We derive a method to reconstruct Gaussian signals from linear measurements with Gaussian noise. This new algorithm is intended for applications in astrophysics and other sciences. The starting point of our considerations is the principle of minimum
Gibbs free energy which was previously used to derive a signal reconstruction algorithm handling uncertainties in the signal covariance. We extend this algorithm to simultaneously uncertain noise and signal covariances using the same principles in the derivation. The resulting equations are general enough to be applied in many different contexts. We demonstrate the performance of the algorithm by applying it to specific example situations and compare it to algorithms not allowing for uncertainties in the noise covariance. The results show that the method we suggest performs very well under a variety of circumstances and is indeed qualitatively superior to the other methods in cases where uncertainty in the noise covariance is present.
We develop information field theory (IFT) as a means of Bayesian inference on spatially distributed signals, the information fields. A didactical approach is attempted. Starting from general considerations on the nature of measurements, signals, nois
e, and their relation to a physical reality, we derive the information Hamiltonian, the source field, propagator, and interaction terms. Free IFT reproduces the well known Wiener-filter theory. Interacting IFT can be diagrammatically expanded, for which we provide the Feynman rules in position-, Fourier-, and spherical harmonics space, and the Boltzmann-Shannon information measure. The theory should be applicable in many fields. However, here, two cosmological signal recovery problems are discussed in their IFT-formulation. 1) Reconstruction of the cosmic large-scale structure matter distribution from discrete galaxy counts in incomplete galaxy surveys within a simple model of galaxy formation. We show that a Gaussian signal, which should resemble the initial density perturbations of the Universe, observed with a strongly non-linear, incomplete and Poissonian-noise affected response, as the processes of structure and galaxy formation and observations provide, can be reconstructed thanks to the virtue of a response-renormalization flow equation. 2) We design a filter to detect local non-linearities in the cosmic microwave background, which are predicted from some Early-Universe inflationary scenarios, and expected due to measurement imperfections. This filter is the optimal Bayes estimator up to linear order in the non-linearity parameter and can be used even to construct sky maps of non-linearities in the data.
We search for an unusual alignment of the preferred axes of the quadrupole and octopole, the so-called axis of evil, in the CMB temperature and polarization data from WMAP. We use the part of the polarization map which is uncorrelated with the temper
ature map as a statistically independent probe of the axis of evil, which helps to assess whether the latter has a cosmological origin or if is a mere chance fluctuation in the temperature. Note, though, that for certain models creating a preferred axis in the temperature map, we would not expect to see the axis in the uncorrelated polarization map. We find that the axis of the quadrupole of the uncorrelated polarization map roughly aligns with the axis of evil within our measurement precision, whereas the axis of the octopole does not. However, with our measurement uncertainty, the probability of such a scenario to happen by chance in an isotropic universe is of the order of 50 per cent. We also find that the so-called cold spot present in the CMB temperature map is even colder in the part of the temperature map which is uncorrelated with the polarization, although there is still a large uncertainty in the latter. Therefore, our analysis of the axis of evil and a future analysis of the cold spot in the uncorrelated temperature data will strongly benefit from the polarization data expected from the Planck satellite.
Secondary anisotropies of the cosmic microwave background (CMB) can be detected by using the cross-correlation between the large-scale structure (LSS) and the CMB temperature fluctuations. In such studies, chance correlations of primordial CMB fluctu
ations with the LSS are the main source of uncertainty. We present a method for reducing this noise by exploiting information contained in the polarisation of CMB photons. The method is described in general terms and then applied to our recently proposed optimal method for measuring the integrated Sachs-Wolfe (ISW) effect. We obtain an expected signal-to-noise ratio of up to 8.5. This corresponds to an enhancement of the signal-to-noise by 23 per cent as compared to the standard method for ISW detection, and by 16 per cent w.r.t. our recently proposed method, both for the best-case scenario of having perfect (noiseless) CMB and LSS data.
We analyse the local variance effect in the standard method for detecting the integrated Sachs-Wolfe effect (ISW) via cross-correlating the cosmic microwave background (CMB) with the large-scale structure (LSS). Local variance is defined as the syste
matic noise in the ISW detection that originates in the realisation of the matter distribution in the observed Universe. We show that the local variance contributes about 11 per cent to the total variance in the standard method, if a perfect and complete LSS survey up to z ~ 2 is assumed. Due to local variance, the estimated detection significance and cosmological parameter constraints in the standard method are biased. In this work, we present an optimal method of how to reduce the local variance effect in the ISW detection by working conditional on the LSS-data. The variance of the optimal method, and hence the signal-to-noise ratio, depends on the actual realisation of the matter distribution in the observed Universe. We show that for an ideal galaxy survey, the average signal-to-noise ratio is enhanced by about 7 per cent in the optimal method, as compared to the standard method. Furthermore, in the optimal method there is no need to estimate the covariance matrix by Monte Carlo simulations as in the standard method, which saves time and increases the accuracy. Finally, we derive the correct joint likelihood function for cosmological parameters given CMB- and LSS-data within the linear LSS formation regime, which includes a small coupling of the two datasets due to the ISW effect.
