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A simple analysis of cosmological perturbations can be obtained from a consideration of the Newtonian approximation to a homogeneous and isotropic universe. Consider a test particle at radius R from an arbitrary center. Because the model is homogeneous the choice of center does not matter. The evolution of the velocity of the test particle is given by the energy equation

Equation 1 (1.1)

If the total energy Etot is positive, the Universe will expand forever since M, the mass (plus energy) enclosed within R, is positive, G is positive, and R is positive. In the absence of a cosmological constant or "dark energy," the expansion of the Universe will stop, leading to a recollapse if Etot is negative. But this simple connection between Etot and the fate of the Universe is broken in the presence of a vacuum energy density. The mass M is proportional to R3 because the Universe is homogeneous and the Hubble velocity upsilon is given by upsilon = HR. Thus Etot propto R2.

We can find the total energy by plugging in the velocity upsilon0 = H0 R0 and the density rho0 in the Universe now. This gives

Equation 2 (1.2)

with the critical density at time t0 being rhocrit = 3 H02 / (8pi G). We define the ratio of density to critical density as Omega = rho / rhocrit. This Omega includes all forms of matter and energy. Omegam will be used to refer to the matter density.

From Equation 1.1 we can compute the time variation of Omega. Let

Equation 3 (1.3)

If we divide this equation by 8pi G rho R2/3 we get

Equation 4 (1.4)

Thus Omega-1 - 1 propto (rho R2)-1. When rho declines with expansion at a rate faster than R-2 then the deviation of Omega from unity grows with expansion. This is the situation during the matter-dominated epoch with rho propto R-3, so Omega-1 - 1 propto R. During the radiation-dominated epoch rho propto R-4, so Omega-1 - 1 propto R2. For Omega0 to be within 0.9 and 1.1, Omega needed to be between 0.999 and 1.001 at the epoch of recombination, and within 10-15 of unity during nucleosynthesis. This fine-tuning problem is an aspect of the "flatness-oldness" problem in cosmology.

Inflation produces such a huge expansion that quantum fluctuations on the microscopic scale can grow to be larger than the observable Universe. These perturbations can be the seeds of structure formation and also will create the anisotropies seen by COBE for spherical harmonic indices ell geq 2. For perturbations that are larger than ~ cs t (or ~ cs / H) we can ignore pressure gradients, since pressure gradients produce sound waves that are not able to cross the perturbation in a Hubble time. In the absence of pressure gradients, the density perturbation will evolve in the same way that a homogeneous universe does, and we can use the equation

Equation 5 (1.5)

the assumption that Omega approx 1 for early times, and Delta rho << rho as indicated by the smallness of the DeltaT's seen by COBE, to derive

Equation 6 (1.6)


Equation 7 (1.7)

where L is the comoving size of the perturbation. This is independent of the scale factor, so it does not change due to the expansion of the Universe.

During inflation (Guth 2003), the Universe is approximately in a steady state with constant H. Thus, the magnitude of Deltaphi for perturbations with physical scale c/H will be the same for all times during the inflationary epoch. But since this constant physical scale is aL, and the scale factor a changes by more than 30 orders of magnitude during inflation, this means that the magnitude of Deltaphi will be the same over 30 decades of comoving scale L. Thus, we get a strong prediction that Deltaphi will be the same on all observable scales from c / H0 down to the scale that is no longer always larger than the sound speed horizon. This means that

Equation 8 (1.8)

so the Universe becomes extremely homogeneous on large scales even though it is quite inhomogeneous on small scales.

This behavior of Deltaphi being independent of scale is called equal power on all scales. It was originally predicted by Harrison (1970), Zel'dovich (1972), and Peebles & Yu (1970) based on a very simple argument: there is no scale length provided by the early Universe, and thus the perturbations should be scale-free - a power law. Therefore Deltaphi propto Lm. The gravitational potential divided by c2 is a component of the metric, and if it gets comparable to unity then wild things happen. If m < 0 then Deltaphi gets large for small L, and many black holes would form. But we observe that this did not happen. Therefore m geq 0. But if m > 0 then Deltaphi gets large on large scales, and the Universe would be grossly inhomogeneous. But we observe that this is not the case, so m leq 0. Combining both results requires that m = 0, which is a scale-invariant perturbation power spectrum. This particular power-law power spectrum is called the Harrison-Zel'dovich spectrum. It was expected that the primordial perturbations should follow a Harrison-Zel'dovich spectrum because all other answers were wrong, but the inflationary scenario provides a good mechanism for producing a Harrison-Zel'dovich spectrum.

Sachs & Wolfe (1967) show that a gravitational potential perturbation produces an anisotropy of the CMB with magnitude

Equation 9 (1.9)

where Deltaphi is evaluated at the intersection of the line-of-sight and the surface of last scattering (or recombination at z approx 1100). The (1/3) factor arises because clocks run faster by a factor (1 + phi / c2) in a gravitational potential, and we can consider the expansion of the Universe to be a clock. Since the scale factor is varying as a propto t2/3 at recombination, the faster expansion leads to a decreased temperature by DeltaT / T = -(2/3)Deltaphi / c2, which, when added to the normal gravitational redshift DeltaT / T = Deltaphi / c2 yields the (1/3) factor above. This is an illustration of the "gauge" problem in calculating perturbations in general relativity. The expected variation of the density contrast as the square of the scale factor for scales larger than the horizon in the radiation-dominated epoch is only obtained after allowance is made for the effect of the potential on the time. For a plane wave with wavenumber k we have -k2 Deltaphi = 4pi G Delta rho, or

Equation 10 (1.10)

so when rho approx rhocrit at recombination, the Sachs-Wolfe effect exceeds the physical temperature fluctuation DeltaT / T = (1/4)Delta rho / rho by a factor of 2(H / ck)2 if fluctuations are adiabatic (all component number densities varying by the same factor).

In addition to the physical temperature fluctuation and the gravitational potential fluctuation, there is a Doppler shift term. When the baryon fluid has a density contrast given by

Equation 11 (1.11)

where cs is the sound speed, then

Equation 12 (1.12)

As a result the velocity perturbation is given by upsilon = -csdeltab. But the sound speed is given by cs = (partialP / partialrho)1/2 = [(4/3)rhogammac2 / (3rhob + 4rhogamma)]1/2 approx c / 31/2, since rhogamma > rhob at recombination (z = 1100). But the photon density is only slightly higher than the baryon density at recombination so the sound speed is about 20% smaller than c / 31/2. The Doppler shift term in the anisotropy is given by DeltaT / T = upsilon / c, as expected. This results in DeltaT / T slightly less than deltab / 31/2, which is nearly 31/2 larger than the physical temperature fluctuation given by DeltaT / T = deltab / 3.

These plane-wave calculations need to be projected onto the sphere that is the intersection of our past light cone and the hypersurface corresponding to the time of recombination. Figure 1.2 shows a plane wave on these surfaces. The scalar density and potential perturbations produce a different pattern on the observed sky than the vector velocity perturbation. Figure 1.3 shows these patterns on the sky for a plane wave with k RLS = 50, where RLS is the radius of the last-scattering surface. The contribution of the velocity term is multiplied by costheta, and since the RMS of this over the sphere is (1/3)1/2, the RMS contribution of the velocity term almost equals the RMS contribution from the density term since the speed of sound is almost c/31/2.

Figure 2a Figure 2b

Figure 1.2. A plane wave on the last-scattering hypersurface. Right: The spherical intersection with our past light cone is shown.

Figure 3a Figure 3b

Figure 1.3. Left: The scalar density and potential perturbation. Right: The vector velocity perturbation.

The anisotropy is usually expanded in spherical harmonics:

Equation 13 (1.13)

Because the Universe is approximately isotropic the probability densities for all the different m's at a given ell are identical. Furthermore, the expected value of DeltaT(hat{n}) is obviously zero, and thus the expected values of the aell m's is zero. But the variance of the aell m's is a measurable function of ell, defined as

Equation 14 (1.14)

Note that in this normalization Cell and aell m are dimensionless. The harmonic index ell associated with an angular scale theta is given by ell approx 180° / theta, but the total number of spherical harmonics contributing to the anisotropy power at angular scale theta is given by Delta ell approx ell times 2ell + 1. Thus to have equal power on all scales one needs to have approximately Cell propto ell-2. Given that the square of the angular momentum operator is actually ell(ell + 1), it is not surprising that the actual angular power spectrum of the CMB predicted by "equal power on all scales" is

Equation 15 (1.15)

where < Q2 > or Q2rms-PS is the expected variance of the ell = 2 component of the sky, which must be divided by T02 because the aell m's are defined to be dimensionless. The "4pi" term arises because the mean of |Yell m|2 is 1 / (4pi), so the |aell m|2's must be 4pi times larger to compensate. Finally, the quadrupole has 5 components, while Cell is the variance of a single component, giving the "5" in the denominator. The COBE DMR experiment determined (< Q2 >)1/2 = 18 µK, and that the Cell's from ell = 2 to ell = 20 were consistent with Equation 1.15.

The other common way of describing the anisotropy is in terms of

Equation 16 (1.16)

Note these definitions give Delta T22 = 2.4 < Q2 >. Therefore, the COBE normalized Harrison-Zel'dovich spectrum has Delta Tell2 = 2.4 × 182 = 778 µK2 for ell leq 20.

It is important to realize that the relationship between the wavenumber k and the spherical harmonic index ell is not a simple ell = k RLS. Figure 1.3 shows that while ell = k RLS at the "equator" the poles have lower ell. In fact, if µ = costheta, where theta is the angle between the wave vector and the line-of-sight, then the "local ell" is given by k RLS (1 - µ2)1/2. The average of this over the sphere is < ell > = (pi / 4) k RLS. For the velocity term the power goes to zero when µ = 0 on the equator, so the average ell is smaller, < ell > = (3pi / 16) k RLS, and the distribution of power over ell lacks the sharp cusp at ell = k RLS. As a result the velocity term, while contributing about 60% as much to the RMS anisotropy as the density term, does not contribute this much to the peak structure in the angular power spectrum. Thus the old nomenclature of "Doppler" peaks was not appropriate, and the new usage of "acoustic" peaks is more correct. Figure 1.4 shows the angular power spectrum from single k skies for both the density and velocity terms for several values of k, and a graph of the variance-weighted mean ell vs. kRLS. These curves were computed numerically but have the expected forms given by the spherical Bessel function jell for the density term and jell' for the velocity term.

Figure 4a Figure 4b

Figure 1.4. Left: Mean ell is plotted vs. the wavenumber kRLS. The light solid line shows ell = kRLS, while the solid line shows ell = (pi / 4)kRLS, and the dashed line shows ell = (3pi / 16)kRLS. Right: The angular power spectra for single k skies is plotted for kRLS = 25, 50, and 100 (from bottom to top).

Seljak (1994) considered a simple model in which the photons and baryons are locked together before recombination, and completely noninteracting after recombination. Thus the opacity went from infinity to zero instantaneously. Prior to recombination there were two fluids, the photon-baryon fluid and the CDM fluid, which interacted only gravitationally. The baryon-photon fluid has a sound speed of about c / 31/2 while the dark matter fluid has a sound speed of zero. Figure 1.5 shows a conformal spacetime diagrams with a traveling wave in the baryon-photon fluid and the stationary wave in the CDM. The CDM dominates the potential, so the large-scale structure (LSS) forms in the potential wells defined by the CDM.

Figure 5a Figure 5b

Figure 1.5. On the left a conformal spacetime diagram showing a traveling wave in the baryon-photon fluid. On the right, the stationary CDM wave and the world lines of matter falling into the potential wells. For this wavenumber the density contrast in the baryon-photon fluid has undergone one-half cycle of its oscillation and is thus in phase with the Sachs-Wolfe effect from the CDM. This condition defines the first acoustic peak.

In Seljak's simple two-fluid model, there are five variables to follow: the density contrast in the CDM and baryons, deltac and deltab, the velocities of these fluids upsilonc and upsilonb, and the potential phi. The photon density contrast is (4/3)deltab. In Figure 1.6 the density contrasts are plotted vs. the scale factor for several values of the wavenumber. To make this plot the density contrasts were adjusted for the effect of the potential on the time, with

Equation 17 (1.17)


Equation 18 (1.18)

Remembering that phi is negative when delta is positive, the two terms on the right-hand side of the above equations cancel almost entirely at early times, leaving a small residual growing like a2 prior to aeq, the scale factor when the matter density and the radiation density were equal. Thus these adjusted density contrasts evolve like Omega-1-1 in homogeneous universes.

Figure 6

Figure 1.6. Density contrasts in the CDM and the photons for wavenumbers kappa = 5, 20, and 80 (see Fig. 1.7) as a function of the scale factor relative to the scale factor and when matter and radiation densities were equal. The photon density contrast starts out slightly larger than the CDM density contrast but oscillates.

Figure 1.7 shows the potential that survives to recombination and produces LSS, the potential plus density effect on the CMB temperature, and the velocity of the baryons as function of wavenumber. Close scrutiny of the potential curve in the plot shows the baryonic wiggles in the LSS that may be detectable in the large redshift surveys by the 2dF and SDSS groups.

Figure 7

Figure 1.7. Density contrasts at recombination as a function of wavenumber kappa. The arrows on the x-axis indicate the values of kappa for delta vs. a, as plotted in Figure 1.6. The solid curve shows the potential (the initial potential is always phi = 1), the long dashed curve curve shows the combined potential plus density effect on the CMB temperature, while the short dashed curve shows the velocity of the baryon-photon fluid.

A careful examination of the angular power spectrum allows several cosmological parameters to be derived. The baryon to photon ratio and the dark matter to baryon density ratio can both be derived from the amplitudes of the first two acoustic peaks. Since the photon density is known precisely, the peak amplitudes determine the baryon density omegab = Omegab h2 and the cold dark matter density omegac = OmegaCDM h2. The matter density is given by Omegam = Omegab + OmegaCDM. The amplitude < Q2 > and spectral index n of the primordial density perturbations are also easily observed. Finally the angular scale of the peaks depends on the ratio of the angular size distance at recombination to the distance sound can travel before recombination. Since the speed of sound is close to c / 31/2, this sound travel distance is primarily affected by the age of the Universe at z = 1100. The age of the Universe goes like t propto rho-1/2 propto Omegam-1/2 h-1. The angular size distance is proportional to h-1 as well, so the Hubble constant cancels out. The angular size distance is almost proportional to Omegam-1/2, but this relation is not quite exact. Figure 1.8 compares the angular size distance to Omegam-1/2. One sees that a peak position that corresponds to Omegatot = 0.95 if Omegam = 0.2 can also be fit by Omegatot = 1.1 if Omegam = 1. Thus, to first order the peak position is a good measure of Omegatot.

Figure 8

Figure 1.8. The angular size distance vs. Omegam for H0 = 60 km s-1 Mpc-1 and three different values of Omegatot (0.9, 1, and 1.1, from top to bottom). The dashed curve shows Omegam-1/2.

The CMBFAST code by Seljak & Zaldarriaga (1996) provides the ability to quickly compute the angular power spectrum Cell. Typically CMBFAST runs in about 1 minute for a given set of cosmological parameters. However, two different groups have developed even faster methods to evaluate Cell. Kaplinghat, Knox, & Skordis (2002) have published the Davis Anisotropy Shortcut (DASh), with code available for download. This program interpolates among precomputed Cell's. Kosowsky, Milosavljevic, & Jiminez (2002) discuss combinations of the parameters that produce simple changes in the power spectrum, and also allow accurate and fast interpolation between Cell's. These shortcuts allow the computation of a Cell from model parameters in about 1 second. This allows the rapid computation of the likelihood of a given data set D for a set of model parameters M, L(D|M). When computing the likelihood for high signal-to-noise ratio observations of a small area of the sky, biases due to the non-Gaussian shape of the likelihood are common. This can be avoided using the offset log-normal form for the likelihood L(Cell) advocated by Bond, Jaffe, & Knox (2000).

Table 1.1. Beam Size and Calibration Corrections

Experiment thetab (') 100(Deltatheta) / theta 100(Delta dT) / dT

COBE 420.0 -0.3 ...
ARCHEOPS 15.0 -0.2 2.7
BOOMERanG 12.9 10.5 -7.6
MAXIMA 10.0 -0.9 0.2
DASI 5.0 -0.4 0.7
VSA 3.0 -0.5 -1.7
CBI 1.5 -1.1 -0.2

The likelihood is a probability distribution over the data, so integ L(D|M) dD = 1 for any M. It is not a probability distribution over the models, so one should never attempt to evaluate integL dM. For example, one could consider the likelihood as a function of the model parameters H0 in km s-1 Mpc-1 and Omegam for flat LambdaCDM models, or one could use the parameters t0 in seconds and Omegam. For any (H0, Omegam) there is a corresponding (t0, Omegam) that makes exactly the same predictions, and therefore gives the same likelihood. But the integral of the likelihood over dt0 dOmegam will be much larger than the integral of the likelihood over dH0 dOmegam just because of the Jacobian of the transformation between the different parameter sets.

Wright (1994) gave the example of determining the primordial power spectrum power-law index n, P(k) = A(k / k0)n. Marginalizing over the amplitude by integrating the likelihood over A gives very different results for different values of k0. Thus, it is very unfortunate that Hu & Dodelson (2002) still accept integration over the likelihood.

Instead of integrating over the likelihood one needs to define the a posteriori probability of the models pf(M) based on an a priori distribution pi(M) and Bayes' theorem:

Equation 19 (1.19)

It is allowable to integrate pf over the space of models because the prior will transform when changing variables so as to keep the integral invariant.

In the modeling reported here, the a priori distribution is chosen to be uniform in omegab, omegac, n, OmegaV, Omegatot, and zri. In doing the fits, the model Cell's are adjusted by a factor of exp[a + b ell(ell + 1)] before comparison with the data. Here a is a calibration adjustment, and b is a beam size correction that assumes a Gaussian beam. For COBE, a is the overall amplitude scaling parameter instead of a calibration correction. Marginalization over the calibration and beam size corrections for each experiment, and the overall spectral amplitude, is done by maximizing the likelihood, not by integrating the likelihood. Table 1.1 gives these beam and calibration corrections for each experiments. All of these corrections are less than the quoted uncertainties for these experiments. BOOMERanG stands out in the table for having honestly reported its uncertainties: ± 11% for the beam size and ± 10% for the gain. The likelihood is given by

Equation 20 (1.20)

where j indexes over experiments, i indexes over points within each experiment, Z = ln(Cell + Nell) in the offset log normal approach of Bond et al. (2000), and Nell is the noise bias. Since for COBE a is the overall normalization, σ(a) is set to infinity for this term to eliminate it from the likelihood. The function f(x) is x2 for small |x| but switches to 4(|x| - 1) when |x| > 2. This downweighs outliers in the data. Most of the experiments have double tabulated their data. I have used both the even and odd points in my fits, but I have multiplied the sigma's by 21/2 to compensate. Thus, I expect to get chi2 per degree of freedom close to 0.5 but should have the correct sensitivity to cosmic parameters.

Table 1.2. Cosmic Parameters from pre-WMAP CMB Data only

Parameter Mean sigma Units

omegab 0.0206 0.0020  
omegac 0.1343 0.0221  
OmegaV 0.3947 0.2083  
Omegak -0.0506 0.0560  
zri 7.58 3.97  
n 0.9409 0.0379  
H0 51.78 12.26 km s-1 Mpc-1
t0 15.34 1.60 Gyr
Gamma 0.2600 0.0498  

The scientific results such as the mean values and the covariance matrix of the parameters can be determined by integrations over parameter space weighted by pf. Table 1.2 shows the mean and standard deviation of the parameters determined by integrating over the a posteriori probability distribution of the models. The evaluation of integrals over multi-dimensional spaces can require a large number of function evaluations when the dimensionality of the model space is large, so a Monte Carlo approach can be used. To achieve an accuracy of O(epsilon) in a Monte Carlo integration requires O(epsilon-2) function evaluations, while achieving the same accuracy with a gridding approach requires O(epsilon-n/2) evaluations when second-order methods are applied on each axis. The Monte Carlo approach is more efficient for more than four dimensions. When the CMB data get better, the likelihood gets more and more sharply peaked as a function of the parameters, so a Gaussian approximation to L(M) becomes more accurate, and concerns about banana-shaped confidence intervals and long tails in the likelihood are reduced. The Monte Carlo Markov Chain (MCMC) approach using the Metropolis-Hastings algorithm to generate models drawn from pf is a relatively fast way to evaluate these integrals (Lewis & Bridle 2002). In the MCMC, a "trial" set of parameters is sampled from the proposal density pt(P';P), where P is the current location in parameter space, and P' is the new location. Then the trial location is accepted with a probability given by

Equation 21 (1.21)

When a trial is accepted the Markov chain one sets P = P'. This algorithm guarantees that the accepted points in parameter space are sampled from the a posteriori probability distribution.

The most common choice for the proposal density is one that depends only on the parameter change P'- P. If the proposal density is a symmetric function then the ratio pt(P;P') / ptP';P) = 1 and lambda is then just the ratio of a posteriori probabilities. But the most efficient choice for the proposal density is pf(P) which is not a function of the parameter change, because this choice makes lambda = 1 and all trials are accepted. However, if one knew how to sample models from pf, why waste time calculating the likelihoods?

Just plotting the cloud of points from MCMC gives a useful indication of the allowable parameter ranges that are consistent with the data. I have done some MCMC calculations using the DASh (Kaplinghat et al. 2002) to find the Cell's. I found DASh to be user unfriendly and too likely to terminate instead of reporting an error for out-of-bounds parameter sets, but it was fast. Figure 1.9 shows the range of baryon and CDM densities consistent with the CMB data set from COBE (Bennett et al. 1996), ARCHEOPS (Amblard 2003), BOOMERanG (Netterfield et al. 2002), MAXIMA (Lee et al. 2001), DASI (Halverson et al. 2002), VSA (Scott et al. 2003), and CBI (Pearson et al. 2003), and the range of matter and vacuum densities consistent with these data. The Hubble constant is strongly correlated with position on this diagram. Figure 1.10 shows the distribution of t0 for models consistent with this pre-WMAP CMB data set. The relative uncertainty in t0 is much smaller than the relative uncertainty in H0 because the low-H0 models have low vacuum energy density (OmegaV), and thus low values of the product H0 t0. The CMB data are giving a reasonable value for t0 without using information on the distances or ages of objects, which is an interesting confirmation of the Big Bang model.

Figure 9a Figure 9b

Figure 1.9. Left: Clouds of models drawn from the a posteriori distribution based on the CMB data set as of 19 November 2002. The gray band shows the Big Bang nucleosynthesis determination of omegab (± 2sigma) from Burles, Nollett, & Turner (2001). Right: the same set of models in the Omegam, OmegaLambda plane.

Figure 10

Figure 1.10. Distribution of the age of the Universe based only on the pre-WMAP CMB data.

Peacock & Dodds (1994) define a shape parameter for the observed LSS power spectrum, Gamma = Omegam h exp(-2Omegab). There are other slightly different definitions of Gamma in use, but this will be used consistently here. Peacock & Dodds determine Gamma = 0.255 ± 0.017 + 0.32(n-1-1). The CMB data specify n, so the slope correction in the last term is only 0.020 ± 0.013. Hence, the LSS power spectrum wants Gamma = 0.275 ± 0.02. The models based only on the pre-WMAP CMB data give the distribution in Gamma shown in Figure 1.11, which is clearly consistent with the LSS data.

Figure 11

Figure 1.11. Distribution of the LSS power spectrum shape parameter Gamma = Omegam h exp(-2Omegab) from the pre-WMAP CMB data.

Two examples of flat (Omegatot = 1) models with equal power on all scales (n = 1), plotted on the pre-WMAP data set, are shown in Figure 1.12. Both these models are acceptable fits, but the LambdaCDM model is somewhat favored based on the positions of the peaks. The rise in Cell at low ell for the LambdaCDM model is caused by the late integrated Sachs-Wolfe effect, which is due to the changing potential that occurs for z < 1 in this model. The potential changes because the density contrast stops growing when Lambda dominates while the Universe continues to expand at an accelerating rate. The potential change during a photon's passage through a structure produces a temperature change given by DeltaT / T = 2Deltaphi / c2 (Fig. 1.13). The factor of 2 is the same factor of 2 that enters into the gravitational deflection of starlight by the Sun. The effect should be correlated with LSS that we can see at z approx 0.6. Boughn & Crittenden (2003) have looked for this correlation using COBE maps compared to radio source count maps from the NVSS, and Boughn, Crittenden, & Koehrsen (2002) have looked at the correlation of COBE and the X-ray background. As of now the correlation has not been seen, which is an area of concern for LambdaCDM, since the (non)correlation implies OmegaLambda = 0 ± 0.33 with roughly Gaussian errors. This correlation should arise primarily from redshifts near z = 0.6, as shown in Figure 1.14. The coming availability of LSS maps based on deep all-sky infrared surveys (Maller 2003) should allow a better search for this correlation.

Figure 12

Figure 1.12. Two flat n = 1 models. One shows LambdaCDM with OmegaLambda = 2/3. The best fit gives omegab = 0.022 and omegac = 0.132, implying H0 = 68 km s-1 Mpc-1. The other fit shows OmegaLambda = 0 with omegab = 0.021 and omegac = 0.196, implying H0 = 47 km s-1 Mpc-1.

Figure 13

Figure 1.13. Fading potentials cause large-scale anisotropy correlated with LSS due to the late integrated Sachs-Wolfe effect.

Figure 14

Figure 1.14. Change of potential vs. redshift in a LambdaCDM model. Note that the most significant changes occur near z = 0.6.

In addition to the late integrated Sachs-Wolfe effect from Lambda, reionization should also enhance Cell at low ell, as would an admixture of tensor waves. Since Lambda, tauri and T / S all increase Cell at low ell, and this increase is not seen, one has an upper limit on a weighted sum of all these parameters. If Lambda is finally detected by the correlation between improved CMB and LSS maps, or if a substantial tauri, such as the tau = 0.1 predicted by Cen (2003), is detected by the correlation between the E-mode polarization and the anisotropy (Zaldarriaga 2003), then one gets a greatly strengthened limit on tensor waves.

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