Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPRISM: Recovery of the primordial spectrum from Planck data
108
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The primordial power spectrum describes the initial perturbations that seeded the large-scale structure we observe today. It provides an indirect probe of inflation or other structure-formation mechanisms. In this letter, we recover the primordial power spectrum from the Planck PR1 dataset, using our recently published algorithm PRISM. PRISM is a sparsity-based inversion method, that aims at recovering features in the primordial power spectrum from the empirical power spectrum of the cosmic microwave background (CMB). This ill-posed inverse problem is regularised using a sparsity prior on features in the primordial power spectrum in a wavelet dictionary. Although this non-parametric method does not assume a strong prior on the shape of the primordial power spectrum, it is able to recover both its general shape and localised features. As a results, this approach presents a reliable way of detecting deviations from the currently favoured scale-invariant spectrum. We applied PRISM to 100 simulated Planck data to investigate its performance on Planck-like data. We also tested the algorithms ability to recover a small localised feature at $k sim 0.125$ Mpc$^{-1}$, which caused a large dip at $ell sim 1800$ in the angular power spectrum. We then applied PRISM to the Planck PR1 power spectrum to recover the primordial power spectrum. We find no significant departures from the fiducial Planck PR1 near scale-invariant primordial power spectrum with $A_s=2.215times10^{-9}$ and $n_s = 0.9624$.
rate research
Read More
The primordial power spectrum is an indirect probe of inflation or other structure-formation mechanisms. We introduce a new method, named textbf{PRISM}, to estimate this spectrum from the empirical cosmic microwave background (CMB) power spectrum. This is a sparsity-based inversion method, which leverages a sparsity prior on features in the primordial spectrum in a wavelet dictionary to regularise the inverse problem. This non-parametric approach is able to reconstruct the global shape as well as localised features of spectrum accurately and proves to be robust for detecting deviations from the currently favoured scale-invariant spectrum. We investigate the strength of this method on a set of WMAP nine-year simulated data for three types of primordial spectra and then process the WMAP nine-year data as well as the Planck PR1 data. We find no significant departures from a near scale-invariant spectrum.
The primordial power spectrum describes the initial perturbations in the Universe which eventually grew into the large-scale structure we observe today, and thereby provides an indirect probe of inflation or other structure-formation mechanisms. Here, we introduce a new method to estimate this spectrum from the empirical power spectrum of cosmic microwave background (CMB) maps. A sparsity-based linear inversion method, coined textbf{PRISM}, is presented. This technique leverages a sparsity prior on features in the primordial power spectrum in a wavelet basis to regularise the inverse problem. This non-parametric approach does not assume a strong prior on the shape of the primordial power spectrum, yet is able to correctly reconstruct its global shape as well as localised features. These advantages make this method robust for detecting deviations from the currently favoured scale-invariant spectrum. We investigate the strength of this method on a set of WMAP 9-year simulated data for three types of primordial power spectra: a nearly scale-invariant spectrum, a spectrum with a small running of the spectral index, and a spectrum with a localised feature. This technique proves to easily detect deviations from a pure scale-invariant power spectrum and is suitable for distinguishing between simple models of the inflation. We process the WMAP 9-year data and find no significant departure from a nearly scale-invariant power spectrum with the spectral index $n_s = 0.972$. A high resolution primordial power spectrum can be reconstructed with this technique, where any strong local deviations or small global deviations from a pure scale-invariant spectrum can easily be detected.
We apply our recently developed code to search for resonance features in the Planck CMB temperature data. We search both for log spaced oscillations or linear spaced oscillations and compare our findings with results of our WMAP9 analysis and the Planck team analysis. While there are hints of log spaced resonant features present in the WMAP9 data, the significance of these features weaken with more data. With more accurate small scale measurements, we also find that the best fit frequency has shifted and the amplitude has been reduced. We confirm the presence of a several low frequency peaks, earlier identified by the Planck team, but with a better improvement of fit (delta chi^2 ~ 12). We further investigate this improvement by allowing the lensing potential to vary as well, showing mild correlation between the amplitude of the oscillations and the lensing amplitude. We find that the improvement of the fit increases even more (delta chi^2 ~ 14) for the low frequencies that modify the spectrum in a way that mimics the lensing effect. Since these features were not present in the WMAP data, they are primarily due to better measurements of Planck at small angular scales. For linear spaced oscillations we find a maximum delta chi^2 ~ 13 scanning two orders of magnitude in frequency space, and the biggest improvements are at extremely high frequencies. We recover a best fit frequency very close to the one found in WMAP9, which confirms that the fit improvement is driven by low l. Further comparisons with WMAP9 show Planck contains many more features, both for linear and log space oscillations, but with a smaller improvement of fit. We discuss the improvement as a function of the number of modes and study the effect of the 217 GHz map, which appears to drive most of the improvement for log spaced oscillations. We conclude that none of the detected features are statistically significant.
We apply a recently developed method to directly measure the gravitational lensing power spectrum from CMB power spectra to the Planck satellite data. This method allows us to analyze the tension between the temperature power spectrum and lens reconstruction in a model independent way. Even when allowing for arbitrary variations in the lensing power spectrum, the tension remains at the 2.4$sigma$ level. By separating the lensing and unlensed high redshift information in the CMB power spectra, we also show that under $Lambda$CDM the two are in tension at a similar level whereas the unlensed information is consistent with lensing reconstruction. These anomalies are driven by the smoother acoustic peaks relative to $Lambda$CDM at $ell sim 1250 - 1500$. Both tensions relax slightly when polarization data are considered. This technique also isolates the one aspect of the lensing power spectrum that the Planck CMB power spectra currently constrain and can be straightforwardly generalized to future data when CMB power spectra constrain multiple aspects of lensing which are themselves correlated with lensing reconstruction.
The shape of the primordial matter power spectrum Plin(k) encodes critical information on cosmological parameters. At large scales, the observable galaxy power spectrum Pobs(k) is expected to follow the shape of Plin(k), but on smaller scales the effects of nonlinearity and galaxy bias make the ratio Pobs(k)/Plin(k) scale-dependent. We develop a method that can extend the dynamic range of the Plin(k) recovery by incorporating constraints on the galaxy halo occupation distribution (HOD) from the projected galaxy correlation function wp. We devise an analytic model to calculate Pobs(k) in real-space and redshift-space. Once HOD parameters are determined by matching wp for a given cosmological model, galaxy bias is completely specified, and our analytic model predicts both the shape and normalization of Pobs(k). Applying our method to SDSS main galaxy samples, we find that the real-space Pobs(k) follows the shape of the nonlinear matter power spectrum at the 1-2% level up to k=0.2 h/Mpc. When we apply our method to SDSS LRG samples, the linear bias approximation is accurate to 5% at k<0.08 h/Mpc, but the scale-dependence of LRG bias prevents the use of linear theory at k>0.08 h/Mpc. Our HOD model prediction is in good agreement with the recent SDSS LRG Pobs(k) measurements at all measured scales (k<0.2 h/Mpc), naturally explaining the shape of Pobs(k). The Q-model prescription is a poor description of galaxy bias for the LRG samples, and it can lead to biased cosmological parameter estimates when measurements at k>0.1 h/Mpc are included in the analysis. We quantify the potential bias and constraints on cosmological parameters that arise from applying linear theory and Q-model fitting, and we demonstrate the utility of HOD modeling of future high precision measurements of Pobs(k) on quasi-linear scales.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
