Simulated Clusters Research Articles

Mass modeling and kinematics of galaxy clusters in modified gravity

The chameleon screening mechanism has been constrained many a time using dynamic and kinematic galaxy cluster observables. Current constraints are, however, insensitive to different mass components within galaxy clusters and have been mainly focused on a single mass density profile, the Navarro-Frenk-White mass density model. In this work, we extend the study of the Chameleon screening mechanism in galaxy clusters by considering a series of mass density models, namely: generalized-Navarro-Frenk-While, b-Navarro-Frenk-While, Burket, Isothermal and Einasto. The coupling strength (β) and asymptotic value of the chameleon field (ϕ ∞) are constrained by using kinematics analyses of simulated galaxy clusters, generated both assuming General Relativity and a strong chameleon scenario. By implementing a Bayesian analysis we comprehensively show that the biases introduced due to an incorrect assumption of the mass model are minimal. Similarly, we also demonstrate that a spurious detection of evidence for modifications to gravity is highly unlikely when utilizing the kinematics of galaxy clusters.

A Systematic Analysis of Star Cluster Disruption by Tidal Shocks. II. Predicting Star Cluster Dissolution Rates from a Time-series Analysis of Their Tidal Histories

Most of the dynamical mass loss from star clusters is thought to be caused by the time variability of the tidal field (“tidal shocks”). Systematic studies of tidal shocks have been hampered by the fact that each tidal history is unique, implying both a reproducibility and a generalization problem. Here we address these issues by investigating how star cluster evolution depends on the statistical properties of its tidal history. We run a large suite of direct N-body simulations of clusters with tidal histories generated from power spectra of a given slope and with different normalizations, which determine the timescales and amplitudes of the shocks, respectively. At fixed normalization (i.e., the same median tidal field strength), the dissolution timescale is nearly independent of the power spectrum slope. However, the dispersion in dissolution timescales, obtained by repeating simulations for different realizations of statistically identical tidal histories, increases with the power spectrum slope. This result means that clusters experiencing high-frequency shocks have more similar mass-loss histories than clusters experiencing low-frequency shocks. The density–mass relationship of the simulated clusters follows a power law with slope between 1.08 and 1.45, except for the lowest normalizations (for which clusters effectively evolve in a static tidal field). Our findings suggest that star cluster evolution can be described statistically from a time-series analysis of its tidal history, which is an important simplification for describing the evolution of the star cluster population during galaxy formation and evolution.

The glow of axion quark nugget dark matter

Context. The existence of axion quark nuggets is a potential consequence of the axion field, which provides a possible solution to the charge-conjugation parity violation in quantum chromodynamics. In addition to explaining the cosmological discrepancy of matter-antimatter asymmetry and a visible-to-dark-matter ratio of Ωdark/Ωvisible ≃ 5, these composite compact objects are expected to represent a potentially ubiquitous electromagnetic background radiation by interacting with ordinary baryonic matter. We conducted an in-depth analysis of axion quark nugget-baryonic matter interactions in the environment of the intracluster medium in the constrained cosmological Simulation of the LOcal Web (SLOW). Aims. Here, we aim to provide upper limit predictions on electromagnetic counterparts of axion quark nuggets in the environment of galaxy clusters by inferring their thermal and non-thermal emission spectrum originating from axion quark nugget-cluster gas interactions. Methods. We analyzed the emission of axion quark nuggets in a large sample of 161 simulated galaxy clusters using the SLOW simulation. These clusters are divided into a sub-sample of 150 galaxy clusters, ordered in five mass bins ranging from 0.8 to 31.7 × 1014 M⊙, along with 11 cross-identified galaxy clusters from observations. We investigated dark matter-baryonic matter interactions in galaxy clusters in their present stage at the redshift of z = 0 by assuming all dark matter consists of axion quark nuggets. The resulting electromagnetic signatures were compared to thermal Bremsstrahlung and non-thermal cosmic ray (CR) synchrotron emission in each galaxy cluster. We further investigated individual frequency bands imitating the observable range of the WMAP, Planck, Euclid, and XRISM telescopes for the most promising cross-identified galaxy clusters hosting detectable signatures of axion quark nugget emission. Results. We observed a positive excess in the low- and high-energy frequency windows, where thermal and non-thermal axion quark nugget emission can significantly contribute to (or even outshine) the emission of the intracluster medium (ICM) in frequencies up to νT ≲ 3842.19 GHz and νT ϵ [3.97, 10.99] × 1010GHz, respectively. Emission signatures of axion quark nuggets are found to be observable if CR synchrotron emission of individual clusters is sufficiently low. The degeneracy in the parameters contributing to an emission excess makes it challenging to offer predictions with respect to pinpointing specific regions of a positive axion quark nugget excess; however, a general increase in the total galaxy cluster emission is expected based on this dark matter model. Axion quark nuggets constitute an increment of 4.80% of the total galaxy cluster emission in the low-energy regime of νT ≲ 3842.19 GHz for a selection of cross-identified galaxy clusters. We propose that the Fornax and Virgo clusters represent the most promising candidates in the search for axion quark nugget emission signatures. Conclusions. The results from our simulations point towards the possibility of detecting an axion quark nugget excess in galaxy clusters in observations if their signatures can be sufficiently disentangled from the ICM radiation. While this model proposes a promising explanation for the composition of dark matter, with the potential to have this outcome verified by observations, we propose further changes that are aimed at refining our methods. Our ultimate goal is to identify the extracted electromagnetic counterparts of axion quark nuggets with even greater precision in the near future.

The formation and evolution of dark star clusters – II. The impact of primordial mass segregation

ABSTRACT We investigate the impact of primordial mass segregation on the formation and evolution of dark star clusters (DSCs). Considering a wide range of initial conditions, we conducted N-body simulations of globular clusters (GCs) around the Milky Way. In particular, we assume a canonical initial mass function for all GCs without natal kicks for supernova remnants, namely neutron stars or black holes. Our results demonstrate that clusters with larger degrees of primordial mass segregation reach their DSC phase earlier and spend a larger fraction of their dissolution time in such a phase, compared to clusters without mass segregation. In primordially segregated clusters, the maximum Galactocentric distance that the clusters can have to enter the DSC phase is almost twice that of the clusters without primordial mass segregation. Primordially segregated clusters evolve with a higher number of stellar mass black holes, accelerating energy creation in their central regions and consequently increasing evaporation rates and cluster sizes during dark phases. The simulations reveal that aggregating heavy components at the centre doubles the time spent in the dark phase. Additionally, the study identifies potential links between simulated dark clusters and initial conditions of Milky Way GCs, suggesting some may transition to dark phases before dissolution. Higher primordial mass segregation coefficients amplify the average binary black hole formation rate by 2.5 times, raising higher expectations for gravitational-wave emissions.

Constructing a Galaxy Cluster Catalog in IllustrisTNG300 Using the Mulguisin Algorithm

We present a new simulated galaxy cluster catalog based on the IllustrisTNG simulation. We use the Mulguisin (MGS) algorithm to identify galaxy overdensities. Our cluster identification differs from the previous friends-of-friends (FoF) cluster identification in two aspects: (1) we identify cluster halos based on the galaxy subhalos instead of unobservable dark matter particles, and (2) we use the MGS algorithm, which separates galaxy overdensities hosted by massive galaxies. Our approach provides a cluster catalog constructed in a way similar to the construction of observed cluster catalogs using spectroscopic surveys. The MGS cluster catalog lists 303 halos with M 200 > 1014 M ⊙, including ∼10% more than the FoF catalog. The MGS catalog includes more systems because we separate some independent massive MGS cluster halos that are bundled into a single FoF halo. These independent MGS halos are apparently distinguishable in the galaxy spatial distribution and the phase-space diagram. Because we construct a refined cluster catalog that identifies local galaxy overdensities, we evaluate the effect of MGS clusters on the evolution of galaxies better than when using the FoF cluster catalog. The MGS halo identification also enables effective identifications of merging clusters by selecting systems with neighboring galaxy overdensities. We thus highlight the fact that the MGS cluster catalog is a useful tool for studying clusters in cosmological simulations and for comparing with observed cluster samples.

The Velocity Dispersion Function for Quiescent Galaxies in Massive Clusters from IllustrisTNG

We derive the central stellar velocity dispersion function (VDF) for quiescent galaxies in 280 massive clusters with log(M200/M⊙)>14 in IllustrisTNG300. The VDF is an independent tracer of the dark matter mass distribution of subhalos in galaxy clusters. Based on the IllustrisTNG cluster catalog, we select quiescent member subhalos with a specific star formation rate <2 × 10−11 yr−1 and stellar mass log(M*/M⊙)>9 . We then simulate fiber spectroscopy to measure the stellar velocity dispersion of the simulated galaxies; we compute the line-of-sight velocity dispersions of star particles within a cylindrical volume that penetrates the core of each subhalo. We construct the VDFs for quiescent subhalos within R 200. The simulated cluster VDF exceeds the simulated field VDF for logσ*>2.2 , indicating the preferential formation of large velocity dispersion galaxies in dense environments. The excess is similar in simulations and in the observations. We also compare the simulated VDF for the three most massive clusters with log(M200/M⊙)>15 with the observed VDF for the two most massive clusters in the local Universe, Coma and A2029. Intriguingly, the simulated VDFs are significantly lower for logσ*>2.0 . This discrepancy results from (1) a smaller number of subhalos with log(M*/M⊙)>10 in TNG300 compared to the observed clusters, and (2) a significant offset between the observed and simulated M *–σ * relations. The consistency in the overall shape of the observed and simulated VDFs offers a unique window into galaxy and structure formation in simulations.

The Three Hundred: The existence of massive dark matter-deficient satellite galaxies in cosmological simulations

The observation of a massive galaxy with an extremely low dark matter content (i.e. NGC 1277) has posed questions about how such objects form and evolve in a hierarchical universe. We here report on the finding of several massive, dark matter-deficient galaxies in a set of 324 galaxy clusters theoretically modelled by means of full-physics hydrodynamical simulations. We first focus on two example galaxies selected amongst the most massive and dark matter-deficient ones. By tracing the evolution of these galaxies, we find that their lack of dark matter is a result of multiple pericentre passages. While orbiting their host halo, tidal interactions gradually strip away dark matter while preserving the stellar component. A statistical analysis of all massive satellite galaxies in the simulated clusters shows that the stellar-to-total mass ratio today is strongly influenced by the number of orbits and the distance at pericentres. Galaxies with more orbits and closer pericentres are more dark matter-deficient. Additionally, we find that massive, dark matter-deficient galaxies at the present day are either the remnants of very massive galaxies at infall or former central galaxies of infalling groups. We conclude that such massive yet dark matter-deficient galaxies exist and are natural by-products of typical cluster galaxy evolution, with no specific requirement for an exotic formation scenario.

Mass scaling relations for dark halos from an analytic universal outer density profile

Context. The average matter density within the turnaround scale, which demarcates where galaxies shift from clustering around a structure to joining the expansion of the Universe, is an important cosmological probe. However, a measurement of the mass enclosed by the turnaround radius is difficult. Analyses of the turnaround scale in simulated galaxy clusters place the turnaround radius at about three times the virial radius in a ΛCDM universe and at a (present-day) density contrast with the background matter density of the Universe of δ ~ 11. Assessing the mass at such extended distances from a cluster’s center is a challenge for current mass measurement techniques. Consequently, there is a need to develop and validate new mass-scaling relations, to connect observable masses at cluster interiors with masses at greater distances. Aims. Our research aims to establish an analytical framework for the most probable mass profile of galaxy clusters, leading to novel mass scaling relations, allowing us to estimate masses at larger scales. We derive such analytical mass profiles and compare them with those from cosmological simulations. Methods. We used excursion set theory, which provides a statistical framework for the density and local environment of dark matter halos, and complement it with the spherical collapse model to follow the non-linear growth of these halos. Results. The profile we developed analytically showed good agreement (better than 30%, and dependent on halo mass) with the mass profiles of simulated galaxy clusters. Mass scaling relations were obtained from the analytical profile with offset better than 15% from the simulated ones. This level of precision highlights the potential of our model for probing structure formation dynamics at the outskirts of galaxy clusters.

Assembly of the intracluster light in the Horizon-AGN simulation

ABSTRACT The diffuse stellar component of galaxy clusters made up of intergalactic stars is termed the intracluster light (ICL). Although there is a developing understanding of the mechanisms by which the ICL is formed, no strong consensus has yet been reached on which objects the stars of the ICL are primarily sourced from. We investigate the assembly of the ICL starting approximately 10 Gyr before $z=0$ in 11 galaxy clusters (halo masses between $\sim 1\times 10^{14}$ and $\sim 7\times 10^{14}$ M$_{\odot }$ at $z\approx 0$) in the Horizon-AGN simulation. By tracking the stars of galaxies that fall into these clusters past cluster infall, we are able to link almost all of the $z\approx 0$ ICL back to progenitor objects. Satellite stripping, mergers, and pre-processing are all found to make significant contributions to the ICL, but any contribution from in situ star formation directly into the ICL appears negligible. Even after compensating for resolution effects, we find that approximately 90 per cent of the stacked ICL of the 11 clusters that is not pre-processed should come from galaxies infalling with stellar masses above $10^{9}$ M$_{\odot }$, with roughly half coming from infalling galaxies with stellar masses within half a dex of $10^{11}$ M$_{\odot }$. The fact that the ICL appears largely sourced from such massive objects suggests that the ICL assembly of any individual cluster may be principally stochastic.

The quasi-stochastic nature of hail growth: Hail trajectory clusters in simulations of the Kingfisher, OK hailstorm

Abstract Current methods that incorporate synoptic, meso-alpha, and even some storm-scale processes to distinguish among storms that produce significantly severe versus merely severe hail have not been highly successful. These results suggest that smaller-scale processes, such as meso-beta, meso-gamma, and microscale, have a larger impact on hail production than previously theorized. Relatedly, previous research, often using hail trajectories driven by steady-state model fields, has also frequently painted hail trajectories as having simple, relatively uniform motions within a supercell. This study uses a Lagrangian hail trajectory model embedded in two idealized supercell simulations and updated every model timestep, in conjunction with a trajectory clustering technique, to examine the variability of hail trajectories both within and across the two simulations. The clustering method identified 11 distinct clusters in both supercell simulations. Clusters sourced embryos from almost all quadrants around the updraft core. Trajectories in both simulations exhibited complex and highly variable behavior, occasionally including multiple vertical loops. Cluster trajectories could follow similar paths but produce different distributions of mass at the surface, or conversely, could follow different paths but result in similar mass distributions at the surface. An aggregate review of all trajectories showed the larger hail in each simulation was somewhat more likely to be produced in regions with less strong peak updraft speeds, but other environmental profile information was more mixed, particularly for the simulation producing the largest hail. In sum, storm-scale information is highly impactful on hail trajectories, and needs to be considered in forecasting applications. Future hail trajectory modeling studies also need to incorporate more realistic storm-scale information.

Novel insights on unraveling dynamics of transmission clusters in outbreaks using phylogeny-based methods

Molecular data analysis is invaluable in understanding the overall behavior of a rapidly spreading virus population when epidemiological surveillance is problematic. It is also particularly beneficial in describing subgroups within the population, often identified as clades within a phylogenetic tree that represent individuals connected via direct transmission or transmission via differing risk factors in viral spread. However, transmission patterns or viral dynamics within these smaller groups should not be expected to exhibit homogeneous behavior over time. As such, standard phylogenetic approaches that identify clusters based on summary statistics would not be expected to capture dynamic clusters of transmission. We, therefore, sought to evaluate the performance of existing and adapted phylogeny-based cluster identification tools on simulated transmission clusters exhibiting dynamic transmission behavior over time. Despite the complementarity of the tools, we provide strong evidence that novel cluster identification methods are needed for reliable detection of epidemiologically linked individuals, particularly those exhibiting changing transmission dynamics during dynamic outbreak scenarios.

The Three Hundred project: Radio luminosity evolution from merger-induced shock fronts in simulated galaxy clusters

The Three Hundred project: Radio luminosity evolution from merger-induced shock fronts in simulated galaxy clusters

Relativistic SZ temperatures and hydrostatic mass bias for massive clusters in the FLAMINGO simulations

ABSTRACT The relativistic Sunyaev-Zel’dovich (SZ) effect can be used to measure intracluster gas temperatures independently of X-ray spectroscopy. Here, we use the large-volume FLAMINGO simulation suite to determine whether SZ y-weighted temperatures lead to more accurate hydrostatic mass estimates in massive ($M_{\rm 500c} \gt 7.5\times 10^{14}\, {\rm M}_{\odot }$) clusters than when using X-ray spectroscopic-like temperatures. We find this to be the case, on average. The median bias in the SZ mass at redshift zero is $\left\langle b \right\rangle \equiv 1-\left\langle M_{\rm 500c,hse}/M_{\rm 500c,true} \right\rangle = -0.05 \pm 0.01$, over 4 times smaller in magnitude than the X-ray spectroscopic-like case, $\left\langle b \right\rangle = 0.22 \pm 0.01$. However, the scatter in the SZ bias, $\sigma _{b} \approx 0.2$, is around 40 per cent larger than for the X-ray case. We show that this difference is strongly affected by clusters with large pressure fluctuations, as expected from shocks in ongoing mergers. Selecting the clusters with the best-fitting generalized NFW pressure profiles, the median SZ bias almost vanishes, $\left\langle b \right\rangle = -0.009 \pm 0.005$, and the scatter is halved to $\sigma _{b} \approx 0.1$. We study the origin of the SZ/X-ray difference and find that, at $R_{\rm 500c}$ and in the outskirts, SZ weighted gas better reflects the hot, hydrostatic atmosphere than the X-ray weighted gas. The SZ/X-ray temperature ratio increases with radius, a result we find to be insensitive to variations in baryonic physics, cosmology, and numerical resolution.

Long-term complementary scheduling model of hydro-wind-solar under extreme drought weather conditions using an improved time-varying hedging rule

The frequent occurrence of extreme drought weather poses serious challenges to the complementary scheduling of renewable energy, including uncertain production processes, ineffective scheduling rules, and complex model solving, which further reduces the reliability of the power grid. To address these issues, a long-term complementary scheduling model of hydro-wind-solar (LCMHWS) considering hedging rules is proposed. First, to fully eliminate the uncertainty in monthly variables, Monte Carlo simulation and K-means clustering are sequentially absorbed into the model to process historical sequences and provide deterministic scenarios in which variable characteristics are conformed as input factors. Second, an improved time-varying hedging rule with strong drought resistance capability is proposed to optimize the complementary process of hydro-wind-solar and maximize the objectives of power generation and final energy storage. Last, NSGAII and membership function are adopted to search for the optimal Pareto solution for the uncertain multi-objective model. LCMHWS is validated for 7 hydropower and nearby wind and solar plants on the Lancang River in China. The results indicate that, while meeting the guaranteed output of the system, LCMHWS can increase the power generation and final energy storage by 14 % and 3 % under extreme drought conditions, respectively, and also smoothed the output process of power grid.

Inferring intrahalo light from stellar kinematics. A deep learning approach

In the context of structure formation, disentangling the central galaxy stellar population from the stellar intrahalo light can help us shed light on the formation history of the halo as a whole, as the properties of the stellar components are expected to retain traces of the formation history. Many approaches are adopted to assess the task, depending on different physical assumptions (e.g. the light profile, chemical composition, and kinematical differences) and depending on whether the full six-dimensional phase-space information is known (much like in simulations) or whether one analyses projected quantities (i.e. observations). This paper paves the way for a new approach to bridge the gap between observational and simulation methods. We propose the use of projected kinematical information from stars in simulations in combination with deep learning to create a robust method for identifying intrahalo light in observational data to enhance understanding and consistency in studying the process of galaxy formation. Using deep learning techniques, particularly a convolutional neural network called U-Net, we developed a methodology for predicting these contributions in simulated galaxy cluster images. We created a sample of mock images from hydrodynamical simulations (including masking of the interlopers) to train, validate and test the network. Reinforced training (Attention U-Net) was used to improve the first results, as the innermost central regions of the mock images consistently overestimate the stellar intrahalo contribution. Our work shows that adequate training over a representative sample of mock images can lead to good predictions of the intrahalo light distribution. The model is mildly dependent on the training size and its predictions are less accurate when applied to mock images from different simulations. However, the main features (spatial scales and gradients of the stellar fractions) are recovered for all tests. While the method presented here should be considered as a proof of concept, future work (e.g. generating more realistic mock observations) is required to enable the application of the proposed model to observational data.

Clustering Individuals Based on Similarity in Idiographic Factor Loading Patterns

Idiographic measurement models such as p-technique and dynamic factor analysis (DFA) assess latent constructs at the individual level. These person-specific methods may provide more accurate models than models obtained from aggregated data when individuals are heterogeneous in their processes. Developing clustering methods for the grouping of individuals with similar measurement models would enable researchers to identify if measurement model subtypes exist across individuals as well as assess if the different models correspond to the same latent concept or not. In this paper, methods for clustering individuals based on similarity in measurement model loadings obtained from time series data are proposed. We review literature on idiographic factor modeling and measurement invariance, as well as clustering for time series analysis. Through two studies, we explore the utility and effectiveness of these measures. In Study 1, a simulation study is conducted, demonstrating the recovery of groups generated to have differing factor loadings using the proposed clustering method. In Study 2, an extension of Study 1 to DFA is presented with a simulation study. Overall, we found good recovery of simulated clusters and provide an example demonstrating the method with empirical data.

The heart of galaxy clusters: Demographics and physical properties of cool-core and non-cool-core halos in the TNG-Cluster simulation

We analyzed the physical properties of the gaseous intracluster medium (ICM) at the center of massive galaxy clusters with TNG-Cluster, a new cosmological magnetohydrodynamical simulation. Our sample contains 352 simulated clusters spanning a halo mass range of 1014 &lt; M500c/M⊙ &lt; 2 × 1015 at z = 0. We focused on the proposed classification of clusters into cool-core (CC) and non-cool-core (NCC) populations, the z = 0 distribution of cluster central ICM properties, and the redshift evolution of the CC cluster population. We analyzed the resolved structure and radial profiles of entropy, temperature, electron number density, and pressure. To distinguish between CC and NCC clusters, we considered several criteria: central cooling time, central entropy, central density, X-ray concentration parameter, and density profile slope. According to TNG-Cluster and with no a priori cluster selection, the distributions of these properties are unimodal, whereby CCs and NCCs represent the two extremes. Across the entire TNG-Cluster sample at z = 0 and based on the central cooling time, the strong CC fraction is fSCC = 24%, compared to fWCC = 60% and fNCC = 16% for weak and NCCs, respectively. However, the fraction of CCs depends strongly on both halo mass and redshift, although the magnitude and even direction of the trends vary with definition. The abundant statistics of simulated high-mass clusters in TNG-Cluster enabled us to match observational samples and make a comparison with data. The CC fractions from z = 0 to z = 2 are in broad agreement with observations, as are the radial profiles of thermodynamical quantities, globally as well as when divided as CC versus NCC halos. TNG-Cluster can therefore be used as a laboratory to study the evolution and transformations of cluster cores due to mergers, AGN feedback, and other physical processes.

A Generative Model for Realistic Galaxy Cluster X-Ray Morphologies

The X-ray morphologies of clusters of galaxies display significant variations, reflecting their dynamical histories and the nonlinear dependence of X-ray emissivity on the density of the intracluster gas. Qualitative and quantitative assessments of X-ray morphology have long been considered a proxy for determining whether clusters are dynamically active or “relaxed.” Conversely, the use of circularly or elliptically symmetric models for cluster emission can be complicated by the variety of complex features realized in nature, spanning scales from megaparsecs down to the resolution limit of current X-ray observatories. In this work, we use mock X-ray images from simulated clusters from The Three Hundred project to define a basis set of cluster image features. We take advantage of the clusters’ approximate self-similarity to minimize the differences between images before encoding the remaining diversity through a distribution of high-order polynomial coefficients. Principal component analysis then provides an orthogonal basis for this distribution, corresponding to natural perturbations from an average model. This representation allows novel, realistically complex X-ray cluster images to be easily generated, and we provide code to do so. The approach provides a simple way to generate training data for cluster image analysis algorithms and could be straightforwardly adapted to generate clusters displaying specific types of features or selected by physical characteristics available in the original simulations.

The SRG/eROSITA All-Sky Survey

Aims. Characterising galaxy cluster populations from a catalogue of sources selected in astronomical surveys requires knowledge of sample incompleteness, known as the selection function. The first All-Sky Survey (eRASS1) by eROSITA on board Spectrum Roentgen Gamma (SRG) has enabled the collection of large samples of galaxy clusters detected in the soft X-ray band over the western Galactic hemisphere. The driving goal consists in constraining cosmological parameters, which puts stringent requirements on the accuracy and flexibility of explainable selection function models. Methods. We used a large set of mock observations of the eRASS1 survey and we processed simulated data identically to the real eRASS1 events. We matched detected sources to simulated clusters and we associated detections to intrinsic cluster properties. We trained a series of models to build selection functions depending only on observable surface brightness data. We developed a second series of models relying on global cluster characteristics such as X-ray luminosity, flux, and the expected instrumental count rate as well as on morphological properties. We validated our models using our simulations and we ranked them according to selected performance metrics. We validated the models with datasets of clusters detected in X-rays and via the Sunyaev–Zeldovich effect. We present the complete Bayesian population modelling framework developed for this purpose. Results. Our results reveal the surface brightness characteristics most relevant to cluster selection in the eRASS1 sample, in particular the ambiguous role of central surface brightness at the scale of the instrument resolution. We have produced a series of user-friendly selection function models and demonstrated their validity and their limitations. Our selection function for bright sources reproduces the catalogue matches with external datasets well. We discuss potential inconsistencies in the selection models at a low signal-to-noise revealed by comparison with a deep X-ray sample acquired by eROSITA during its performance verification phase. Conclusions. Detailed modelling of the eRASS1 galaxy cluster selection function is made possible by reformulating selection into a classification problem. Our models are used in the first eRASS1 cosmological analysis and in sample studies of eRASS1 cluster and groups. These models are crucial for science with eROSITA cluster samples and our new methods pave the way for further investigation of faint cluster selection effects.

Observational Predictions for the Survival of Atomic Hydrogen in Simulated Fornax-like Galaxy Clusters

The presence of dense, neutral hydrogen clouds in the hot, diffuse intragroup and intracluster (IC) medium is an important clue to the physical processes controlling the survival of cold gas and sheds light on cosmological baryon flows in massive halos. Advances in numerical modeling and observational surveys mean that theory and observational comparisons are now possible. In this paper, we use the high-resolution TNG50 cosmological simulation to study the H i distribution in seven halos with masses similar to the Fornax galaxy cluster. Adopting observational sensitivities similar to the MeerKAT Fornax Survey (MFS), an ongoing H i survey that will probe to column densities of 1018 cm−2, we find that Fornax-like TNG50 halos have an extended distribution of neutral hydrogen clouds. Within 1 R vir, we predict the MFS will observe a total H i covering fraction of ∼12% (mean value) for 10 kpc pixels and 6% for 2 kpc pixels. If we restrict this to gas more than 10 half-mass radii from galaxies, the mean values only decrease mildly, to 10% (4%) for 10 (2) kpc pixels (albeit with significant halo-to-halo spread). Although there are large amounts of H i outside of galaxies, the gas seems to be associated with satellites, judging both by the visual inspection of projections and by comparison of the line of sight velocities of galaxies and IC H i.