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The central task is for a given cosmological model, calculate the formation and evolution of a population of clusters from which synthetic X-ray and SZ catalogs can be derived. These can be used to calibrate simpler analytic models, as well as to build synthetic surveys (mock catalogs) which can be used to assess instrumental effects and survey biases. One would like to directly simulate n(M, z), n(Lx, z), n(T, z), n(Y, z) from the governing equations for collisionless and collisional matter in an expanding universe. Clearly, the quality of these statistical predictions relies on the ability to adequately resolve the internal structure and thermodynamical evolution of the ICM.

Since X-ray emission and the SZE are both consequences of hot plasma bound in the cluster's gravitational potential well, the requirements to faithfully simulate X-ray clusters and SZ clusters are essentially the same. Numerical progress can be characterized as a quest for higher resolution and essential baryonic physics. In this section I describe the technical challenges involved and the numerical methods that have been developed to overcome them. I then discuss the effects of assumed baryonic physics on ICM structure. Our point of reference is the non-radiative (so-called adiabatic) case, which has been the subject of an extensive code comparison [20].

In Norman (2003) [19] I provided a historical review of the progress that has been made in simulating the evolution of gas in galaxy clusters motivated by X-ray observations. In Norman (2004) [69] I discuss the statistical properties of simulated galaxy cluster samples and how they depend on assumed baryonic physics. The key result of this work is that while Lx is highly sensitive to input physics and numerical resolution, LSZ is not, and therefore potentially a useful proxy for the cluster mass and thereby a cosmological probe. I discuss recent progress on increasing the physics fidelity of individual cluster simulations in Sec. 5, and the use of cluster SZ surveys as cosmological probes in Sec. 6.

4.1. Dynamic range considerations

Fig. 11 illustrates the dynamic range difficulties encountered with simulating a statistical ensemble of galaxy clusters, while at the same time resolving their internal structure. Massive clusters are rare at any redshift, yet these are the ones most that are most sensitive to cosmology. From the cluster mass function (Fig. 9), in order to get adequate statistics, one deduces that one must simulate a survey volume many hundreds of megaparsecs on a side (Fig. 11, left panel). A massive cluster has a virial radius of ~ 2 Mpc. It forms via the collapse of material within a comoving Lagrangian volume of ~ 15 Mpc. However, tidal effects from a larger region (50-100 Mpc) are important on the dynamics of cluster formation. The internal structure of cluster's ICM is shown in Fig. 11, center panel. While clusters are not spherical, two important radii are generally used to characterize them: the virial radius, which is the approximate location of the virialization shock wave that thermalizes infalling gas to 10-100 million K, and the core radius, within which the baryon densities plateau and the highest X-ray emissions and SZ intensity changes are measured. A typical radius is ~ 200 kpc. Within the core, radiative cooling and possibly other physical processes are important. Outside the core, cooling times are longer than the Hubble time, and the ICM gas is effectively adiabatic. If we wanted to achieve a spatial resolution of 1/10 of a core radius everywhere within the survey volume, we would need a spatial dynamic range of D = 500 Mpc/20 kpc = 25,000. The mass dynamic range is more severe. If we want 1 million dark matter particles within the virial radius of a 1015 Modot cluster, then we would need Nparticle = Mbox / Mparticle = Omegam rhocrit L3 / 109 approx 1011 if they were uniformly distributed in the survey volume.

Figure 11a Figure 11b

Figure 11. Left: A range of length scales of ~ 250 separates the size of a reasonable survey volume and the virial radius of a rich cluster. Right: Simplified structure of the ICM in a massive cluster. A range of length scales of ~ 20-30 separates the virial radius and the core radius.

Two solutions to spatial dynamic range problem have been developed: tree codes for gridless N-body methods [21, 22] and adaptive mesh refinement (AMR) for Eulerian particle-mesh/hydrodynamic methods [23, 24, 25, 26]. Both methods increase the spatial resolution automatically in collapsing regions as described below. The solution to the mass dynamic range problem is the use of multi-mass initial conditions in which a hierarchy of particle masses is used, with many low mass particles concentrated in the region of interest. This approach has most recently used by Springel et al. (2000) [27], who simulated the formation of a galaxy cluster dark matter halo with N = 6.9 × 106 dark matter particles, resolving the dark matter halos down to the mass scale of the Fornax dwarf spheroidal galaxy. The spatial dynamic range achieved in this simulation was R = 2 × 105. Such dynamic ranges have not yet been achieved in galaxy cluster simulations with gas.

Figure 12

Figure 12. Cosmological simulations are generally carried out in a frame of reference that is comoving with the expanding universe. Initial conditions are generated from the input power spectrum at a starting redshift and then advanced in time using equations 34 - 36.

4.2. Simulating cluster formation

Simulations of cosmological structure formation are done in a cubic domain which is comoving with the expanding universe (cf. Fig. 12). Matter density and velocity fluctuations are initialized at the starting redshift chosen such that all modes in the volume are still in the linear regime. Once initialized, these fluctuations are then evolved to z = 0 by solving the equations for collisionless N-body dynamics for cold dark matter, and the equations of ideal gas dynamics for the baryons in an expanding universe. Making the transformation from proper to comoving coordinates vector{r} = a(t) vector{x}, Newton's laws for the collsionless dark matter particles become

Equation 34 (34)

where x and v are the particle's comoving position and peculiar velocity, respectively, and phi is the comoving gravitational potential that includes baryonic and dark matter contributions. The hydrodynamical equations for mass, momentum, and energy conservation in an expanding universe in comoving coordinates are ([28])

Equation 35 (35)

where rhob, p and e, are the baryonic density, pressure and internal energy density defined in the proper reference frame, vector{upsilon}b is the comoving peculiar baryonic velocity, a = 1 / (1 + z) is the cosmological scale factor, and Gamma and Lambda are the microphysical heating and cooling rates. The baryonic and dark matter components are coupled through Poisson's equation for the gravitational potential

Equation 36 (36)

where bar{rho}(z) = 3H0 Omegam(0) / 8pi Ga3 is the proper background density of the universe.

The cosmological scale factor a(t) is obtained by integrating the Friedmann equation (Eq. 4). To complete the specification of the problem we need the ideal gas equation of state p = (gamma - 1)e, and the gas heating and cooling rates. When simulating the ICM, the simplest approximation is to assume Gamma and Lambda = 0; i.e., no heating or cooling of the gas other than by adiabatic processes and shock heating. Such simulations are referred to as adiabatic (despite entropy-creating shock waves), and are a reasonable first approximation to real clusters because except in the cores of clusters, the radiative cooling time is longer than a Hubble time, and gravitational heating is much larger than sources of astrophysical heating. However, as discussed in the paper by Cavaliere in this volume, there is strong evidence that the gas in cores of clusters has evolved non-adiabatically. This is revealed by the entropy profiles observed in clusters [29] which deviate substantially from adiabatic predictions. In the simulations presented below, we consider radiative cooling due to thermal bremsstrahlung, and mechanical heating due to galaxy feedback, details of which are described below.

4.3. Numerical methods overview

A great deal of literature exists on the gravitational clustering of CDM using N-body simulations. A variety of methods have been employed including the fast grid-based methods particle-mesh (PM), and particle-particle+particle-mesh (P3M) [30], spatially adaptive methods such as adaptive P3M [31], adaptive mesh refinement [24], tree codes [32, 33], and hybrid methods such as TreePM [34]. Because of the large dynamic range required, spatially adaptive methods are favored, with Tree and TreePM methods the most widely used today. Fig. 13 shows a high resolution N-body simulation of the substructure within a dark matter halo performed by Springel using the GADGET code [78].

Figure 13

Figure 13. Ultra-high resolution N-body simulations of the clustering of dark matter reveal a wealth of substructure. From [78].

When gas dynamics is included, only certain combinations of hydrodynamics algorithms and collisionless N-body algorithms are "natural". Dynamic range considerations have led to two principal approaches: P3MSPH and TreeSPH, which marries a P3M or tree code for the dark matter with the Lagrangian smoothed-particle-hydrodynamics (SPH) method [35, 21, 22], and adaptive mesh refinement (AMR), which marries PM with Eulerian finite-volume gas dynamics schemes on a spatially adaptive mesh [23, 26, 25, 36]. Pioneering hydrodynamic simulations using non-adaptive Eulerian grids [37, 38, 13] yielded some important insights about cluster formation and statistics, but generally have inadequate resolution to resolve their internal structure in large survey volumes. In the following we concentrate on our latest results using the AMR code Enzo [26]. The reader is also referred to the paper by Borgani et al. [39] which presents recent, high-resolution results from a large TreeSPH simulation.

Enzo is a grid-based hybrid code (hydro + N-body) which uses the block-structured AMR algorithm of Berger & Collela [40] to improve spatial resolution in regions of large gradients, such as in gravitationally collapsing objects. The method is attractive for cosmological applications because it: (1) is spatially- and time-adaptive, (2) uses accurate and well-tested grid-based methods for solving the hydrodynamics equations, and (3) can be well optimized and parallelized. The central idea behind AMR is to solve the evolution equations on a grid, adding finer meshes in regions that require enhanced resolution. Mesh refinement can be continued to an arbitrary level, based on criteria involving any combination of overdensity (dark matter and/or baryon), Jeans length, cooling time, etc., enabling us to tailor the adaptivity to the problem of interest. The code solves the following physics models: collisionless dark matter and star particles, using the particle-mesh N-body technique [41]; gravity, using FFTs on the root grid and multigrid relaxation on the subgrids; cosmic expansion; gas dynamics, using the piecewise parabolic method (PPM) [42]; multispecies nonequilibrium ionization and H2 chemistry, using backward Euler time differencing [28]; radiative heating and cooling, using subcycled forward Euler time differencing [43]; and a parameterized star formation/ feedback recipe [44]. At the present time, magnetic fields and radiation transport are being installed. Enzo is publicly available at

4.4. Structure of nonradiative clusters: the Santa Barbara test cluster

In Frenk et al. [20] 12 groups compared the results of a variety of hydrodynamic cosmological algorithms on a standard test problem. The test problem, called the Santa Barbara cluster, was to simulate the formation of a Coma-like cluster in a standard CDM cosmology (Omegam = 1) assuming the gas is nonradiative. Groups were provided with uniform initial conditions and were asked to carry out a "best effort" computation, and analyze their results at z = 0.5 and z = 0 for a set of specified outputs. These outputs included global integrated quantities, radial profiles, and column-integrated images. The simulations varied substantially in their spatial and mass resolution owing to algorithmic and hardware limitations. Nonetheless, the comparisons brought out which predicted quantities were robust, and which were not yet converged. In Fig. 14 we show a few figures from Frenk et al. (1999) which highlight areas of agreement (top row) and disagreement (bottom row).

Figure 14

Figure 14. The Santa Barbara test cluster. Top row, left to right: profiles of dark matter density, gas density, and gas pressure. Bottom row, left to right: profiles of gas temperature, gas entropy, and X-ray emissivity. Different symbols correspond to different code results. From [20].

The top row shows profile of dark matter density, baryon density, and pressure for the different codes. All are in quite good agreement for the mechanical structure of the cluster. The dark matter profile is well described by an NFW profile which has a central cusp [45]. The baryon density profiles show more dispersion, but all codes agree that the profile flattens at small radius, as observed. All codes agree extremely well on the gas pressure profile, which is not surprising, since mechanical equilibrium is easy to achieve for all methods even with limited resolution. This bodes well for the interpretation of SZE observations of clusters, since the Compton y parameter is proportional to the projected pressure distribution. In section 5 we show results from a statistical ensemble of clusters which bear this out.

The bottom row shows the thermodynamic structure of the cluster, as well as the profile of X-ray emissivity. The temperature profiles show a lot of scatter within about one-third the virial radius (= 2.7 Mpc). Systematically, the SPH codes produce nearly isothermal cores, while the grid codes produce temperature profiles which continue to rise as r 0. The origin of this discrepancy has not been resolved, but improved SPH formulations come closer to reproducing the AMR results [51]. This discrepancy is reflected in the entropy profiles. Again, agreement is good in the outer two-thirds of the cluster, but the profiles show a lot of dispersion in the inner one third. Discounting the codes with inadequate resolution, one finds the SPH codes produce an entropy profile which continues to fall as r 0, while the grid codes show an entropy core, which is more consistent with observations [29]. The dispersion in the density and temperature profiles are amplified in the X-ray emissivity profile, since epsilonx propto rhob2 T1/2. The different codes agree on the integrated X-ray luminosity of the cluster only to within a factor of 2. This is primarily because the density profile is quite sensitive to resolution in the core; any underestimate in the core density due to inadequate resolution is amplified by the density squared dependence of the emissivity. This suggests that quite high resolution is needed, as well as a good grasp on non-adiabatic processes operating in cluster cores, before simulations will be able to accurately predict X-ray luminosities.

4.5. Effect of additional physics

Within r = 0.15 rvir, Vikhlinin et al. [50] found large variation in temperature profiles, but in all cases the gas is cooler than the cluster mean. This suggests that radiative cooling is important in cluster cores, and possibly other effects as well. It has been long known that ~ 60 percent of nearby, luminous X-ray clusters have central X-ray excesses, which has been interpreted as evidence for the presence of a cluster-wide cooling flows [64]. More recently, Ponman et al. [29] have used X-ray observations to deduce the entropy profiles in galaxy groups and clusters. They find an entropy floor in the cores of clusters indicative of extra, non-gravitational heating, which they suggest is feedback from galaxy formation. It is easy to imagine cooling and heating both may be important to the thermodynamic evolution of ICM gas.

To explore the effects of additional physics on the ICM, we recomputed the entire sample of clusters changing the assumed baryonic physics, keeping initial conditions the same. Three additional samples of about 100 clusters each were simulated: The "radiative cooling" sample assumes no additional heating, but gas is allowed to cool due to X-ray line and bremsstrahlung emission in a 0.3 solar metallicity plasma. The "star formation" sample uses the same cooling, but additionally cold gas is turned into collisionless star particles at a rate dot{rho}SF = epsilonsf rhob / max(taucool , taudyn), where epsilon sf is the star formation efficiency factor ~ 0.1, and taucool and taudyn are the local cooling time and freefall time, respectively. This locks up cold baryons in a non-X-ray emitting component, which has been shown to have an important effect of the entropy profile of the remaining hot gas [56, 57]. Finally, we have the "star formation feedback" sample, which is similar to the previous sample, except that newly formed stars return a fraction of their rest mass energy as thermal and mechanical energy. The source of this energy is high velocity winds and supernova energy from massive stars. In Enzo, we implement this as thermal heating in every cell forming stars: Gammasf = epsilonSN dot{rho}SF c2. The feedback parameter depends on the assumed stellar IMF the explosion energy of individual supernovae. It is estimated to be in the range 10-6 leq epsilonSN leq 10-5 [44]. We treat it as a free parameter.

Fig. 15 shows synthetic maps of X-ray surface brightness, temperature, and Compton y-parameter for a M = 2 × 1015 Modot cluster at z = 0 for the three cases indicated. The "star formation" case is omitted because the images are very similar to the "star formation feedback" case (see reference [46].) The adiabatic cluster shows that the X-ray emission is highly concentrated to the cluster core. The projected temperature distribution shows a lot of substructure, which is true for the adiabatic sample as a whole [47]. A complex virialization shock is toward the edge of the frame. The y-parameter is smooth, relatively symmetric, and centrally concentrated. The inclusion of radiative cooling has a strong effect on the temperature and X-ray maps, but relatively little effect on the SZE map. The significance of this is discussed in Section 5. In simulations with radiative cooling only, dense gas in merging subclusters cools to 104 K and is brought into the cluster core intact [48]. These cold lumps are visible as dark spots in the temperature map. They appear as X-ray bright features. The inclusion of star formation and energy feedback erases these cold lumps, producing maps in all three quantities that resemble slightly smoothed versions of the adiabatic maps. However, an analysis of the radial temperature profiles (Fig. 15) reveal important differences in the cluster core. The temperature continues to rise toward smaller radii in the adiabatic case, while it plummets to ~ 104 K for the radiative cooling case. While the temperature profile looks qualitatively similar to observations of so-called cooling flow clusters, our central temperature is too low and the X-ray brightness too high. The star formation feedback case converts the cool gas into stars, and yields a temperature profile which follows the UTP at r geq 0.15rvir, but flattens out at smaller radii. This is consistent with the high resolution Chandra observations of Vikhlinin et al. [50].

Figure 15a Figure 15b

Figure 15. Effect of physical processes on simulated galaxy cluster ICM observables. Left: Columns show X-ray surface brightness, projected temperature, and Compton y-parameter for a M = 2 × 1015 Modot cluster assuming different baryonic physics. Field of view is 5 h-1 Mpc. Right: Corresponding spherically averaged radial temperature profiles. From [46].

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