4.1. The Abundance of Dark Matter Halos
In addition to characterizing the properties of individual halos, a critical prediction of any theory of structure formation is the abundance of halos, i.e. the number density of halos as a function of mass, at any redshift. This prediction is an important step toward inferring the abundances of galaxies and galaxy clusters. While the number density of halos can be measured for particular cosmologies in numerical simulations, an analytic model helps us gain physical understanding and can be used to explore the dependence of abundances on all the cosmological parameters.
A simple analytic model which successfully matches most of the numerical simulations was developed by Press & Schechter (1974) [291]. The model is based on the ideas of a Gaussian random field of density perturbations, linear gravitational growth, and spherical collapse. To determine the abundance of halos at a redshift z, we use _{m}, the density field smoothed on a mass scale M, as defined in Section 3.3. Since _{m} is distributed as a Gaussian variable with zero mean and standard deviation (M) [which depends only on the present linear power spectrum, see equation (17)], the probability that _{m} is greater than some equals
(90) |
The fundamental ansatz is to identify this probability with the fraction of dark matter particles which are part of collapsed halos of mass greater than M, at redshift z. There are two additional ingredients: First, the value used for is _{crit}(z) given in equation (81), which is the critical density of collapse found for a spherical top-hat (extrapolated to the present since (M) is calculated using the present power spectrum); and second, the fraction of dark matter in halos above M is multiplied by an additional factor of 2 in order to ensure that every particle ends up as part of some halo with M > 0. Thus, the final formula for the mass fraction in halos above M at redshift z is
(91) |
This ad-hoc factor of 2 is necessary, since otherwise only positive fluctuations of _{m} would be included. Bond et al. (1991) [52] found an alternate derivation of this correction factor, using a different ansatz. In their derivation, the factor of 2 has a more satisfactory origin, namely the so-called "cloud-in-cloud" problem: For a given mass M, even if _{m} is smaller than _{crit}(z), it is possible that the corresponding region lies inside a region of some larger mass M_{L} > M, with _{ML} > _{crit}(z). In this case the original region should be counted as belonging to a halo of mass M_{L}. Thus, the fraction of particles which are part of collapsed halos of mass greater than M is larger than the expression given in equation (90). Bond et al. showed that, under certain assumptions, the additional contribution results precisely in a factor of 2 correction.
Differentiating the fraction of dark matter in halos above M yields the mass distribution. Letting dn be the comoving number density of halos of mass between M and M + dM, we have
(92) |
where _{c} = _{crit}(z) / (M) is the number of standard deviations which the critical collapse overdensity represents on mass scale M. Thus, the abundance of halos depends on the two functions (M) and _{crit} (z), each of which depends on the energy content of the Universe and the values of the other cosmological parameters. Since recent observations confine the standard set of parameters to a relatively narrow range, we illustrate the abundance of halos and other results for a single set of parameters: _{m} = 0.3, _{} = 0.7, _{b} = 0.045, _{8} = 0.9, a primordial power spectrum index n = 1 and a Hubble constant h = 0.7.
Figure 14 shows (M) and _{crit}(z), with the input power spectrum computed from Eisenstein & Hu (1999) [118]. The solid line is (M) for the cold dark matter model with the parameters specified above. The horizontal dotted lines show the value of _{crit}(z) at z = 0, 2, 5, 10, 20 and 30, as indicated in the figure. From the intersection of these horizontal lines with the solid line we infer, e.g., that at z = 5 a 1- fluctuation on a mass scale of 2 × 10^{7} M_{} will collapse. On the other hand, at z = 5 collapsing halos require a 2- fluctuation on a mass scale of 3 × 10^{10} M_{}, since (M) on this mass scale equals about half of _{crit}(z = 5). Since at each redshift a fixed fraction (31.7%) of the total dark matter mass lies in halos above the 1- mass, Figure 14 shows that most of the mass is in small halos at high redshift, but it continuously shifts toward higher characteristic halo masses at lower redshift. Note also that (M) flattens at low masses because of the changing shape of the power spectrum. Since as M 0, in the cold dark matter model all the dark matter is tied up in halos at all redshifts, if sufficiently low-mass halos are considered.
Also shown in Figure 14 is the effect of cutting off the power spectrum on small scales. The short-dashed curve corresponds to the case where the power spectrum is set to zero above a comoving wavenumber k = 10 Mpc^{-1}, which corresponds to a mass M = 1.7 × 10^{8} M_{}. The long-dashed curve corresponds to a more radical cutoff above k = 1 Mpc^{-1}, or below M = 1.7 × 10^{11} M_{}. A cutoff severely reduces the abundance of low-mass halos, and the finite value of (M = 0) implies that at all redshifts some fraction of the dark matter does not fall into halos. At high redshifts where _{crit}(z) >> (M = 0), all halos are rare and only a small fraction of the dark matter lies in halos. In particular, this can affect the abundance of halos at the time of reionization, and thus the observed limits on reionization constrain scenarios which include a small-scale cutoff in the power spectrum [21].
In figures 15 - 18 we show explicitly the properties of collapsing halos which represent 1-, 2-, and 3- fluctuations (corresponding in all cases to the curves in order from bottom to top), as a function of redshift. No cutoff is applied to the power spectrum. Figure 15 shows the halo mass, Figure 16 the virial radius, Figure 17 the virial temperature (with µ in equation (86) set equal to 0.6, although low temperature halos contain neutral gas) as well as circular velocity, and Figure 18 shows the total binding energy of these halos. In figures 15 and 17, the dotted curves indicate the minimum virial temperature required for efficient cooling with primordial atomic species only (upper curve) or with the addition of molecular hydrogen (lower curve). Figure 18 shows the binding energy of dark matter halos. The binding energy of the baryons is a factor ~ _{b} / _{m} ~ 15% smaller, if they follow the dark matter. Except for this constant factor, the figure shows the minimum amount of energy that needs to be deposited into the gas in order to unbind it from the potential well of the dark matter. For example, the hydrodynamic energy released by a single supernovae, ~ 10^{51} erg, is sufficient to unbind the gas in all 1- halos at z > 5 and in all 2- halos at z > 12.
At z = 5, the halo masses which correspond to 1-, 2-, and 3- fluctuations are 1.8 × 10^{7} M_{}, 3.0 × 10^{10} M_{}, and 7.0 × 10^{11} M_{}, respectively. The corresponding virial temperatures are 2.0 × 10^{3} K, 2.8 × 10^{5} K, and 2.3 × 10^{6} K. The equivalent circular velocities are 7.5 km s^{-1}, 88 km s^{-1}, and 250 km s^{-1}. At z = 10, the 1-, 2-, and 3- fluctuations correspond to halo masses of 1.3 × 10^{3} M_{}, 5.7 × 10^{7} M_{}, and 4.8 × 10^{9} M_{}, respectively. The corresponding virial temperatures are 6.2 K, 7.9 × 10^{3} K, and 1.5 × 10^{5} K. The equivalent circular velocities are 0.41 km s^{-1}, 15 km s^{-1}, and 65 km s^{-1}. Atomic cooling is efficient at T_{vir} > 10^{4} K, or a circular velocity V_{c} > 17 km s^{-1}. This corresponds to a 1.2- fluctuation and a halo mass of 2.1 × 10^{8} M_{} at z = 5, and a 2.1- fluctuation and a halo mass of 8.3 × 10^{7} M_{} at z = 10. Molecular hydrogen provides efficient cooling down to T_{vir} ~ 300 K, or a circular velocity V_{c} ~ 2.9 km s^{-1}. This corresponds to a 0.81- fluctuation and a halo mass of 1.1 × 10^{6} M_{} at z = 5, and a 1.4- fluctuation and a halo mass of 4.3 × 10^{5} M_{} at z = 10.
In Figure 19 we show the halo mass function dn / dln(M) at several different redshifts: z = 0 (solid curve), z = 5 (dotted curve), z = 10 (short-dashed curve), z = 20 (long-dashed curve), and z = 30 (dot-dashed curve). Note that the mass function does not decrease monotonically with redshift at all masses. At the lowest masses, the abundance of halos is higher at z > 0 than at z = 0.
Figure 19. Halo mass function at several redshifts: z = 0 (solid curve), z = 5 (dotted curve), z = 10 (short-dashed curve), z = 20 (long-dashed curve), and z = 30 (dot-dashed curve). |
4.2. The Excursion-Set (Extended Press-Schechter) Formalism
The usual Press-Schechter formalism makes no attempt to deal with the correlations between halos or between different mass scales. In particular, this means that while it can generate a distribution of halos at two different epochs, it says nothing about how particular halos in one epoch are related to those in the second. We therefore would like some method to predict, at least statistically, the growth of individual halos via accretion and mergers. Even restricting ourselves to spherical collapse, such a model must utilize the full spherically-averaged density profile around a particular point. The potential correlations between the mean overdensities at different radii make the statistical description substantially more difficult.
The excursion set formalism (Bond et al. 1991 [52]) seeks to describe the statistics of halos by considering the statistical properties of (R), the average overdensity within some spherical window of characteristic radius R, as a function of R. While the Press-Schechter model depends only on the Gaussian distribution of for one particular R, the excursion set considers all R. Again the connection between a value of the linear regime and the final state is made via the spherical collapse solution, so that there is a critical value _{c} (z) of which is required for collapse at a redshift z.
For most choices of window function, the functions (R) are correlated from one R to another such that it is prohibitively difficult to calculate the desired statistics directly [although Monte Carlo realizations are possible [52]]. However, for one particular choice of a window function, the correlations between different R greatly simplify and many interesting quantities may be calculated [52, 212]. The key is to use a k-space top-hat window function, namely W_{k} = 1 for all k less than some critical k_{c} and W_{k} = 0 for k > k_{c}. This filter has a spatial form of W(r) j_{1} (k_{c} r) / k_{c} r, which implies a volume 6^{2} / k_{c}^{3} or mass 6^{2} _{b} / k_{c}^{3}. The characteristic radius of the filter is ~ k_{c}^{-1}, as expected. Note that in real space, this window function converges very slowly, due only to a sinusoidal oscillation, so the region under study is rather poorly localized.
The great advantage of the sharp k-space filter is that the difference at a given point between on one mass scale and that on another mass scale is statistically independent from the value on the larger mass scale. With a Gaussian random field, each _{k} is Gaussian distributed independently from the others. For this filter,
(93) |
meaning that the overdensity on a particular scale is simply the sum of the random variables _{k} interior to the chosen k_{c}. Consequently, the difference between the (M) on two mass scales is just the sum of the _{k} in the spherical shell between the two k_{c}, which is independent from the sum of the _{k} interior to the smaller k_{c}. Meanwhile, the distribution of (M) given no prior information is still a Gaussian of mean zero and variance
(94) |
If we now consider as a function of scale k_{c}, we see that we begin from = 0 at k_{c} = 0 (M = ) and then add independently random pieces as k_{c} increases. This generates a random walk, albeit one whose stepsize varies with k_{c}. We then assume that, at redshift z, a given function (k_{c} ) represents a collapsed mass M corresponding to the k_{c} where the function first crosses the critical value _{c} (z). With this assumption, we may use the properties of random walks to calculate the evolution of the mass as a function of redshift.
It is now easy to rederive the Press-Schechter mass function, including the previously unexplained factor of 2 [52, 212, 382]. The fraction of mass elements included in halos of mass less than M is just the probability that a random walk remains below _{c} (z) for all k_{c} less than K_{c}, the filter cutoff appropriate to M. This probability must be the complement of the sum of the probabilities that: (a) (K_{c}) > _{c}(z); or that (b) (K_{c}) < _{c}(z) but (k'_{c}) > _{c}(z) for some k'_{c} < K_{c}. But these two cases in fact have equal probability; any random walk belonging to class (a) may be reflected around its first upcrossing of _{c}(z) to produce a walk of class (b), and vice versa. Since the distribution of (K_{c}) is simply Gaussian with variance ^{2}(M), the fraction of random walks falling into class (a) is simply (1 / (2 ^{2})^{1/2}) _{c (z)}^{} d exp{- ^{2} / 2^{2} (M)}. Hence, the fraction of mass elements included in halos of mass less than M at redshift z is simply
(95) |
which may be differentiated to yield the Press-Schechter mass function. We may now go further and consider how halos at one redshift are related to those at another redshift. If we are given that a halo of mass M_{2} exists at redshift z_{2}, then we know that the random function (k_{c}) for each mass element within the halo first crosses (z_{2}) at k_{c2} corresponding to M_{2}. Given this constraint, we may study the distribution of k_{c} where the function (k_{c}) crosses other thresholds. It is particularly easy to construct the probability distribution for when trajectories first cross some _{c} (z_{1}) > _{c} (z_{2}) (implying z_{1} > z_{2}); clearly this occurs at some k_{c1} > k_{c2}. This problem reduces to the previous one if we translate the origin of the random walks from (k_{c}, ) = (0,0) to (k_{c2}, _{c}(z_{2})). We therefore find the distribution of halo masses M_{1} that a mass element finds itself in at redshift z_{1} given that it is part of a larger halo of mass M_{2} at a later redshift z_{2} is [52, 55])
(96) |
This may be rewritten as saying that the quantity
(97) |
is distributed as the positive half of a Gaussian with unit variance; equation (97) may be inverted to find M_{1}().
We seek to interpret the statistics of these random walks as those of merging and accreting halos. For a single halo, we may imagine that as we look back in time, the object breaks into ever smaller pieces, similar to the branching of a tree. Equation (96) is the distribution of the sizes of these branches at some given earlier time. However, using this description of the ensemble distribution to generate Monte Carlo realizations of single merger trees has proven to be difficult. In all cases, one recursively steps back in time, at each step breaking the final object into two or more pieces. An elaborate scheme (Kauffmann & White 1993 [195]) picks a large number of progenitors from the ensemble distribution and then randomly groups them into sets with the correct total mass. This generates many (hundreds) possible branching schemes of equal likelihood. A simpler scheme (Lacey & Cole 1993 [212]) assumes that at each time step, the object breaks into two pieces. One value from the distribution (96) then determines the mass ratio of the two branchs.
One may also use the distribution of the ensemble to derive some additional analytic results. A useful example is the distribution of the epoch at which an object that has mass M_{2} at redshift z_{2} has accumulated half of its mass [212]. The probability that the formation time is earlier than z_{1} is equal to the probability that at redshift z_{1} a progenitor whose mass exceeds M_{2}/2 exists:
(98) |
where dP / dM is given in equation (96). The factor of M_{2} / M corrects the counting from mass weighted to number weighted; each halo of mass M_{2} can have only one progenitor of mass greater than M_{2} / 2. Differentiating equation (98) with respect to time gives the distribution of formation times. This analytic form is an excellent match to scale-free N-body simulations [213]. On the other hand, simple Monte Carlo implementations of equation (96) produce formation redshifts about 40% higher [212]. As there may be correlations between the various branches, there is no unique Monte Carlo scheme.
Numerical tests of the excursion set formalism are quite encouraging. Its predictions for merger rates are in very good agreement with those measured in scale-free N-body simulations for mass scales down to around 10% of the nonlinear mass scale (that scale at which _{m} = 1), and distributions of formation times closely match the analytic predictions [213]. The model appears to be a promising method for tracking the merging of halos, with many applications to cluster and galaxy formation modeling. In particular, one may use the formalism as the foundation of semi-analytic galaxy formation models [196]. The excursion set formalism may also be used to derive the correlations of halos in the nonlinear regime [258].
4.3. Response of Baryons to Nonlinear Dark Matter Potentials
The dark matter is assumed to be cold and to dominate gravity, and so its collapse and virialization proceeds unimpeded by pressure effects. In order to estimate the minimum mass of baryonic objects, we must go beyond linear perturbation theory and examine the baryonic mass that can accrete into the final gravitational potential well of the dark matter.
For this purpose, we assume that the dark matter had already virialized and produced a gravitational potential (r) at a redshift z_{vir} (with 0 at large distances, and < 0 inside the object) and calculate the resulting overdensity in the gas distribution, ignoring cooling (an assumption justified by spherical collapse simulations which indicate that cooling becomes important only after virialization; see Haiman et al. 1996 [168]).
After the gas settles into the dark matter potential well, it satisfies the hydrostatic equilibrium equation,
(99) |
where p_{b} and _{b} are the pressure and mass density of the gas. At z < 100 the gas temperature is decoupled from the CMB, and its pressure evolves adiabatically (ignoring atomic or molecular cooling),
(100) |
where a bar denotes the background conditions. We substitute equation (100) into (99) and get the solution,
(101) |
where = _{b} µ m_{p} / (k _{b}) is the background gas temperature. If we define T_{vir}= -1/3m_{p} / k as the virial temperature for a potential depth -, then the overdensity of the baryons at the virialization redshift is
(102) |
This solution is approximate for two reasons: (i) we assumed that the gas is stationary throughout the entire region and ignored the transitions to infall and the Hubble expansion at the interface between the collapsed object and the background intergalactic medium (henceforth IGM), and (ii) we ignored entropy production at the virialization shock surrounding the object. Nevertheless, the result should provide a better estimate for the minimum mass of collapsed baryonic objects than the Jeans mass does, since it incorporates the nonlinear potential of the dark matter.
We may define the threshold for the collapse of baryons by the criterion that their mean overdensity, _{b}, exceeds a value of 100, amounting to > 50% of the baryons that would assemble in the absence of gas pressure, according to the spherical top-hat collapse model. Equation (102) then implies that T_{vir} > 17.2 .
As mentioned before, the gas temperature evolves at z < 160 according to the relation 170 [(1 + z) / 100]^{2} K. This implies that baryons are overdense by _{b} > 100 only inside halos with a virial temperature T_{vir} > 2.9 × 10^{3} [(1 + z) / 100]^{2} K. Based on the top-hat model, this implies a minimum halo mass for baryonic objects of
(103) |
where we consider sufficiently high redshifts so that _{m}^{z} 1. This minimum mass is coincidentally almost identical to the naive Jeans mass calculation of linear theory in equation (62) despite the fact that it incorporates shell crossing by the dark matter, which is not accounted for by linear theory. Unlike the Jeans mass, the minimum mass depends on the choice for an overdensity threshold [taken arbitrarily as _{b} > 100 in equation (103)]. To estimate the minimum halo mass which produces any significant accretion we set, e.g., _{b} = 5, and get a mass which is lower than M_{min} by a factor of 27.
Of course, once the first stars and quasars form they heat the surrounding IGM by either outflows or radiation. As a result, the Jeans mass which is relevant for the formation of new objects changes [148, 152]). The most dramatic change occurs when the IGM is photo-ionized and is consequently heated to a temperature of ~ (1 - 2) × 10^{4} K.