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Register a new userPhysical modelling of galaxy clusters using Einasto dark matter profiles
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We derive a model for Sunyaev--Zeldovich data from a galaxy cluster which uses an Einasto profile to model the clusters dark matter component. This model is similar to the physical models for clusters previously used by the Arcminute Microkelvin Imager (AMI) consortium, which model the dark matter using a Navarro-Frenk-White (NFW) profile, but the Einasto profile provides an extra degree of freedom. We thus present a comparison between two physical models which differ only in the way they model dark matter: one which uses an NFW profile (PM I) and one that uses an Einasto profile (PM II). We illustrate the differences between the models by plotting physical properties of clusters as a function of cluster radius. We generate AMI simulations of clusters which are textit{created} and textit{analysed} with both models. From this we find that for 14 of the 16 simulations, the Bayesian evidence gives no preference to either of the models according to the Jeffreys scale, and for the other two simulations, weak preference in favour of the correct model. However, for the mass estimates obtained from the analyses, the values were within $1sigma$ of the input values for 14 out of 16 of the clusters when using the correct model, but only in 6 out of 16 cases when the incorrect model was used to analyse the data. Finally we apply the models to real data from cluster A611 obtained with AMI, and find the mass estimates to be consistent with one another except in the case of when PM II is applied using an extreme value for the Einasto shape parameter.
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Recent high-resolution N-body CDM simulations indicate that nonsingular three-parameter models such as the Einasto profile perform better than the singular two-parameter models, e.g. the Navarro, Frenk and White, in fitting a wide range of dark matter haloes. While many of the basic properties of the Einasto profile have been discussed in previous studies, a number of analytical properties are still not investigated. In particular, a general analytical formula for the surface density, an important quantity that defines the lensing properties of a dark matter halo, is still lacking to date. To this aim, we used a Mellin integral transform formalism to derive a closed expression for the Einasto surface density and related properties in terms of the Fox H and Meijer G functions, which can be written as series expansions. This enables arbitrary-precision calculations of the surface density and the lensing properties of realistic dark matter halo models. Furthermore, we compared the Sersic and Einasto surface mass densities and found differences between them, which implies that the lensing properties for both profiles differ.
(Abriged) Assuming that the hydrostatic equilibrium holds between the intracluster medium and the gravitational potential, we constrain the NFW profiles in a sample of 44 X-ray luminous galaxy clusters observed with XMM-Newton in the redshift range 0.1-0.3. We evaluate several systematic uncertainties that affect our reconstruction of the X-ray masses. We measure the concentration c200, the dark mass M200 and the gas mass fraction within R500 in all the objects of our sample, providing the largest dataset of mass parameters for galaxy clusters in this redshift range. We confirm that a tight correlation between c200 and M200 is present and in good agreement with the predictions from numerical simulations and previous observations. When we consider a subsample of relaxed clusters that host a Low-Entropy-Core (LEC), we measure a flatter c-M relation with a total scatter that is lower by 40 per cent. From the distribution of the estimates of c200 and M200, with associated statistical (15-25%) and systematic (5-15%) errors, we use the predicted values from semi-analytic prescriptions calibrated through N-body numerical runs and measure sigma_8*Omega_m^(0.60+-0.03)= 0.45+-0.01 (at 2 sigma level, statistical only) for the subsample of the clusters where the mass reconstruction has been obtained more robustly, and sigma_8*Omega_m^(0.56+-0.04) = 0.39+-0.02 for the subsample of the 11 more relaxed LEC objects. With the further constraint from the fgas distribution in our sample, we break the degeneracy in the sigma_8-Omega_m plane and obtain the best-fit values sigma_8~1.0+-0.2 (0.75+-0.18 when the subsample of the more relaxed objects is considered) and Omega_m = 0.26+-0.01.
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.
I compare the mass values obtained with data taken from the Arcminute Microkelvin Imager (AMI) radio interferometer system and from the Planck satellite. The former of these uses a Bayesian analysis pipeline that parameterises a cluster in terms of its physical quantities, and models the dark matter & baryonic components of a cluster using Navarro-Frenk-White (NFW) and generalised-NFW profiles respectively. I also analyse simulated AMI data with input values based on PwS mass estimates. I then compare three cluster models using AMI data for the 54 cluster sample. The two observational models considered only model the gas content of the cluster. To compare the physical and observational models I consider their posterior parameter estimates, including the calculation of a metric defined between two probability distributions. The models fit to the cluster data is evaluated by looking at the Bayesian evidence values. Improvements to the physical modelling of galaxy clusters are then considered, either by relaxing some of the assumptions underlying the physical model, or by introducing a new profile for the dark matter component of clusters. The final part of the cluster analysis work focuses on Bayesian analysis using a joint likelihood function of data from both AMI and the Planck satellite simultaneously. Finally, a new Bayesian inference algorithm based on nested sampling is presented. The algorithm, named the geometric nested sampler, is an adaption of the Metropolis-Hastings nested sampler and makes use of the geometrical interpretation of sets of parameters to sample from their domains efficiently. The geometric nested sampler is tested on several toy models as well as a model representing the emission of gravitational waves from binary black hole mergers.
N-body simulations predict that dark matter haloes are described by specific density profiles on both galactic- and cluster-sized scales. Weak gravitational lensing through the measurements of their first and second order properties, shear and flexion, is a powerful observational tool for investigating the true shape of these profiles. One of the three-parameter density profiles recently favoured in the description of dark matter haloes is the Einasto profile. We present exact expressions for the shear and the first and second flexions of Einasto dark matter haloes derived using a Mellin-transform formalism in terms of the Fox H and Meijer G functions, that are valid for general values of the Einasto index. The resulting expressions can be written as series expansions that permit us to investigate the asymptotic behaviour of these quantities. Moreover, we compare the shear and flexion of the Einasto profile with those of different mass profiles including the singular isothermal sphere, the Navarro-Frenk-White profile, and the Sersic profile. We investigate the concentration and index dependences of the Einasto profile, finding that the shear and second flexion could be used to determine the halo concentration, whilst for the Einasto index the shear and first and second flexions may be employed. We also provide simplified expressions for the weak lensing properties and other lensing quantities in terms of the generalized hypergeometric function.
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