A full-sky prediction of the Sunyaev–Zeldovich effect from diffuse hot gas in the local universe and the upper limit from the WMAP data

We use the Point Source Catalogue Redshift Survey galaxy redshift catalogue combined with constrained simulations based on the IRAS 1.2-Jy galaxy density field to estimate the contribution of hot gas in the local universe to the Sunyaev–Zeldovich (SZ) effect on a large scale. We produce a full-sky healpix map predicting the SZ effect from clusters as well as diffuse hot gas within  80  h ⁻¹ Mpc  . Performing cross-correlation tests between this map and the WMAP data in pixel, harmonic and wavelet space we can put an upper limit on the effect. We conclude that the SZ effect from diffuse gas in the local universe cannot be detected in current cosmic microwave background (CMB) data and is not a large-scale contaminating factor  (ℓ < 60)  in studies of CMB angular anisotropies. We derive an upper limit for the mean temperature decrement of  Δ T < 0.33 μK  at the 2σ confidence level for the 61-GHz frequency channel. However, for future high-sensitivity experiments observing at a wider range of frequencies, the predicted large-scale SZ effect could be of importance.     

Observation number correlation in WMAP data

A remarkable similarity between the large-scale non-Gaussian pattern of cosmic microwave background (CMB) temperatures obtained by the Wilkinson Microwave Anisotropy Probe ( WMAP ) mission and the distribution features of observation numbers is noted. Motivated by such a similarity, in this work we check the WMAP data for the correlation between pixel temperature t and observation number N . Systematic effects of imbalance in the differential observations and significant t – N correlations in magnitude, distribution of non-Gaussianity and north–south asymmetry are found. Our results indicate that, for precision cosmology studies based on WMAP observations, the observation effect on released WMAP temperature maps requires further careful study.     

Primordial non-Gaussianity: local curvature method and statistical significance of constraints on fNL from WMAP data

We test the consistency of estimates of the non-linear coupling constant f _NL using non-Gaussian cosmic microwave background (CMB) maps generated by the method described in the work of Liguori, Matarrese & Moscardini. This procedure to obtain non-Gaussian maps differs significantly from the method used in previous works on the estimation of f _NL. Nevertheless, using spherical wavelets, we find results in very good agreement with Mukherjee & Wang, showing that the two ways of generating primordial non-Gaussian maps give equivalent results. Moreover, we introduce a new method for estimating the non-linear coupling constant from CMB observations by using the local curvature of the temperature fluctuation field. We present both Bayesian credible regions (assuming a flat prior) and proper (frequentist) confidence intervals on f _NL, and discuss the relation between the two approaches. The Bayesian approach tends to yield lower error bars than the frequentist approach, suggesting that a careful analysis of the different interpretations is needed. Using this method, we estimate   f _NL=−10⁺²⁷⁰₋₂₆₀  at the 2σ level (Bayesian) and   f _NL=−10⁺³¹⁰₋₂₇₀  (frequentist). Moreover, we find that the wavelet and the local curvature approaches, which provide similar error bars, yield approximately uncorrelated estimates of f _NL and therefore, as advocated in the work of Cabella et al., the estimates may be combined to reduce the error bars. In this way, we obtain   f _NL=−5 ± 85  and   f _NL=−5 ± 175  at the 1σ and 2σ level respectively using the frequentist approach.     

Processing and compression of noise-dominated data: application to the cosmic microwave background data on board the Planck satellite

We address the problem of encoding and compressing data dominated by noise. Information is decomposed into 'reference' sequences plus arrays containing noisy differences susceptible to being described by a known probability distribution. One can then give reliable estimates of the optimal compression rates by estimating the corresponding Shannon entropy. As a working example, this idea is applied to an idealized model of the cosmic microwave background (CMB) data on board the Planck satellite. Data reduction is a critical issue in space missions because the total information that can be downloaded to Earth is sometimes limited by telemetry allocation. Similar limitations might arise in remotely operated ground based telescopes. This download-rate limitation could reduce the amount of diagnostics sent on the stability of the instruments and, as a consequence, curb the final sensitivity of the scientific signal. Our proposal for Planck consists of taking differences of consecutive circles at a given sky pointing. To a good approximation, these differences could be made independent of the external signal, so that they are dominated by thermal (white) instrumental noise, which is simpler to model than the sky signal. Similar approaches can be found in other individual applications. Generic simulations and analytical predictions show that high compression rates,     can be obtained with minor or zero loss of sensitivity. Possible effects of digital distortion are also analysed. The proposed scheme is flexible and reliable enough to be optimized in relation to other critical aspects of the corresponding application. For Planck , this study constitutes an important step towards a more realistic modelling of the final sensitivity of the CMB temperature anisotropy maps.     

The XMM Cluster Survey: forecasting cosmological and cluster scaling-relation parameter constraints

We forecast the constraints on the values of  σ₈, Ω_m  and cluster scaling-relation parameters which we expect to obtain from the XMM Cluster Survey (XCS). We assume a flat Λ cold dark matter Universe and perform a Monte Carlo Markov Chain analysis of the evolution of the number density of galaxy clusters that takes into account a detailed simulated selection function. Comparing our current observed number of clusters shows good agreement with predictions. We determine the expected degradation of the constraints as a result of self-calibrating the luminosity–temperature relation (with scatter), including temperature measurement errors, and relying on photometric methods for the estimation of galaxy cluster redshifts. We examine the effects of systematic errors in scaling relation and measurement error assumptions. Using only  ( T , z )  self-calibration, we expect to measure Ω_m to ±0.03 (and  Ω_Λ  to the same accuracy assuming flatness), and σ₈ to ±0.05, also constraining the normalization and slope of the luminosity–temperature relation to ±6 and ±13 per cent (at 1σ), respectively, in the process. Self-calibration fails to jointly constrain the scatter and redshift evolution of the luminosity–temperature relation significantly. Additional archival and/or follow-up data will improve on this. We do not expect measurement errors or imperfect knowledge of their distribution to degrade constraints significantly. Scaling-relation systematics can easily lead to cosmological constraints 2σ or more away from the fiducial model. Our treatment is the first exact treatment to this level of detail, and introduces a new 'smoothed ML' (Maximum Likelihood) estimate of expected constraints.     

Limitations of Bayesian Evidence applied to cosmology

There has been increasing interest by cosmologists in applying Bayesian techniques, such as Bayesian Evidence, for model selection. A typical example is in assessing whether observational data favour a cosmological constant over evolving dark energy. In this paper, the example of dark energy is used to illustrate limitations in the application of Bayesian Evidence associated with subjective judgements concerning the choice of model and priors. An analysis of recent cosmological data shows a statistically insignificant preference for a cosmological constant over simple dynamical models of dark energy. It is argued that for nested problems, as considered here, Bayesian parameter estimation can be more informative than computing Bayesian Evidence for poorly motivated physical models.     

Smoothing supernova data to reconstruct the expansion history of the Universe and its age

We propose a non-parametric method of smoothing supernova data over redshift using a Gaussian kernel in order to reconstruct important cosmological quantities including   H ( z )  and   w ( z )  in a model-independent manner. This method is shown to be successful in discriminating between different models of dark energy when the quality of data is commensurate with that expected from the future Supernova Acceleration Probe ( SNAP ). We find that the Hubble parameter is especially well determined and useful for this purpose. The look-back time of the Universe may also be determined to a very high degree of accuracy (≲0.2 per cent) using this method. By refining the method, it is also possible to obtain reasonable bounds on the equation of state of dark energy. We explore a new diagnostic of dark energy – the ' w -probe'– which can be calculated from the first derivative of the data. We find that this diagnostic is reconstructed extremely accurately for different reconstruction methods even if Ω_{0 m} is marginalized over. The w -probe can be used to successfully distinguish between Λ cold dark matter and other models of dark energy to a high degree of accuracy.     

Alignment and signed-intensity anomalies in Wilkinson Microwave Anisotropy Probe data

Significant alignment and signed-intensity anomalies of local features of the cosmic microwave background (CMB) are detected on the three-year Wilkinson Microwave Anisotropy Probe data, through a decomposition of the signal with steerable wavelets on the sphere. In addition to identifying local features of a signal at specific scales, steerable wavelets allow one to determine their local orientation and signed intensity. First, an alignment analysis identifies two mean preferred planes in the sky, both with normal axes close to the CMB dipole axis. The first plane is defined by the directions towards which local CMB features are anomalously aligned. A mean preferred axis is also identified in this plane, located very close to the ecliptic poles axis. The second plane is defined by the directions anomalously avoided by local CMB features. This alignment anomaly provides further insight on recent results. Secondly, a signed-intensity analysis identifies three mean preferred directions in the southern Galactic hemisphere with anomalously high or low temperature of local CMB features: a cold spot essentially identified with a known cold spot, a second cold spot lying very close to the southern end of the CMB dipole axis, and a hotspot lying close to the southern end of the ecliptic poles axis. In both analyses, the anomalies are observed at wavelet scales corresponding to angular sizes around 10° on the celestial sphere, with global significance levels around 1 per cent. Further investigation reveals that the alignment and signed-intensity anomalies are only very partially related. Instrumental noise, foreground emissions and some form of other systematics are strongly rejected as possible origins of the detections. An explanation might still be envisaged in terms of a global violation of the isotropy of the Universe, inducing an intrinsic statistical anisotropy of the CMB.     

Probing dark energy with steerable wavelets through correlation of WMAP and NVSS local morphological measures

Using local morphological measures on the sphere defined through a steerable wavelet analysis, we examine the three-year Wilkinson Microwave Anisotropy Probe WMAP and the NRAO VLA Sky Survey (NVSS) data for correlation induced by the integrated Sachs–Wolfe (ISW) effect. The steerable wavelet constructed from the second derivative of a Gaussian allows one to define three local morphological measures, namely the signed-intensity, orientation and elongation of local features. Detections of correlation between the WMAP and NVSS data are made with each of these morphological measures. The most significant detection is obtained in the correlation of the signed-intensity of local features at a significance of 99.9 per cent. By inspecting signed-intensity sky maps, it is possible for the first time to see the correlation between the WMAP and NVSS data by eye. Foreground contamination and instrumental systematics in the WMAP data are ruled out as the source of all significant detections of correlation. Our results provide new insight on the ISW effect by probing the morphological nature of the correlation induced between the cosmic microwave background and large-scale structure of the Universe. Given the current constraints on the flatness of the Universe, our detection of the ISW effect again provides direct and independent evidence for dark energy. Moreover, this new morphological analysis may be used in future to help us to better understand the nature of dark energy.     

asmooth: a simple and efficient algorithm for adaptive kernel smoothing of two-dimensional imaging data

An efficient algorithm for adaptive kernel smoothing (AKS) of two-dimensional imaging data has been developed and implemented using the Interactive Data Language ( idl ). The functional form of the kernel can be varied (top-hat, Gaussian, etc.) to allow different weighting of the event counts registered within the smoothing region. For each individual pixel, the algorithm increases the smoothing scale until the signal-to-noise ratio (S/N) within the kernel reaches a pre-set value. Thus, noise is suppressed very efficiently, while at the same time real structure, that is, signal that is locally significant at the selected S/N level, is preserved on all scales. In particular, extended features in noise-dominated regions are visually enhanced. The asmooth algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all pixel values share the same S/N above the background .
We apply asmooth to both real observational data (an X-ray image of clusters of galaxies obtained with the Chandra X-ray Observatory) and to a simulated data set. We find the asmooth ed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, that is, they possess Poissonian variance and a near-Gaussian distribution around a mean of zero, and are spatially uncorrelated.