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Scott T. Kay

Publications and source records attributed to Scott T. Kay.

At least 37 records · Page 2Linked to original sources

Redshift evolution of the hot intracluster gas metallicity in the C-EAGLE cluster simulations

The abundance and distribution of metals in galaxy clusters contains valuable information about their chemical history and evolution. By looking at how metallicity evolves with redshift, it is possible to constrain the different metal production channels. We use the C-EAGLE clusters, a sample of 30 high resolution ($m_{gas} \simeq 1.8\times 10^{6}$ M$_{\odot}$) cluster zoom simulations, to investigate the redshift evolution of metallicity, with particular focus on the cluster outskirts. The early enrichment model, in which the majority of metals are produced in the core of cluster progenitors at high redshift, suggests that metals in cluster outskirts have not significantly evolved since $z=2$. With the C-EAGLE sample, we find reasonable agreement with the early enrichment model as there is very little scatter in the metallicity abundance at large radius across the whole sample, out to at least $z=2$. The exception is Fe for which the radial dependence of metallicity was found to evolve at low redshift as a result of being mainly produced by Type Ia supernovae, which are more likely to be formed at later times than core-collapse supernovae. We also found considerable redshift evolution of metal abundances in the cores of the C-EAGLE clusters which has not been seen in other simulations or observation based metallicity studies. Since we find this evolution to be driven by accretion of low metallicity gas, it suggests that the interaction between outflowing, AGN heated material and the surrounding gas is important for determining the core abundances in clusters.

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The intra-cluster light as a tracer of the total matter density distribution: a view from simulations

By using deep observations of clusters of galaxies, it has been recently found that the projected stellar mass density closely follows the projected total (dark and baryonic) mass density within the innermost ~140 kpc. In this work, we aim to test these observations using the Cluster-EAGLE simulations, comparing the projected densities inferred directly from the simulations. We compare the iso-density contours using the procedure of Montes \& Trujillo (2019), and find that the shape of the stellar mass distribution follows that of the total matter even more closely than observed, although their radial profiles differ substantially. The ratio between stellar and total matter density profiles in circular apertures, shows a slope close to -1, with a small dependence on the cluster's total mass. We propose an indirect method to calculate the halo mass and mass density profile from the radial profile of the intra-cluster stellar mass density.

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SuperCLASS -- III. Weak lensing from radio and optical observations in Data Release 1

We describe the first results on weak gravitational lensing from the SuperCLASS survey: the first survey specifically designed to measure the weak lensing effect in radio-wavelength data, both alone and in cross-correlation with optical data. We analyse 1.53 square degrees of optical data from the Subaru telescope and 0.26 square degrees of radio data from the e-MERLIN and VLA telescopes (the DR1 data set). Using standard methodologies on the optical data only we make a significant (10 sigma) detection of the weak lensing signal (a shear power spectrum) due to the massive supercluster of galaxies in the targeted region. For the radio data we develop a new method to measure the shapes of galaxies from the interferometric data, and we construct a simulation pipeline to validate this method. We then apply this analysis to our radio observations, treating the e-MERLIN and VLA data independently. We achieve source densities of 0.5 per square arcmin in the VLA data and 0.06 per square arcmin in the e-MERLIN data, numbers which prove too small to allow a detection of a weak lensing signal in either the radio data alone or in cross-correlation with the optical data. Finally, we show preliminary results from a visibility-plane combination of the data from e-MERLIN and VLA which will be used for the forthcoming full SuperCLASS data release. This approach to data combination is expected to enhance both the number density of weak lensing sources available and the fidelity with which their shapes can be measured.

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SuperCLASS -- I. The Super CLuster Assisted Shear Survey: Project overview and Data Release 1

The SuperCLuster Assisted Shear Survey (SuperCLASS) is a legacy programme using the e-MERLIN interferometric array. The aim is to observe the sky at L-band (1.4 GHz) to a r.m.s. of 7 uJy per beam over an area of ~1 square degree centred on the Abell 981 supercluster. The main scientific objectives of the project are: (i) to detect the effects of weak lensing in the radio in preparation for similar measurements with the Square Kilometre Array (SKA); (ii) an extinction free census of star formation and AGN activity out to z~1. In this paper we give an overview of the project including the science goals and multi-wavelength coverage before presenting the first data release. We have analysed around 400 hours of e-MERLIN data allowing us to create a Data Release 1 (DR1) mosaic of ~0.26 square degrees to the full depth. These observations have been supplemented with complementary radio observations from the Karl G. Jansky Very Large Array (VLA) and optical/near infra-red observations taken with the Subaru, Canada-France-Hawaii and Spitzer Telescopes. The main data product is a catalogue of 887 sources detected by the VLA, of which 395 are detected by e-MERLIN and 197 of these are resolved. We have investigated the size, flux and spectral index properties of these sources finding them compatible with previous studies. Preliminary photometric redshifts, and an assessment of galaxy shapes measured in the radio data, combined with a radio-optical cross-correlation technique probing cosmic shear in a supercluster environment, are presented in companion papers.

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Relativistic SZ temperature scaling relations of groups and clusters derived from the BAHAMAS and MACSIS simulations

The Sunyaev-Zeldovich (SZ) effect has long been recognized as a powerful cosmological probe. Using the BAHAMAS and MACSIS simulations to obtain $>10,000$ simulated galaxy groups and clusters, we compute three temperature measures and quantify the differences between them. The first measure is related to the X-ray emission of the cluster, while the second describes the non-relativistic thermal SZ (tSZ) effect. The third measure determines the lowest order relativistic correction to the tSZ signal, which is seeing increased observational relevance. Our procedure allows us to accurately model the relativistic SZ (rSZ) contribution and we show that a $\gtrsim 10\%-40\%$ underestimation of this rSZ cluster temperature is expected when applying standard X-ray relations. The correction also exhibits significant mass and redshift evolution, as we demonstrate here. We present the mass dependence of each temperature measure alongside their profiles and a short analysis of the temperature dispersion as derived from the aforementioned simulations. We also discuss a new relation connecting the temperature and Compton-$y$ parameter, which can be directly used for rSZ modelling. Simple fits to the obtained scaling relations and profiles are provided. These should be useful for future studies of the rSZ effect and its relevance to cluster cosmology.

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Characterizing hydrostatic mass bias with Mock-X

Surveys in the next decade will deliver large samples of galaxy clusters that transform our understanding of their formation. Cluster astrophysics and cosmology studies will become systematics limited with samples of this magnitude. With known properties, hydrodynamical simulations of clusters provide a vital resource for investigating potential systematics. However, this is only realized if we compare simulations to observations in the correct way. Here we introduce the \textsc{Mock-X} analysis framework, a multiwavelength tool that generates synthetic images from cosmological simulations and derives halo properties via observational methods. We detail our methods for generating optical, Compton-$y$ and X-ray images. Outlining our synthetic X-ray image analysis method, we demonstrate the capabilities of the framework by exploring hydrostatic mass bias for the IllustrisTNG, BAHAMAS and MACSIS simulations. Using simulation derived profiles we find an approximately constant bias $b\approx0.13$ with cluster mass, independent of hydrodynamical method or subgrid physics. However, the hydrostatic bias derived from synthetic observations is mass-dependent, increasing to $b=0.3$ for the most massive clusters. This result is driven by a single temperature fit to a spectrum produced by gas with a wide temperature distribution in quasi-pressure equilibrium. The spectroscopic temperature and mass estimate are biased low by cooler gas dominating the emission, due to its quadratic density dependence. The bias and the scatter in estimated mass remain independent of the numerical method and subgrid physics. Our results are consistent with current observations and future surveys will contain sufficient samples of massive clusters to confirm the mass dependence of the hydrostatic bias.

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Hydrostatic mass estimates of massive galaxy clusters: a study with varying hydrodynamics flavours and non-thermal pressure support

We use a set of 45 simulated clusters with a wide mass range ($8\times 10^{13} < M_{500}~[$M$_{\odot}]~< 2\times 10^{15}$) to investigate the effect of varying hydrodynamics flavours on cluster mass estimates. The cluster zooms were simulated using the same cosmological models as the BAHAMAS and C-EAGLE projects, leading to differences in both the hydrodynamic solvers and the subgrid physics but still producing clusters which broadly match observations. At the same mass resolution as BAHAMAS, for the most massive clusters ($M_{500} > 10^{15}$ M$_{\odot}$), we find changes in the SPH method produce the greatest differences in the final halo, while the subgrid models dominate at lower mass. By calculating the mass of all of the clusters using different permutations of the pressure, temperature and density profiles, created with either the true simulated data or mock spectroscopic data, we find that the spectroscopic temperature causes a bias in the hydrostatic mass estimates which increases with the mass of the cluster, regardless of the SPH flavour used. For the most massive clusters, the estimated mass of the cluster using spectroscopic density and temperature profiles is found to be as low as 50 per cent of the true mass compared to $\sim$ 90 per cent for low mass clusters. When including a correction for non-thermal pressure, the spectroscopic hydrostatic mass estimates are less biased on average and the mass dependence of the bias is reduced, although the scatter in the measurements does increase.

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Disruption of satellite galaxies in simulated groups and clusters: the roles of accretion time, baryons, and pre-processing

We investigate the disruption of group and cluster satellite galaxies with total mass (dark matter plus baryons) above 10^10 M_sun in the Hydrangea simulations, a suite of 24 high-resolution cosmological hydrodynamical zoom-in simulations based on the EAGLE model. The simulations predict that ~50 per cent of satellites survive to redshift z = 0, with higher survival fractions in massive clusters than in groups and only small differences between baryonic and pure N-body simulations. For clusters, up to 90 per cent of galaxy disruption occurs in lower-mass sub-groups (i.e., during pre-processing); 96 per cent of satellites in massive clusters that were accreted at z < 2 and have not been pre-processed survive. Of those satellites that are disrupted, only a few per cent merge with other satellites, even in low-mass groups. The survival fraction changes rapidly from less than 10 per cent of those accreted at high z to more than 90 per cent at low z. This shift, which reflects faster disruption of satellites accreted at higher z, happens at lower z for more massive galaxies and those accreted onto less massive haloes. The disruption of satellite galaxies is found to correlate only weakly with their pre-accretion baryon content, star formation rate, and size, so that surviving galaxies are nearly unbiased in these properties. These results suggest that satellite disruption in massive haloes is uncommon, and that it is predominantly the result of gravitational rather than baryonic processes.

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Galaxies with monstrous black holes in galaxy cluster environments

Massive early-type galaxies follow a tight relation between the mass of their central supermassive black hole ($\rm M_{BH}$) and their stellar mass ($\rm M_{\star}$). The origin of observed positive outliers from this relation with extremely high $\rm M_{BH}$ ($> 10^{9} M_{\odot}$) remains unclear. We present a study of such outliers in the Hydrangea/C-EAGLE cosmological hydrodynamical simulations, designed to enable the study of high-mass galaxy formation and evolution in cluster environments. We find 69 $M_{\rm BH}(M_{\star})$ outliers at $z=0$, defined as those with $ \rm M_{BH} >10^{7} M_{\odot}$ and $\rm M_{BH}/\rm M_{\star}> 0.01$. This paper focusses on a sample of 5 extreme outliers, that have been selected based on their $\rm M_{BH}$ and $\rm M_{\star}$ values, which are comparable to the most recent estimates of observed positive outliers. This sample of 5 outliers, classified as `Black hole monster galaxies' (BMGs), was traced back in time to study their origin and evolution. In agreement with the results of previous simulations for lower-mass $\rm M_{BH}(\rm M_{\star})$ outliers, we find that these galaxies became outliers due to a combination of their early formation times and tidal stripping. For BMGs with $\rm M_{BH} > 10^9 M_{\odot}$, major mergers (with a stellar mass ratio of $μ> 0.25$) at early times ($z>2$) precede the rapid growth of their supermassive BHs. Furthermore, the scatter in the relation between $\rm M_{BH}$ and stellar velocity dispersion, $σ$, correlates positively with the scatter in [Mg/Fe]($σ$). This indicates that the alpha enhancement of these galaxies, which is closely related to their star formation history, is related to the growth of their central BHs.

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The Cluster-EAGLE project: a comparison of dynamical mass estimators using simulated clusters

Forthcoming large-scale spectroscopic surveys will soon provide data on thousands of galaxy clusters. It is important that the systematics of the various mass estimation techniques are well understood and calibrated. We compare three different dynamical mass estimators using the C-EAGLE galaxy clusters, a set of high resolution simulations with resolved galaxies a median total mass, $M_{200c} = 10^{14.7} \, \mathrm{M_\odot}$. We quantify the bias and scatter of the Jeans, virial, and caustic mass estimators using all galaxies with a stellar mass $M_*> 10^9 \, \mathrm{M_\odot}$, both in the ideal 3D case and in the more realistic projected case. On average we find our mass estimates are unbiased, though relative to the true mass within $r_{200c}$ the scatter is large with a range of $0.09$ - $0.15$ dex. We see a slight increase in the scatter when projecting the clusters. Selecting galaxies using the same criteria, we find no significant difference in the mass bias or scatter when comparing results from hydrodynamical and dark matter only simulations. However, selecting galaxies by stellar mass reduces the bias compared to selecting by total mass. Comparing X-ray derived hydrostatic and dynamical masses, the former are ${\sim} 30$ per cent lower. We find a slight dependence between substructure, measured using two different metrics, and mass bias. In conclusion, we find that dynamical mass estimators, when averaged together, are unbiased with a scatter of $0.11 \pm 0.02$ dex when including interloper galaxies and with no prior knowledge of $r_{200c}$.

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An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations

Machine learning (ML) techniques, in particular supervised regression algorithms, are a promising new way to use multiple observables to predict a cluster's mass or other key features. To investigate this approach we use the \textsc{MACSIS} sample of simulated hydrodynamical galaxy clusters to train a variety of ML models, mimicking different datasets. We find that compared to predicting the cluster mass from the $σ-M$ relation, the scatter in the predicted-to-true mass ratio is reduced by a factor of 4, from $0.130\pm0.004$ dex (${\simeq} 35$ per cent) to $0.031 \pm 0.001$ dex (${\simeq} 7$ per cent) when using the same, interloper contaminated, spectroscopic galaxy sample. Interestingly, omitting line-of-sight galaxy velocities from the training set has no effect on the scatter when the galaxies are taken from within $r_{200c}$. We also train ML models to reproduce estimated masses derived from mock X-ray and weak lensing analyses. While the weak lensing masses can be recovered with a similar scatter to that when training on the true mass, the hydrostatic mass suffers from significantly higher scatter of ${\simeq} 0.13$ dex (${\simeq} 35$ per cent). Training models using dark matter only simulations does not significantly increase the scatter in predicted cluster mass compared to training on simulated clusters with hydrodynamics. In summary, we find ML techniques to offer a powerful method to predict masses for large samples of clusters, a vital requirement for cosmological analysis with future surveys.

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The signal of decaying dark matter with hydrodynamical simulations

Dark matter particles may decay, emitting photons. Drawing on the EAGLE family of hydrodynamic simulations of galaxy formation -- including the APOSTLE and C-EAGLE simulations -- we assess the systematic uncertainties and scatter on the decay flux from different galaxy classes, from Milky Way satellites to galaxy clusters, and compare our results to studies of the 3.55~keV line. We demonstrate that previous detections and non-detections of this line are consistent with a dark matter interpretation. For example, in our simulations the width of the the dark matter decay line for Perseus-analogue galaxy clusters lies in the range 1300-1700~\kms. Therefore, the non-detection of the 3.55~keV line in the centre of the Perseus cluster by the {\it Hitomi} collaboration is consistent with detections by other instruments. We also consider trends with stellar and halo mass and evaluate the scatter in the expected fluxes arising from the anisotropic halo mass distribution and from object-to-object variations. We provide specific predictions for observations with {\it XMM-Newton} and with the planned X-ray telescopes {\it XRISM} and {\it ATHENA}. If future detections of unexplained X-ray lines match our predictions, including line widths, we will have strong evidence that we have discovered the dark matter.

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The diverse density profiles of galaxy clusters with self-interacting dark matter plus baryons

We present the first simulated galaxy clusters (M_200 > 10^14 Msun) with both self-interacting dark matter (SIDM) and baryonic physics. They exhibit a greater diversity in both dark matter and stellar density profiles than their counterparts in simulations with collisionless dark matter (CDM), which is generated by the complex interplay between dark matter self-interactions and baryonic physics. Despite variations in formation history, we demonstrate that analytical Jeans modelling predicts the SIDM density profiles remarkably well, and the diverse properties of the haloes can be understood in terms of their different final baryon distributions.

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Localized massive halo properties in Bahamas and Macsis simulations: scalings, log-normality, and covariance

Using tens of thousands of halos realized in the BAHAMAS and MACSIS simulations produced with a consistent astrophysics treatment that includes AGN feedback, we validate a multi-property statistical model for the stellar and hot gas mass behavior in halos hosting groups and clusters of galaxies. The large sample size allows us to extract fine-scale mass--property relations (MPRs) by performing local linear regression (LLR) on individual halo stellar mass (${\rm M}_{\rm star}$) and hot gas mass (${\rm M}_{\rm gas}$) as a function of total halo mass (${\rm M}_{\rm halo}$). We find that: 1) both the local slope and variance of the MPRs run with mass (primarily) and redshift (secondarily); 2) the conditional likelihood, $p({\rm M}_{\rm star},\ {\rm M}_{\rm gas} | \ {\rm M}_{\rm halo}, z)$ is accurately described by a multivariate, log-normal distribution, and; 3) the covariance of ${\rm M}_{\rm star}$ and ${\rm M}_{\rm gas}$ at fixed ${\rm M}_{\rm halo}$ is generally negative, reflecting a partially closed baryon box model for high mass halos. We validate the analytical population model of Evrard et al. (2014), finding sub-percent accuracy in the log-mean halo mass selected at fixed property, $\langle \ln {\rm M}_{\rm halo} | {\rm M}_{\rm gas} \rangle$ or $\langle \ln {\rm M}_{\rm halo} | {\rm M}_{\rm star} \rangle$, when scale-dependent MPR parameters are employed. This work highlights the potential importance of allowing for running in the slope and scatter of MPRs when modeling cluster counts for cosmological studies. We tabulate LLR fit parameters as a function of halo mass at $z=0$, $0.5$ and 1 for two popular mass conventions.

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Super-cluster simulations: impact of baryons on the matter power spectrum and weak lensing forecasts for Super-CLASS

We use a combination of full hydrodynamic and dark matter only simulations to investigate the effect that baryonic physics and selecting super-cluster regions have on the matter power spectrum, by re-simulating a sample of super-cluster sub-volumes. On large scales we find that the matter power spectrum measured from our super-cluster sample has at least twice as much power as that measured from our random sample. Our investigation of the effect of baryonic physics on the matter power spectrum is found to be in agreement with previous studies and is weaker than the selection effect over the majority of scales. In addition, we investigate the effect of targeting a cosmologically non-representative, super-cluster region of the sky on the weak lensing shear power spectrum. We do this by generating shear and convergence maps using a line of sight integration technique, which intercepts our random and super-cluster sub-volumes. We find the convergence power spectrum measured from our super-cluster sample has a larger amplitude than that measured from the random sample at all scales. We frame our results within the context of the Super-CLuster Assisted Shear Survey (Super-CLASS), which aims to measure the cosmic shear signal in the radio band by targeting a region of the sky that contains five Abell clusters. Assuming the Super-CLASS survey will have a source density of 1.5 galaxies/arcmin$^2$, we forecast a detection significance of $2.7^{+1.5}_{-1.2}$, which indicates that in the absence of systematics the Super-CLASS project could make a cosmic shear detection with radio data alone.

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The Cluster-EAGLE project: global properties of simulated clusters with resolved galaxies

We introduce the Cluster-EAGLE (C-EAGLE) simulation project, a set of cosmological hydrodynamical zoom simulations of the formation of $30$ galaxy clusters in the mass range $10^{14}<M_{200}/\mathrm{M}_{\odot}<10^{15.4}$ that incorporates the Hydrangea sample of Bahé et al. (2017). The simulations adopt the state-of-the-art EAGLE galaxy formation model, with a gas particle mass of $1.8\times10^{6}\,\mathrm{M}_{\odot}$ and physical softening length of $0.7\,\mathrm{kpc}$. In this paper, we introduce the sample and present the low-redshift global properties of the clusters. We calculate the X-ray properties in a manner consistent with observational techniques, demonstrating the bias and scatter introduced by using estimated masses. We find the total stellar content and black hole masses of the clusters to be in good agreement with the observed relations. However, the clusters are too gas rich, suggesting that the AGN feedback model is not efficient enough at expelling gas from the high-redshift progenitors of the clusters. The X-ray properties, such as the spectroscopic temperature and the soft-band luminosity, and the Sunyaev-Zel'dovich properties are in reasonable agreement with the observed relations. However, the clusters have too high central temperatures and larger-than-observed entropy cores, which is likely driven by the AGN feedback after the cluster core has formed. The total metal content and its distribution throughout the ICM are a good match to the observations.

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Reducing biases on $H_0$ measurements using strong lensing and galaxy dynamics: results from the EAGLE simulation

Cosmological parameter constraints from observations of time-delay lenses are becoming increasingly precise. However, there may be significant bias and scatter in these measurements due to, among other things, the so-called mass-sheet degeneracy. To estimate these uncertainties, we analyze strong lenses from the largest EAGLE hydrodynamical simulation. We apply a mass-sheet transformation to the radial density profiles of lenses, and by selecting lenses near isothermality, we find that the bias on H0 can be reduced to 5% with an intrinsic scatter of 10%, confirming previous results performed on a different simulation data set. We further investigate whether combining lensing observables with kinematic constraints helps to minimize this bias. We do not detect any significant dependence of the bias on lens model parameters or observational properties of the galaxy, but depending on the source--lens configuration, a bias may still exist. Cross lenses provide an accurate estimate of the Hubble constant, while fold (double) lenses tend to be biased low (high). With kinematic constraints, double lenses show bias and intrinsic scatter of 6% and 10%, respectively, while quad lenses show bias and intrinsic scatter of 0.5% and 10%, respectively. For lenses with a reduced $χ^2 > 1$, a power-law dependence of the $χ^2$ on the lens environment (number of nearby galaxies) is seen. Lastly, we model, in greater detail, the cases of two double lenses that are significantly biased. We are able to remove the bias, suggesting that the remaining biases could also be reduced by carefully taking into account additional sources of systematic uncertainty.

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The Hydrangea simulations: galaxy formation in and around massive clusters

We introduce the Hydrangea simulations, a suite of 24 cosmological hydrodynamic zoom-in simulations of massive galaxy clusters (M_200c = 10^14-10^15 M_Sun) with baryon particle masses of ~10^6 M_Sun. Designed to study the impact of the cluster environment on galaxy formation, they are a key part of the `Cluster-EAGLE' project (Barnes et al. 2017). They use a galaxy formation model developed for the EAGLE project, which has been shown to yield both realistic field galaxies and hot gas fractions of galaxy groups consistent with observations. The total stellar mass content of the simulated clusters agrees with observations, but central cluster galaxies are too massive, by up to 0.6 dex. Passive satellite fractions are higher than in the field, and at stellar masses Mstar > 10^10 M_Sun this environmental effect is quantitatively consistent with observations. The predicted satellite stellar mass function matches data from local cluster surveys. Normalized to total mass, there are fewer low-mass (Mstar < 10^10 M_Sun) galaxies within the virial radius of clusters than in the field, primarily due to star formation quenching. Conversely, the simulations predict an overabundance of massive galaxies in clusters compared to the field that persists to their far outskirts (> 5r_200c). This is caused by a significantly increased stellar mass fraction of (sub-)haloes in the cluster environment, by up to ~0.3 dex even well beyond r_200c. Haloes near clusters are also more concentrated than equally massive field haloes, but these two effects are largely uncorrelated.

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