by the noise present in the maps. In order to obtain a proper dynamical-state classification for the $Planck$ clusters, we exploit the conversion from the $\mathcal{C}$ parameter derived in each $Planck$ map in $χ$. A fraction of the order of $63\%$ of relaxed clusters is estimated in the selected $Planck$ sample. Our classification is then compared with those of previous works that have attempted to evaluate, with different indicators and/or other wavelengths, the dynamical state of the same $Planck$ objects. The agreement with the other works is larger than $58\%$.

astro-ph.CO↗

arXiv2025

Deep Learning generated observations of galaxy clusters from dark-matter-only simulations

Hydrodynamical simulations play a fundamental role in modern cosmological research, serving as a crucial bridge between theoretical predictions and observational data. However, due to their computational intensity, these simulations are currently constrained to relatively small volumes. Therefore, this study investigates the feasibility of utilising dark matter-only simulations to generate observable maps of galaxy clusters using a deep learning approach based on the U-Net architecture. We focus on reconstructing Compton-y parameter maps (SZ maps) and bolometric X-ray surface brightness maps (X-ray maps) from total mass density maps. We leverage data from \textsc{The Three Hundred} simulations, selecting galaxy clusters ranging in mass from $10^{13.5} h^{-1}M_{\odot}\leq M_{200} \leq 10^{15.5} h^{-1}M_{\odot}$. Despite the machine learning models being independent of baryonic matter assumptions, a notable limitation is their dependency on the underlying physics of hydrodynamical simulations. To evaluate the reliability of our generated observable maps, we employ various metrics and compare the observable-mass scaling relations. For clusters with masses greater than $2 \times 10^{14} h^{-1} M_{\odot}$, the predictions show excellent agreement with the ground-truth datasets, with percentage errors averaging (0.5 $\pm$ 0.1)\% for the parameters of the scaling laws.

astro-ph.CO↗

arXiv2025

Moving to ICSC: synergy between PNRR projects for more powerful Data Centers: a case study

The paper presents the new enhancement to the Data Center named DC1 at the University of Naples "Federico II". The ICSC funds at INFN have allowed to improve the power and cooling subsystems, while other funds from the PNRR (the STILES project) and funds directly from the MUR have allowed to enhance the computing, storage and network equipments. All these resources are in addition to the IBiSCo cluster and equipments described earlier in this book, but all together, thanks to a strong synergy between projects, have leaded to a very powerful Data Center for scientific applications.

astro-ph.IM↗

arXiv2024

The Three Hundred project: Estimating the dependence of gas filaments on the mass of galaxy clusters

Galaxy clusters are located in the densest areas of the universe and are intricately connected to larger structures through the filamentary network of the Cosmic Web. In this scenario, matter flows from areas of lower density to higher density. As a result, the properties of galaxy clusters are deeply influenced by the filaments that are attached to them, which are quantified by a parameter known as connectivity. We explore the dependence of gas-traced filaments connected to galaxy clusters on the mass and dynamical state of the cluster. Moreover, we evaluate the effectiveness of the cosmic web extraction procedure from the gas density maps of simulated cluster regions. Using the DisPerSE cosmic web finder, we identify filamentary structures from 3D gas particle distribution in 324 simulated regions of $30 \, h^{-1}$ Mpc side from The Three Hundred hydrodynamical simulation at redshifts z=0, 1, and 2. We estimate the connectivity at various apertures for $\sim3000$ groups and clusters spanning a mass range from $10^{13} \, h^{-1} \, M_{\odot}$ to $10^{15} \, h^{-1} \, M_{\odot}$. Relationships between connectivity and cluster properties like radius, mass, dynamical state and hydrostatic mass bias are explored. We show that the connectivity is strongly correlated with the mass of galaxy clusters, with more massive clusters being on average more connected. This finding aligns with previous studies in literature, both from observational and simulated data sets. Additionally, we observe a dependence of the connectivity on the aperture at which it is estimated. We find that connectivity decreases with cosmic time, while no dependencies on the dynamical state and hydrostatic mass bias of the cluster are found. Lastly, we observe a significant agreement between the connectivity measured from gas-traced and mock-galaxies-traced filaments in the simulation.

astro-ph.CO↗

arXiv2024

CHEX-MATE: Dynamical masses for a sample of 101 Planck Sunyaev-Zeldovich-selected galaxy clusters

The Cluster HEritage project with XMM-Newton - Mass Assembly and Thermodynamics at the Endpoint of structure formation (CHEX-MATE) is a programme to study a minimally biased sample of 118 galaxy clusters detected by Planck through the Sunyaev-Zeldovich effect. Accurate and precise mass measurements are required to exploit CHEX-MATE as an astrophysical laboratory and a calibration sample for cosmological probes in the era of large surveys. We measured masses based on the galaxy dynamics, which are highly complementary to weak-lensing or X-ray estimates. We analysed the sample with a uniform pipeline that is stable both for poorly sampled or rich clusters - using spectroscopic redshifts from public (NED, SDSS, and DESI) or private archives - and dedicated observational programmes. We modelled the halo mass density and the anisotropy profile. Membership is confirmed with a cleaning procedure in phase space. We derived masses from measured velocity dispersions under the assumed model. We measured dynamical masses for 101 CHEX-MATE clusters with at least ten confirmed members within the virial radius r_200c. Estimated redshifts and velocity dispersions agree with literature values when available. Validation with weak-lensing masses shows agreement within 8+-16(stat.)+-5(sys.)%, and confirms dynamical masses as an unbiased proxy. Comparison with {\it Planck} masses shows them to be biased low by 34+-3(stat.)+-5(sys.)%. A follow-up spectroscopic campaign is underway to cover the full CHEX-MATE sample.

astro-ph.CO↗

arXiv2024

Generating Galaxy Clusters Mass Density Maps from Mock Multiview Images via Deep Learning

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 {\sc{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.

astro-ph.CO↗

arXiv2024

The Three Hundred Project: Mapping The Matter Distribution in Galaxy Clusters Via Deep Learning from Multiview Simulated Observations

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, {\it 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 idealised observations of simulated galaxy clusters at multi-wavelengths. The model is trained with a large dataset of simulated images from clusters of {\sc The Three Hundred Project}. Although Machine Learning (ML) models do not depend on the assumptions of the dynamics of the intra-cluster 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.

astro-ph.CO↗

arXiv2023

Galaxy clusters morphology with Zernike polynomials: the first application on $\textit{Planck}$ Compton parameter maps

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. After the first validation of this approach on a set of high-resolution mock maps of the Compton parameter, $y$, from hydrodynamically simulated galaxy clusters in THE THREE HUNDRED project, 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.

astro-ph.CO↗

arXiv2023

Impact of filaments on galaxy cluster properties in The Three Hundred simulation

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 $10^{13} \leq M_{200} \, h^{-1} \, M_{\odot} \leq 10^{15} $ 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 $χ$.

astro-ph.CO↗

arXiv2022

\textsc{The Three Hundred} project: The \textsc{Gizmo-Simba} run

We introduce \textsc{Gizmo-Simba}, a new suite of galaxy cluster simulations within \textsc{The Three Hundred} project. \textsc{The Three Hundred} consists of zoom re-simulations of 324 clusters with $M_{200}\gtrsim 10^{14.8}M_\odot$ drawn from the MultiDark-Planck $N$-body simulation, run using several hydrodynamic and semi-analytic codes. The \textsc{Gizmo-Simba} suite adds a state-of-the-art galaxy formation model based on the highly successful {\sc Simba} simulation, mildly re-calibrated to match $z=0$ cluster stellar properties. Comparing to \textsc{The Three Hundred} zooms run with \textsc{Gadget-X}, we find intrinsic differences in the evolution of the stellar and gas mass fractions, BCG ages, and galaxy colour-magnitude diagrams, with \textsc{Gizmo-Simba} generally providing a good match to available data at $z \approx 0$. \textsc{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. \textsc{Gizmo-Simba} provides a new and novel platform to elucidate the co-evolution of galaxies, gas, and black holes within the densest cosmic environments.

astro-ph.GA↗

arXiv2021

Morphological analysis of SZ and X-ray maps of galaxy clusters with Zernike polynomials

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, $\mathcal{C}$, by combining the contribution of some ZPs in the modelling. We verify that $\mathcal{C}$ is linearly correlated with a combination of common morphological parameters and also with a proper 3D dynamical-state 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 $\textit{Planck}$ satellite. At last, we report the preliminary results of this kind of morphological analysis on mock X-ray maps of THE THREE HUNDRED clusters.

astro-ph.CO↗

arXiv2021

Velocity dispersion vs cluster mass: a new scaling law with The Three Hundred clusters

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 \THP{} project with mass above $10^{13} M_\odot$. 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.

astro-ph.CO↗

arXiv2019

Biases in the estimation of velocity dispersions and dynamical masses for galaxy clusters

Using a set of 73 numerically simulated galaxy clusters, we have characterised the statistical and physical biases for three velocity dispersion and mass estimators, namely biweight, gapper and standard deviation, in the small number of galaxies regime ($N_{gal} \leq 75$), both for the determination of the velocity dispersion and the dynamical mass of the clusters via the $σ-M$ relation. These results are used to define a new set of unbiased estimators, that are able to correct for those statistical biases. By applying these new estimators to a subset of simulated observations, we show that they can retrieve bias-corrected values for both the mean velocity dispersion and the mean mass.

astro-ph.CO↗

arXiv2018

Gravitational lensing detection of an extremely dense environment around a galaxy cluster

Galaxy clusters form at the highest density nodes of the cosmic web. The clustering of massive halos is enhanced relative to the general mass distribution and matter beyond the virial region is strongly correlated to the halo mass (halo bias). Clustering can be further enhanced depending on halo properties other than mass (secondary bias). The questions of how much and why the regions surrounding rich clusters are over-dense are still unanswered. Here, we report the analysis of the environment bias in a sample of very massive clusters, selected through the Sunyaev-Zel'dovich effect by the Planck mission. We present the first detection of the correlated dark matter associated to a single cluster, PSZ2 G099.86+58.45. The system is extremely rare in the current paradigm of structure formation. The gravitational lensing signal was traced up to 30 megaparsecs with high signal-to-noise ratio ~3.4. The measured shear is very large and points at environment matter density in notable excess of the cosmological mean. The boosting of the correlated dark matter density around high mass halos can be very effective. Together with ensemble studies of the large scale structure, lensing surveys can picture the surroundings of single haloes.

astro-ph.CO↗