arXiv subjects

David J. Barnes

Publications and source records attributed to David J. Barnes.

32 records · Page 2Linked to original sources

arXiv2018

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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arXiv2018

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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arXiv2018

Enhancing AGN efficiency and cool-core formation with anisotropic thermal conduction

Understanding how baryonic processes shape the intracluster medium (ICM) is of critical importance to the next generation of galaxy cluster surveys. However, most models of structure formation neglect potentially important physical processes, like anisotropic thermal conduction (ATC). In this letter, we explore the impact of ATC on the prevalence of cool-cores (CCs) using 12 pairs of magnetohydrodynamical galaxy cluster simulations, simulated using the IllustrisTNG model with and without ATC. Although the impact of ATC varies from cluster to cluster and with CC criterion, its inclusion produces a systematic shift to larger CC fractions at z = 0 for all CC criteria considered. Additionally, the inclusion of ATC yields a flatter CC fraction redshift evolution, easing the tension with the observed evolution. With ATC included, the energy required for the central black hole to achieve self-regulation is reduced and the gas fraction in the cluster core increases, resulting in larger CC fractions. ATC makes the ICM unstable to perturbations and the increased efficiency of AGN feedback suggests that its inclusion results in a greater level of mixing in the ICM. Therefore, ATC is potentially an important physical process in reproducing the thermal structure of the ICM.

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arXiv2018

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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arXiv2017

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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arXiv2017

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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arXiv2017

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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arXiv2017

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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arXiv2017

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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arXiv2016

The redshift evolution of massive galaxy clusters in the MACSIS simulations

We present the MAssive ClusterS and Intercluster Structures (MACSIS) project, a suite of 390 clusters simulated with baryonic physics that yields realistic massive galaxy clusters capable of matching a wide range of observed properties. MACSIS extends the recent BAHAMAS simulation to higher masses, enabling robust predictions for the redshift evolution of cluster properties and an assessment of the effect of selecting only the hottest systems. We study the observable-mass scaling relations and the X-ray luminosity-temperature relation over the complete observed cluster mass range. As expected, we find the slope of these scaling relations and the evolution of their normalization with redshift departs significantly from the self-similar predictions. However, for a sample of hot clusters with core-excised temperatures $k_{\rm{B}}T\geq5\,\rm{keV}$ the normalization and slope of the observable-mass relations and their evolution are significantly closer to self-similar. The exception is the temperature-mass relation, for which the increased importance of non-thermal pressure support and biased X-ray temperatures leads to a greater departure from self-similarity in the hottest systems. As a consequence, these also affect the slope and evolution of the normalization in the luminosity-temperature relation. The median hot gas profiles show good agreement with observational data at $z=0$ and $z=1$, with their evolution again departing significantly from the self-similar prediction. However, selecting a hot sample of clusters yields profiles that evolve significantly closer to the self-similar prediction. In conclusion, our results show that understanding the selection function is vital for robust calibration of cluster properties with mass and redshift.

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arXiv2016

The impact of baryons on massive galaxy clusters: halo structure and cluster mass estimates

We use the BAHAMAS and MACSIS hydrodynamic simulations to quantify the impact of baryons on the mass distribution and dynamics of massive galaxy clusters, as well as the bias in X-ray and weak lensing mass estimates. These simulations use the sub-grid physics models calibrated in the BAHAMAS project, which include feedback from both supernovae and active galactic nuclei. They form a cluster population covering almost two orders of magnitude in mass, with more than 3,500 clusters with masses greater than $10^{14}\,\mathrm{M}_\odot$ at $z=0$. We start by characterising the clusters in terms of their spin, shape and density profile, before considering the bias in both weak lensing and hydrostatic mass estimates. Whilst including baryonic effects leads to more spherical, centrally concentrated clusters, the median weak lensing mass bias is unaffected by the presence of baryons. In both the dark matter only and hydrodynamic simulations, the weak lensing measurements underestimate cluster masses by ${\approx}10\%$ for clusters with $M_{200}{\leq}10^{15}\mathrm{M}_\odot$ and this bias tends to zero at higher masses. We also consider the hydrostatic bias when using both the true density and temperature profiles, and those derived from X-ray spectroscopy. When using spectroscopic temperatures and densities, the hydrostatic bias decreases as a function of mass, leading to a bias of ${\approx}40\%$ for clusters with $M_{500}{\geq}10^{15}\,\mathrm{M}_\odot$. This is due to the presence of cooler gas in the cluster outskirts. Using mass weighted temperatures and the true density profile reduces this bias to $5{-}15\%$.

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arXiv2013

Numerical Simulations of Bubble-Induced Star Formation in Dwarf Irregular Galaxies with a Novel Stellar Feedback Scheme

To study the star formation and feedback mechanism, we simulate the evolution of an isolated dwarf irregular galaxy (dIrr) in a fixed dark matter halo, similar in size to WLM, using a new stellar feedback scheme. We use the new version of our original N-body/smoothed particle chemodynamics code, GCD+, which adopts improved hydrodynamics, metal diffusion between the gas particles and new modelling of star formation and stellar wind and supernovae (SNe) feedback. Comparing the simulations with and without stellar feedback effects, we demonstrate that the collisions of bubbles produced by strong feedback can induce star formation in a more widely spread area. We also demonstrate that the metallicity in star forming regions is kept low due to the mixing of the metal-rich bubbles and the metal-poor inter-stellar medium. Our simulations also suggest that the bubble-induced star formation leads to many counter-rotating stars. The bubble-induced star formation could be a dominant mechanism to maintain star formation in dIrrs, which is different from larger spiral galaxies where the non-axisymmetric structures, such as spiral arms, are a main driver of star formation.

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arXiv2012

Calibrating an updated SPH scheme within GCD+

We adapt a modern scheme of smoothed particle hydrodynamics (SPH) to our tree N-body/SPH galactic chemodynamics code GCD+. The applied scheme includes imple- mentations of the artificial viscosity switch and artificial thermal conductivity pro- posed by Morris & Monaghan (1997), Rosswog & Price (2007) and Price (2008), to model discontinuities and Kelvin-Helmholtz instabilities more accurately. We first present hydrodynamics test simulations and contrast the results to runs undertaken without artificial viscosity switch or thermal conduction. In addition, we also explore the different levels of smoothing by adopting larger or smaller smoothing lengths, i.e. a larger or smaller number of neighbour particles, Nnb. We demonstrate that the new version of GCD+ is capable of modelling Kelvin-Helmholtz instabilities to a simi- lar level as the mesh code, Athena. From the Gresho vortex, point-like explosion and self-similar collapse tests, we conclude that setting the smoothing length to keep the number of neighbour particles as high as Nnb~58 is preferable to adopting smaller smoothing lengths. We present our optimised parameter sets from the hydrodynamics tests.

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arXiv2011

Cosmological Simulations using GCMHD+

Radio observations of galaxy clusters show that the intra cluster medium is permeated by μG magnetic fields. The origin and evolution of these cosmological magnetic fields is currently not well understood and so their impact on the dynamics of structure formation is not known. Numerical simulations are required to gain a greater understanding and produce predictions for the next generation of radio telescopes. We present the galactic chemodynamics smoothed particle magnetohydrodynamic (SPMHD) code (GCMHD+), which is an MHD implementation for the cosmological smoothed particle hydrodynamic code GCD+. The results of 1, 2 and 3 dimensional tests are presented and the performance of the code is shown relative to the ATHENA grid code. GCMHD+ shows good agreement with the reference solutions produced by ATHENA. The code is then used to simulate the formation of a galaxy cluster with a simple primordial magnetic field embedded in the gas. A homogeneous seed field of 10^-11 G is amplified by a factor of 10^3 during the formation of the cluster. The results show good agreement with the profiles found in other magnetic cluster simulations of similar resolution.

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