A&A 459, 375-389 (2006)
DOI: 10.1051/0004-6361:20065377
G. Hütsi^{1,2}
1 - Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, 86740 Garching bei München, Germany
2 - Tartu Observatory, Tõravere 61602, Estonia
Received 6 April 2006 / Accepted 21 August 2006
Abstract
In this paper we determine the constraints on cosmological parameters using the CMB data from the W MAP experiment together with the recent power spectrum measurement of the SDSS Luminous Red Galaxies (LRGs). Specifically, we focus on spatially flat, low matter density models with adiabatic Gaussian initial conditions. The spatial flatness is achieved with an additional quintessence component whose equation of state parameter
is taken to be independent of redshift. Throughout most of the paper we do not allow any massive neutrino contribution and also the influence of the gravitational waves on the CMB is taken to be negligible. The analysis is carried out separately for two cases: (i) using the acoustic scale measurements as presented in Hütsi (2006, A&A, 449, 891), (ii) using the full SDSS LRG power spectrum and its covariance matrix. We are able to obtain a very tight constraint on the Hubble constant:
H_{0} = 70.8 ^{+2.1}_{-2.0} km s^{-1} Mpc^{-1}, which helps in breaking several degeneracies between the parameters and allows us to determine the low redshift expansion law with much higher accuracy than available from the W MAP + HST data alone. The positive deceleration parameter q_{0} is found to be ruled out at
confidence level. Finally, we extend our analysis by investigating the effects of relaxing the assumption of spatial flatness and also allow for a contribution from massive neutrinos.
Key words: cosmology: cosmological parameters - large-scale structure of Universe - cosmic microwave background
Since the flight of the C OBE^{} satellite in the beginning of 90's the field of observational cosmology has witnessed an extremely rapid development. The data from various Cosmic Microwave Background (CMB) experiments (W MAP^{} (Bennett et al. 2003); C OBE (Smoot et al. 1992); A RCHEOPS^{} (Benoît et al. 2003); B OOMERANG^{} (Netterfield et al. 2002); M AXIMA^{} (Hanany et al. 2000); C BI^{} (Pearson et al. 2003); V SA^{} (Scott et al. 2003); D ASI^{} (Halverson et al. 2002) etc.); supernova surveys (S CP^{} (Perlmutter et al. 1999), High-Z SN Search^{} (Riess et al. 1998)) and large galaxy redshift surveys (SDSS^{} (York et al. 2000), 2dFGRS^{} (Colless et al. 2001)) has lead us to the cosmological model that is able to accommodate almost all the available high quality data - the so-called "concordance'' model (Spergel et al. 2003; Bahcall et al. 1999). Useful cosmological information has also been obtained from the Ly- forest, weak lensing, galaxy cluster, and large-scale peculiar velocity studies. It is remarkable that this diversity of observational data can be fully explained by a cosmological model that in its simplest form has only 5-6 free parameters (Tegmark et al. 2004; Liddle 2004). As the future data sets will be orders of magnitude larger, leading to the extremely small statistical errors, any further progress is possible only in case we fully understand various systematic uncertainties that could potentially bias our conclusions about the underlying cosmology. As such, one should try to use observables that are least sensitive to the theoretical uncertainties, contaminating foregrounds etc. Currently the "cleanest'' constraints on cosmological models are provided by the measurements of the angular power spectrum of the CMB. Since the underlying linear physics is well understood (see e.g. Dodelson 2003; Hu 1995) we have a good knowledge of how the angular position and amplitude ratios of the acoustic peaks depend on various cosmological parameters. However, the CMB data alone is able to provide accurate measurements of only a few combinations of the cosmological parameters. In order to break the degeneracies between the parameters one has to complement the CMB data with additional information from other independent sources e.g. the data from the type Ia supernovae, large-scale structure, or the Hubble parameter measurements. In fact, the well understood physical processes responsible for the prominent peak structure in the CMB angular power spectrum are also predicted to leave imprints on the large-scale matter distribution. Recently the analysis of the spatial two-point correlation function of the Sloan Digital Sky Survey (SDSS) Luminous Red Galaxy (LRG) sample (Eisenstein et al. 2005), and power spectra of the 2dF (Cole et al. 2005) and SDSS LRG (Hütsi 2006) redshift samples, have lead to the detection of these acoustic features, providing the clearest support for the gravitational instability picture, where the large-scale structure of the Universe is believed to arise through the gravitational amplification of the density fluctuations laid down in the very early Universe.
In the current paper we work out the constraints on cosmological parameters using the SDSS LRG power spectrum as determined by Hütsi (2006). In order to break the degeneracies between the parameters we complement our analysis with the data from other cosmological sources: the CMB data from the W MAP, and the measurement of the Hubble parameter by the HST Key Project^{}. We focus our attention on simple models with Gaussian adiabatic initial conditions. In the initial phase of the analysis we further assume spatial flatness, and also negligible massive neutrino and gravitational wave contributions. This leads us to the models with 6 free parameters: total matter and baryonic matter density parameters: and , the Hubble parameter h, the optical depth to the last-scattering surface , the amplitude and spectral index of the scalar perturbation spectrum^{}. This minimal set is extended with the constant dark energy effective equation of state parameter . We carry out our analysis in two parts. In the first part we use only the measurement of the acoustic scale from the SDSS LRG power spectrum as given in Hütsi (2006). The analysis in the second part uses the full power spectrum measurement along with the covariance matrix as provided by Hütsi (2006). Here we add two extra parameters: bias parameter b and parameter Q that describes the deformation of the linear power spectrum to the nonlinear redshift-space spectrum. These extra parameters are treated as nuissance parameters and are marginalized over. Thus the largest parameter space we should cope with is 9-dimensional^{}. Since the parameter space is relatively high dimensional it is natural to use Markov Chain Monte Carlo (MCMC) techniques. For this purpose we use publicly available cosmological MCMC engine C OSMOMC^{} (Lewis & Bridle 2002). All the CMB spectra and matter transfer functions are calculated using the fast Boltzmann code C AMB^{} (Lewis et al. 2000).
The paper is organized as follows. In Sect. 2 we describe the observational data used for the parameter estimation. Section 3 discusses and tests the accuracy of the transformations needed to convert the linear input spectrum to the observed redshift-space galaxy power spectrum. In Sect. 4 we present the main results of the cosmological parameter study and we conclude in Sect. 5.
Figure 1: Upper panel: power spectra in somewhat unconventional form. Here the spectra have been multiplied by an extra factor of k to increase the visibility of details. Filled circles with solid errorbars represent the SDSS LRG power spectrum as determined by Hütsi (2006). The upper data points provide the deconvolved version of the spectrum. The thin solid lines show the best-fitting model spectra. Lower panel: the same spectra as above now plotted in the usual form. | |
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As mentioned in the Introduction, in order to break several degeneracies between the cosmological parameters, we complement the SDSS LRG power spectrum data with the data from the W MAP CMB measurements. Specifically, we use the CMB temperature power spectrum as found in Hinshaw et al. (2003) and the temperature-polarization cross-power as determined by Kogut et al. (2003). The description of the likelihood calculation using this data is given in Verde et al. (2003). We use the Fortran90 version of this likelihood code as provided by the C OSMOMC package.
While investigating the constraints arising from the measurement of the acoustic scale we do not run each time the full new MCMC calculation. Instead we importance sample the chains built for the W MAP data along with the constraint on the Hubble parameter as provided by the HST Key Project, km s^{-1} Mpc^{-1} (Freedman et al. 2001). Using the W MAP data alone would result in too loose constraints on several parameters, and thus after importance sampling a large fraction of the chain elements would get negligible statistical weight, leaving us with too small effective number of samples.
In this section we discuss the relation of the observed galaxy power spectrum to the underlying spectrum of the matter distribution. We stress the need to take into account the so-called cosmological distortion^{}, which almost always is being completely neglected^{}. This is fine for the very shallow surveys, but as we show later, for the samples like the SDSS LRGs, with an effective depth of , the cosmological distortion should certainly be taken into account. This is especially important if power spectrum, instead of being well approximated by a simple power law, contains some characteristic features.
There are other difficulties one has to face while trying to make cosmological inferences using the observed galaxy samples. It is well known that galaxies need not faithfully follow the underlying matter distribution. This phenomenon is known as biasing (Kaiser 1984). Whereas on the largest scales one might expect linear and scale-independent biasing (e.g. Narayanan et al. 2000; Coles 1993), on smaller scales this is definitely not the case. In general the biasing can be scale-dependent, nonlinear, and stochastic (Dekel & Lahav 1999). The other complications involved are the redshift-space distortions and the effects due to nonlinear evolution of the density field. The redshift-space distortions, biasing, and nonlinearities can be approximately treated in the framework of the Halo Model approach as described in Appendix A. The implementation of the Halo Model as presented there introduces four new parameters:
,
the lower cutoff of the halo mass i.e. below that mass halos are assumed to be "dark'';
and M_{0}, the parameters describing the mean of the halo occupation distribution i.e. the average number of galaxies per halo with mass M, which was assumed to have the form
;
,
the parameter describing the amplitude of the virial motions inside the haloes with respect to the isothermal sphere model. This formulation of the Halo Model, along with the assumption of the best-fit W MAP cosmology (Spergel et al. 2003), is able to produce a very good fit to the observed SDSS LRG power spectrum as demonstrated in Fig. 1. Moreover, all the parameters:
,
,
M_{0}, ,
are reasonably well determined. It turns out that to a good approximation these four extra parameters can be compressed down just to a single parameter Q, describing the deformation of the linearly evolved spectrum:
Figure 2: Upper panel: a density plot showing the probability distribution functions for the relative accuracy of the approximation given in Eq. (1) with . The set of Halo Model parameters , , M_{0}, and , needed to calculate the "exact'' spectra, were drawn from the multidimensional Gaussian distribution centered at the best-fit values and with a covariance matrix as found in Hütsi (2006). The heavy dashed lines mark the and quantiles of the relative accuracy distributions. Lower panel: filled circles with solid errorbars provide the SDSS LRG power spectrum. The data points are connected with a smooth cubic spline fit. The other set of lines represents some examples of the pairs of spectra that correspond, starting from below, to the best matching case, to the , and to the quantiles of the distribution of the values. The solid lines show the Halo Model spectra while the dashed ones are the approximations from Eq. (1). | |
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Figure 3: The version of Fig. 2 with . | |
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The cosmological distortion, mentioned in the beginning of this section, arises due to the simple fact that conversion of the observed redshifts to comoving distances requires the specification of the cosmological model. If this cosmology differs from the true one, we are left with additional distortion of distances along and perpendicular to the line of sight. In general, the spatial power spectrum measurements, in contrast to the angular spectra, are model dependent i.e. along with the measurements of the 3D power spectrum one always has to specify the so-called fiducial model used to analyze the data. The fiducial model corresponding to the data shown in Fig. 1 is the best-fit W MAP "concordance'' model (Spergel et al. 2003). In principle, for each of the fitted cosmological model one should redo the full power spectrum analysis to accommodate different distance-redshift relation. However, there is an easier way around: one can find an approximate analytical transformation that describes how the model spectrum should look like under the distance-redshift relation given by the fiducial model i.e. instead of transforming the data points we transform the fitted model spectra. Since the distance intervals along and perpendicular to the line of sight transform differently, the initial isotropic theoretical spectrum P transforms to the 2D spectrum:
(2) |
(3) | |||
(4) |
(5) |
(6) |
(7) |
(8) |
Figure 4: An analog of Fig. 2, here provided in the context of the accuracy test for the cosmological distortion approximation given in Eq. (12). The set of cosmological models was drawn from the combined posterior corresponding to the W MAP plus HST Key Project data. | |
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Figure 5: As Fig. 4, here instead showing the error one makes if cosmological distortion is completely neglected. In the lower panel we have shown only the examples corresponding to the and quantiles. The inset shows the probability distribution function for the "isotropized'' dilation scale, as given in Eq. (11), compatible with the W MAP plus HST Key project constraints. | |
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As a starting point for several subsequent calculations we build a Markov chain using the W MAP temperature-temperature (Hinshaw et al. 2003) and temperature-polarization (Kogut et al. 2003) angular spectra in combination with the constraint on the Hubble parameter from the HST Key Project (Freedman et al. 2001). The results for the 2D marginalized distributions for all of the involved parameter pairs are shown in Fig. 6. Here the and credible regions are shown by solid lines. The original MCMC calculation as performed by the C OSMOMC software uses the variable - the angle subtended by the sound horizon at last scattering - in place of the more common Hubble parameter H_{0}. This leads to the better mixing of the resulting chain since is only weakly correlated with other variables (Kosowsky et al. 2002). The proposal distribution for all of the MCMC calculations carried out in this paper is taken to be a multivariate Gaussian. For the current W MAP + HST case we have used the CMB parameter covariance matrix as provided by the C OSMOMC package. All of the seven default parameters here get implicit flat priors. The marginalized distributions in Fig. 6 are derived from a 100 000-element Markov chain. As there is a very good proposal distribution available the chains typically equilibrate very fast and only a few hundred first elements need to be removed to eliminate the effects of the initial transients. We determine the length of this so-called burn-in period using the Gibbsit^{} software (Raftery & Lewis 1995). The same program can also be used to estimate the length of the Markov chain required to achieve a desired accuracy for the parameter measurements. As a test one can run initially a short chain of a few thousand elements and analyze it with Gibbsit. It turned out that in the current case if we would like to achieve a accuracy at confidence level for the measurement of the and -quantiles of the most poorly sampled parameter, we would need a chain of 25 000 elements. Thus according to this result our 100 000 element chain is certainly more than sufficient. Of course, all the various tools for diagnosing the convergence and for estimating the required chain length^{} are just some more or less justified "recipes'' that can lead to strongly incorrect results, especially in cases of poorly designed proposal distributions. Luckily, in cosmology as we have a very good knowledge about the possible parameter degeneracies, and also as the parameter spaces are relatively low dimensional, the construction of very good samplers is not too difficult.
In the following subsections we use this W MAP + HST chain for the very fast determination of the parameter constraints resulting from the additional measurement of the SDSS LRG acoustic scale. The same chain was also used to produce Figs. 4 and 5.
Figure 6: The 2D marginalized distributions for the W MAP + HST data. | |
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The low redshift acoustic scale as measured via the analysis of the SDSS LRG power spectrum was found to be if adiabatic initial conditions were assumed (i.e. allowing only for the sinusoidal modulation in the spectrum), and if this assumption was relaxed by allowing additional oscillation phase shifts (Hütsi 2006). These measurements refer to the W MAP best-fit cosmology (Spergel et al. 2003) which was used to analyze the SDSS LRG data. In Sect. 3 we have described accurate transformations needed to accommodate other background cosmologies. In the following we use SH1 and SH2 to denote the sound horizon measurements of and , respectively.
In this section we investigate the constraints on cosmological parameters using the above given values for the sound horizon in combination with the W MAP data. We obtain initial bounds on parameters in a numerically efficient way by applying the method of importance sampling on the earlier calculated W MAP + HST chain. However, to be confident in the results obtained we always carry out a full MCMC calculation from scratch for each of the considered cases. As a final results we only quote the constraints on cosmological parameters obtained from the direct MCMC calculations. Importance sampling is only used as an independent check of the validity of the results. In general both methods reach to the parameter bounds that are in a good agreement.
It is fine to use importance sampling if new constraints are not too constraining and are consistent with the earlier generated chain. Having a measurement of the acoustic scale
^{} with an error
,
importance sampling simply amounts to multiplying each original sample weight by
Figure 7: Upper panel: comparison of the sound horizon as determined from 1000 model spectra, via the same fitting techniques that were used in Hütsi (2006) to measure the SDSS LRG sound horizon, with the analytical approximation given in Eqs. (B.4), (B.5), (B.9), (B.10), (B.11). The model spectra were drawn from the posterior distribution corresponding to the W MAP + HST data. Lower panel: the density plot of the residuals after removing the average bias of . The solid dashed lines mark the credible region. | |
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Figure 8: The 2D marginalized distributions for the W MAP data along with the constraint on the low redshift sound horizon, (SH1), obtained via the importance sampling of the W MAP + HST results shown in Fig. 6. | |
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Figure 9: The same as Fig. 8, only for the sound horizon measurement (SH2). | |
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Figure 10: The exact analog of Fig. 8, now for the full MCMC calculation. | |
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Using the W MAP data and the SDSS LRG power spectrum as shown in Fig. 1 along with the power spectrum transformation and an additional new parameter Q, as described in Sect. 3, we build a 100 000 element Markov chain in the 8-dimensional parameter space. The resulting 2D parameter distribution functions are shown in Fig. 11. Here we see that in several cases distributions start to become doubly-peaked. Also the constraints on H_{0} and are weaker than the ones obtained in the previous subsection. On the other hand, now a rather strong constraint has been obtained for . Even stronger constraint (not shown in the figure) is obtained for - the shape parameter . This just illustrates the the well-known fact that the shape of the matter power spectrum is most sensitive to . The new parameter Q, describing the deformation of the linear spectrum to the evolved redshift-space galaxy power spectrum, is seen to be significantly degenerate with several parameters e.g. , , , . On the other hand it does not interfere too strongly with H_{0}.
It might seem strange that using the full data we obtain weaker constraints on H_{0} and . But after all, we should not be too surprised, since our understanding of how the linear spectrum is deformed to the evolved redshift-space power spectrum is rather limited. Here we were introducing an additional parameter Q, which starts to interfere with the rest of the parameter estimation. Also one should remind that maximum likelihood is the global fitting technique i.e. it is not very sensitive to specific features in the data. On the other hand, modeling of the oscillatory component of the spectrum does not call for any extra parameters. Also the underlying physics is much better understood. In fact, the observable low redshift acoustic scale is determined by four parameters only: , , and h. The optimal data analysis of course should incorporate both components: (i) general shape of the spectrum i.e. low frequency components, and (ii) oscillatory part, with appropriate weightings. It is clear that in the current "full spectrum'' maximum likelihood analysis the acoustic features are weighted too weakly.
Figure 11: The 2D marginalized distributions from the W MAP + SDSS LRG full power spectrum MCMC calculation. | |
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Figure 12: The 1D posterior distributions for several cosmological parameters. Solid, dash-dotted, dashed, and dotted lines correspond to the W MAP + HST, W MAP + SDSS LRG SH1, W MAP + SDSS LRG SH2, and W MAP + SDSS LRG full power spectrum cases, respectively. The compact summary of these results can be found in Table 1. | |
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To compare the measurements of the parameters in a more clear fashion we provide in Fig. 12 several 1D marginalized distributions. The
and credible regions along with the medians of these distributions are provided in Table 1. Here the parameters
,
,
,
,
,
,
and Q (the last in case of the full spectrum analysis only) are primary parameters as used in the MCMC calculations. All the rest:
,
t_{0},
,
,
H_{0}, q_{0}, j_{0} are derived from these. Here t_{0} is the age of the Universe, q_{0} the deceleration parameter and j_{0} the so-called jerk (see e.g. Blandford et al. 2005) at z=0. The deceleration parameter q_{0} and jerk j_{0} are introduced as usual via the Taylor expansion of the scale factor:
(15) |
Table 1: Various quantiles of the 1D distributions shown in Fig. 12. The first group of parameters are the primary ones used in the MCMC calculations, the second group represents various derived quantities, and the last shows the parameters held fixed due to our prior assumptions. The last row of the table also gives the total number of free parameters, excluding the bias parameter that was marginalized out analytically, for all of the investigated cases. Also shown are the -values for the best-fitting model and the effective number of degrees of freedom involved.
Figure 13: The comparison of constraints on H_{0}, and . In all panels the largest error contours correspond to the W MAP + HST, while the tightest to the W MAP + SDSS LRG SH1 case. The upper group of panels shows additionally the constraints for the W MAP + SDSS LRG SH2 case, whereas the lower group provides extra limits from the full spectrum + W MAP analysis. The dashed lines in all the panels show the principal component from Eq. (14). The additional lines in plane provide the directions and . | |
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Figure 14: The version of Fig. 13 with the parameter trio (H_{0}, , ) replaced by (H_{0}, q_{0}, j_{0}). | |
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(16) |
Figure 15: Constraints on the low redshift expansion law. Upper panel: credible regions of the look-back time as a function of redshift for the W MAP + HST and W MAP + SDSS LRG SH1 cases. The inset displays these regions after dividing by the look-back time corresponding to the best-fit W MAP cosmology. Here in addition to the contours also regions are given. Lower panel: analog of the inset in the upper panel. Here we have given the regions as a function of redshift for (starting from the bottommost) the W MAP + HST, W MAP + SDSS LRG full spectrum, W MAP + SDSS LRG SH2 and W MAP + SDSS LRG SH1 cases. | |
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In this section we investigate the effect of several previously made assumptions and carry out an extended analysis by relaxing some of these.
We start out with a small comment about tensor modes. There exist several inflationary models which predict a non-negligible tensor mode contribution to the CMB angular power spectrum (for a classification of several models see e.g. Dodelson et al. 1997). However, the inclusion of the tensor modes into our analysis would not help to constrain them better than the results obtainable from the CMB data alone^{}. In order to make a clean separation of the tensor and scalar contributions to the CMB angular fluctuations one would greatly benefit from the independent knowledge of the scalar fluctuation level as obtainable from the large-scale structure studies. Nevertheless, as in our analysis the biasing parameter is treated as a completely free quantity that is marginalized out, we do not have any sensitivity to the absolute level of the scalar fluctuation component. It is worth pointing out that in principle a good handle on bias parameter can be obtained by the study of the higher order clustering measures e.g. bispectrum (Matarrese et al. 1997b; Verde et al. 1998).
Since several neutrino oscillation experiments unambiguously indicate that neutrinos have a non-zero mass (for a recent review see e.g. Lesgourgues & Pastor 2006) it would be interesting to investigate models with massive neutrino component. For simplicity we concentrate on models with three generations of neutrinos with degenerate masses. Also, as the generic prediction of almost all the inflationary models is the nonmeasurably small spatial curvature i.e. it is of great interest to test whether this is compatible with the observational data. For these reasons we have extended our initial analysis to allow for the massive neutrino component and also have investigated the effect of relaxing the assumption about spatial flatness. The results of this study for some of the model classes are briefly presented in Table 2. Here we have not carried out an analysis using the full measured LRG power spectrum, instead only the measurement of the acoustic scale was added as an additional information to the CMB data. This can be justified for two reasons: (i) to model the observed power spectrum correctly one needs to incorporate the corrections due to nonlinearities and redshift-space distortions, which in our case was done simply by introducing one additional parameter Q (see Eq. (1)). However, it is clear that the effect of this parameter can resemble very closely the power-damping effect of massive neutrinos i.e. there will be very strong degeneracies that result in poor constraints on neutrino mass. Instead of the dynamic damping effect of massive neutrinos we can probe their effect on the kinematics of the background expansion by exploiting the measurement of the low redshift acoustic scale. (ii) The non-zero spatial curvature has twofold effect on the matter power spectrum. First, it influences the growth rate of the fluctuations and thus changes the amplitude of the spectrum. Since in our case the amplitude is treated as a free parameter we are unable to use this effect. Second, the spatial scales get transformed, resulting in the horizontal stretch/compression of the power spectrum. It is clear that the smooth "continuum'' of the power spectrum with an unknown amplitude does not contain much information about the possible horizontal stretch/compression^{} as this transformation can be easily mimicked by the corresponding change of the normalization. The degeneracy between these two transformations can be broken if the spectrum contains some sharper features e.g. acoustic oscillations.
Table 2: Constraints on selected parameters from an extended analysis. Also shown are the -values for the best-fitting model and the effective number of degrees of freedom involved.
According to the results presented in Fig. B.2 one probably would not expect any significant improvement on the measurement of the neutrino mass over the one obtained using the W MAP data alone. This is indeed the case as can be seen from Table 2. Our results on are in good agreement with the constraints obtained in Ichikawa et al. (2005); Fukugita et al. (2006). Contrary to the results presented in Eisenstein et al. (2005) we do not find any improvement in the measurement of once the measurement of the low redshift acoustic scale is incorporated into the analysis. Although by measuring more accurately the value of the Hubble constant one would expect to better break the geometric degeneracy (Bond et al. 1997) and thus should in principle get better constraint on , this is currently not the case. As seen from Fig. 16, by including acoustic scale information, model points lie inside the significantly reduced ellipse that is tilted with respect to the bigger one that uses the W MAP+HST data alone, in such a way, that the projection perpendicular to the flatness line still has practically the same width i.e. the constraints on are also practically identical. We can see how the original distribution of the models turns towards the and also gets significantly compressed perpendicular to the almost vertical degeneracy line corresponding to the measurement of the low redshift sound horizon (see Eq. (B.19)). In Table 2 along with and we have also given a subset of parameters that benefit mostly from the inclusion of the measurement of the acoustic scale. As expected, the parameter constraints are generally getting weaker as we allow for more freedom in the models.
In this paper we have performed a MCMC cosmological parameter study using the results from the recent SDSS LRG power spectrum analysis by Hütsi (2006) along with the CMB temperature-temperature and temperature-polarization angular power spectra as determined by the W MAP team (Kogut et al. 2003; Hinshaw et al. 2003). We have carried out the analysis in two parts: (i) using the W MAP data + the measurement of the low redshift sound horizon as found from the SDSS LRG redshift-space power spectrum, (ii) using the W MAP data + full SDSS LRG power spectrum as shown in Fig. 1. As the formation of the acoustic features in the large-scale matter distribution is theoretically very well understood the separate treatment for the oscillatory part of the LRG power spectrum is well justified. Moreover, in comparison to the full power spectrum, which along with the dependence on several cosmological parameters requires additional modeling of the redshift-space distortions, nonlinear evolution, and biasing^{}, the acoustic scale depends on only a few cosmological parameters. The CMB measurements calibrate the physical scale of the sound horizon to a very good accuracy. By comparing it with the scale inferred from the low redshift LRG power spectrum measurements, we are able to get a very tight constraint on the Hubble parameter:
h=0.708^{+0.021}_{-0.020} if assuming adiabatic initial conditions, or
h=0.705^{+0.038}_{-0.037} if additional shift in oscillation phase is allowed. Having a tight constraint on h allows us to break several parameter degeneracies, and thus helps us to determine various parameters like
,
,
with a good precision. Also, in comparison to the W MAP + HST data, a significantly tighter constraint on
is obtained. The full results for all the parameters are summarized in Table 1. The obtained values are in general in a good agreement with several other parameter studies e.g. Spergel et al. (2003); Percival et al. (2002); Tegmark et al. (2004). Relatively tight bounds on (H_{0},
,
)
or equivalently on (H_{0}, q_{0}, j_{0}) help us to determine the low redshift expansion law with significantly higher precision than available from the W MAP + HST data alone. If the initial fluctuations are constrained to be adiabatic, the measurement of the acoustic scale rules out a decelerating Universe, i.e. q_{0}>0, at
confidence level.
Figure 16: The distribution of models in plane. Gray points correspond to the W MAP+HST case, black ones also include the low redshift sound horizon measurement. The dashed line represents spatially flat models. Dotted lines provide approximate degeneracy directions as given by Eqs. (B.13) and (B.19). Dark energy is assumed to be given by the cosmological constant. | |
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In contrast to the acoustic scale measurement, that gave a precise value for the Hubble parameter, the full spectrum provides us with a good estimate for the shape parameter , which is in a very good agreement with the one found in Tegmark et al. (2004). Since in the plane the line (see Fig. 13) is only relatively weakly tilted with respect to the relevant CMB degeneracy direction , the obtained limits on and h are not as strong as the ones obtained from the measurement of the acoustic scale. In contrast, the degeneracy lines corresponding to the low redshift acoustic scale measurement are in many cases almost orthogonal to the W MAP + HST "ellipses'', which explains the stronger constraints for several parameters.
Throughout most of this work we have focused on spatially flat models and also have assumed negligible contribution from massive neutrinos. As expected, by relaxing these assumptions the bounds on several cosmological parameters loosen significantly. The results of this extended analysis for some of the parameters are represented in a compact form in Table 2.
We have also stressed the need to apply cosmological transformations to the theoretical model spectra before being compared with the relevant observational spectrum, which is valid only in the reference frame of the fiducial cosmological model that was used to analyze the data. So far almost all the parameter studies have completely ignored this point, which is probably fine for the shallow redshift surveys. On the other hand, in case of more deeper surveys like the SDSS LRG, reaching , these transformations have to be certainly applied. In general the line intervals along and perpendicular to the line of sight transform differently. Also the transformations depend on redshift. We have shown that for the samples like SDSS LRGs, with a typical redshift of , the single "isotropized'' transformation taken at the median redshift of the survey provides a very accurate approximation to the more complete treatment.
For the parameter estimation we have used the SDSS LRG power spectrum down to the quasilinear scales, which calls for the extra treatment of nonlinear effects, small scale redshift-space distortions and biasing. These additional complications can be relatively well dealt with the aid of the Halo Model (see Appendix A). We have shown in Hütsi (2006) that a simple analytical model with additional four free parameters is able to approximate the observed spectrum to a very good precision. Also, the Halo Model has been shown to provide a good match to the results of the semianalytical galaxy formation studies (see e.g. Cooray & Sheth 2002). In this paper we have shown that to a rather tolerable accuracy the above four extra parameter Halo Model spectra (for reasonable values of the parameters) can be represented as a simple transformation of the linear power spectrum with only one extra parameter (see Fig. 2). The similar type of transformation was also used in Cole et al. (2005).
In order to investigate the possible biases introduced by the specific method used to extract the sound horizon from the power spectrum measurements, we have performed a Monte Carlo study, the results of what are shown in Fig. 7.
Acknowledgements
I thank Enn Saar and Jose Alberto Rubiño-Martín for valuable discussions and Rashid Sunyaev for the comments on the manuscript. Also I am grateful to the referee for helpful comments and suggestions. I acknowledge the Max Planck Institute for Astrophysics for a graduate fellowship and the support provided through Estonian Ministry of Education and Recearch project TO 0062465S03.
The power spectrum of galaxies in redshift space can be given as:
P(k) = P^{1h}(k) + P^{2h}(k), | (A.1) |
P^{1h}(k) | = | (A.2) | |
p | = | (A.3) |
(A.4) |
(A.8) |
After specifying the background cosmology the above described model has four free parameters: M_{0}, (Eq. (A.9)), (Eq. (A.12)) and . The last parameter represents the lower boundary of the mass integration i.e. halos with masses below are assumed to be "dark''.
= | (B.2) | ||
(B.3) |
(B.6) |
g_{1} | = | (B.7) | |
g_{2} | = | (B.8) |
(B.12) |
(B.14) |
(B.15) |
In addition to the dynamical effect that massive neutrinos have on the evolution of density perturbations they also lead to the modification of the expansion law of the Universe. Assuming three families of massive neutrinos with degenerate masses
one has to replace the term
in Eq. (B.16) with:
Since the lower limit for the sum of neutrino masses coming from the oscillation experiments
(e.g. Lesgourgues & Pastor 2006) is large enough, Eq. (B.21) at low redshifts (as relevant for the acoustic scale measurements using the galaxy clustering data) can be very well approximated as:
(B.23) |
The relative change of the size of the measurable sound horizon at z=0.35 as a function of the sum of neutrino masses , for the mass range compatible with the constraints arising from the W MAP data (Ichikawa et al. 2005; Fukugita et al. 2006), is shown in Fig. B.2. The other parameters here are kept fixed to the best-fit W MAP CDM model values (Spergel et al. 2003). As we can see the measurable sound horizon is only a weak function of and thus one would not expect any strong constraints on neutrino mass from the measurement of the low redshift acoustic scale. We remind that the relative accuracy of the sound horizon measurement as found in Hütsi (2006) is in the range .
In case of the constant dark energy equation of state parameter
,
deceleration parameter q_{0} and jerk j_{0} at redshift z=0 can be expressed as:
(C.1) | |||
(C.2) |
(C.3) | |||
(C.4) |