A new generation of radio telescopes with unprecedented capabilities for astronomy and fundamental physics will be in operation over the next few years. With high sensitivities and large fields of view, they are ideal for cosmological applications. We discuss their uses for cosmology focusing on the observational technique of HI intensity mapping, in particular at low redshifts (z < 4). This novel observational window promises to bring new insights for cosmology, in particular on ultra-large scales and at a redshift range that can go beyond the dark energy domination epoch. In terms of standard constraints on the dark energy equation of state, telescopes such as Phase I of the SKA should be able to obtain constrains about as well as a future galaxy redshift surveys. Statistical techniques to deal with foregrounds and calibration issues, as well as possible systematics are also discussed.

We describe an analysis of 3-GHz confusion-limited data from the Karl J. Jansky Very Large Array (VLA). We show that with minimal model assumptions, P(D), Bayesian and Markov-Chain Mone-Carlo (MCMC) methods can define the source count to levels some 10 times fainter than the conventional confusion limit. Our verification process includes a full realistic simulation that considers known information on source angular extent and clustering. It appears that careful analysis of the statistical properties of an image is more effective than counting individual objects.

We investigate the contribution of a local over- or under-density to linear estimates of the cosmic dipole. We focus on radio continuum surveys. Recently it was shown that the radio dipole amplitude is larger than expected from the corresponding dipole of the CMB. We show that a significant contribution to this excess could come from local structure.

Radio interferometers suffer from the problem of missing information in their data, due to the gaps between the antennae. This results in artifacts, such as bright rings around sources, in the images obtained. Multiple deconvolution algorithms have been proposed to solve this problem and produce cleaner radio images. However, these algorithms are unable to correctly estimate uncertainties in derived scientific parameters or to always include the effects of instrumental errors. We propose an alternative technique called Bayesian Inference for Radio Observations (BIRO) which uses a Bayesian statistical framework to determine the scientific parameters and instrumental errors simultaneously directly from the raw data, without making an image. We use a simple simulation of Westerbork Synthesis Radio Telescope data including pointing errors and beam parameters as instrumental effects, to demonstrate the use of BIRO.

We discuss a selection of semi-numerical simulations of reionization whose analysis investigates the effect of small absorbing systems, neutral hydrogen within galaxies and the efficiency with which galaxies produce X-rays. We focus on the consequences for both observing the 21–cm 1-point statistics and their interpretation.

With the advent of wide-field surveys, cosmology has entered a new golden age of data where our cosmological model and the nature of dark universe will be tested with unprecedented accuracy, so that we can strive for high precision cosmology. Observational probes like weak lensing, galaxy surveys and the cosmic microwave background as well as other observations will all contribute to these advances. These different probes trace the underlying expansion history and growth of structure in complementary ways and can be combined in order to extract cosmological parameters as best as possible. With future wide-field surveys, observational overlap means these will trace the same physical underlying dark matter distribution, and extra care must be taken when combining information from different probes. Consideration of probe combination is a fundamental aspect of cosmostatistics and important to ensure optimal use of future wide-field surveys.

We present the first measurement of the correlation between the map of the CMB lensing potential derived from the Planck nominal mission data and z ≳ 1.5 galaxies detected by Herschel-ATLAS (H-ATLAS) survey covering about 550 deg2. We detect the cross-power spectrum with a significance of ∼ 8.5σ, ruling out the absence of correlation at 9σ. We check detection with a number of null tests. The amplitude of cross-correlation and the galaxy bias are estimated using joint analysis of the cross-power spectrum and the galaxy survey auto-spectrum, which allows to break degeneracy between these parameters. The estimated galaxy bias is consistent with previous estimates of the bias for the H-ATLAS data, while the cross-correlation amplitude is higher than expected for a ΛCDM model. The content of this work is to appear in a forthcoming paper Bianchini, et al. (2014).

Combining datasets from different experiments and probes to constrain cosmological models is an important challenge in observational cosmology. We summarize a framework for measuring the constraining power and the consistency of separately or jointly analyzed data within a given model that we proposed in earlier work (Seehars et al.2014). Applying the Kullback-Leibler divergence to posterior distributions, we can quantify the difference between constraints and distinguish contributions from gains in precision and shifts in parameter space. We show results from applying this technique to a combination of datasets and probes such as the cosmic microwave background or baryon acoustic oscillations.

I discuss how the cosmographic approach to the determination of cosmological parameters can be implemented with the inclusion of high-redshift data. I argue on the viability of such high-z probes for cosmographic purposes, and resume some statistical issues in finding the most reliable cosmographic truncation.

Does photometric and spectroscopic survey benefit from overlapping areas? The photometric survey measures 2D Weak Lensing (WL) information from galaxy shape distortions. On the other hand, the higher redshift precision of an spectroscopic survey allows measurements of redshift space distortions (RSD) and baryonic accustic oscillations (BAO) from 3D galaxy counts.

The two surveys are combined using 2D-correlations, using sufficiently narrow bins to capture the radial information. This poster present effects of RSD and intrinsic correlations between narrow redshift bins. In understanding how the effects affects cosmological constrains, we first define two stage-IV and then present forecast for various configurations. When surveys overlap, they benefit from additional cross-correlations and sample variance cancellations from overlapping volumes. For a combined dark energy and growth history figure of merit, the result increase 50% for overlapping surveys, corresponding to 30% larger area.

Present and future large scale surveys offer promising probes of cosmology. For example the Dark Energy Survey (DES) is forecast to detect ~300 millions galaxies and thousands clusters up to redshift ~1.3. I here show ongoing work to combine two probes of large scale structure : cluster number counts and galaxy 2-point function (in real or harmonic space). The halo model (coupled to a Halo Occupation Distribution) can be used to model the cross-covariance between these probes, and I introduce a diagrammatic method to compute easily the different terms involved. Furthermore, I compute the joint non-Gaussian likelihood, using the Gram-Charlier series. Then I show how to extend the methods of Bayesian hyperparameters to Poissonian distributions, in a first step to include them in this joint likelihood.

Distance measurements are currently the most powerful tool to study the expansion history of the universe without assuming its matter content nor any theory of gravitation. In general, the reconstruction of the scale factor derivatives, such as the deceleration parameter q(z), is computed using two different methods: fixing the functional form of q(z), which yields potentially biased estimates, or approximating q(z) by a piecewise nth-order polynomial function, whose variance is large. In this work, we address these two methods by reconstructing q(z) assuming only an isotropic and homogeneous universe. For this, we approximate q(z) by a piecewise cubic spline function and, then, we add to the likelihood function a penalty factor, with scatter given by σrel. This factor allows us to vary continuously between the full n knots spline, σrel → ∞, and a single linear function, σrel → 0. We estimate the coefficients of q(z) using the Monte Carlo approach, where the realizations are generated considering ΛCDM as a fiducial model. We apply this procedure in two different cases and assuming four values of σrel to find the best balance between variance and bias. First, we use only the Supernova Legacy Survey 3-year (SNLS3) sample and, in the second analysis, we combine the type Ia supernova (SNeIa) likelihood with those of baryonic acoustic oscillations (BAO) and Hubble function measurements. In both cases we fit simultaneously q(z) and 4 nuisance parameters of the supernovae, namely, the magnitudes $\mathcal{M}$1 and $\mathcal{M}$2 and the light curve parameters α and β.

The avalanche of data over the past 10-20 years has propelled cosmology into the “precision era”. The next challenge cosmology has to meet is to enter the era of accuracy. Because of the intrinsic nature of studying the Cosmos and the sheer amount of data available now and coming soon, the only way to meet this challenge is by developing suitable and specific statistical techniques. The road from precision Cosmology to accurate Cosmology goes through statistical Cosmology. I will outline some open challenges and discuss some specific examples.

The sufficient statistics of the one-point probability density function of the dark matter density field is worked out using cosmological perturbation theory and tested to the Millennium simulation density field. The logarithmic transformation is recovered for spectral index close to -1 as a special case of the family of power transformations. We then discuss how these transforms should be modified in the case of noisy tracers of the field and focus on the case of Poisson sampling. This gives us optimal local transformations to apply to galaxy survey data prior the extraction of the spectrum in order to capture most efficiently the information encoded in large scale structures.

The cosmic web is the intricate network of filaments outlined by the galaxies positions distribution in our Universe. One possible manner to break the complexity of such an elaborate geometrical structure is to assume it made of simple interacting objects. Under this hypothesis, the filamentary network can be considered as the realization of an object or a marked point process. These processes are probabilistic models dealing with configurations of random objects given by random points having random characteristics or marks. Here, the filamentary network is considered as the realization of such a process, with the objects being cylinders that align and connect in order to form the network. The paper presents the use of marked point processes to the detection and the characterization of the galactic filaments.

We present accurate measurements of the large-scale clustering of photometric quasars from the Sloan Digital Sky Survey. These results, detailed in Leistedt & Peiris (2014), rely on a novel technique to identify and treat systematics when measuring angular power spectra, using null-tests and analytical marginalisation. This approach can be used to maximise the extraction of information from current and future galaxy or quasar surveys. For example, it enables to robustly constrain primordial non-Gaussianity (PNG), which modifies the bias of galaxies and quasars on large scales – the most sensitive to observational systematics. The constraints on PNG obtained with the quasar power spectra are detailed in Leistedt, Peiris & Roth (2014); these are the most stringent constraints to date obtained with a single tracer of the large-scale structure.

We investigate the validity of internal methods to estimate the uncertainty of the galaxy two-point correlation function. We consider the jackknife and bootstrap methods, which are based on re-sampling sub-regions of the original data. These are cheap computationally, and do not depend on the accuracy of external simulations. We test the different methods over a large range of scales using a set of 160 mock catalogues from the LasDamas set of simulations. Our results show that the standard bootstrap method significantly overestimates the true uncertainty at all scales. We try two possible generalisations of the bootstrap, but find them not to be robust. Regarding jackknife, we obtain that this method provides an unbiased estimation of the error at small and intermediate scales, up to ∼ 40 h−1, Mpc. At larger scales, it typically overestimates the error by a ∼13%.

Gaussianizing transformations are used statistically in many non-cosmological fields, but in cosmology, we are only starting to apply them. Here I explain a strategy of analyzing the 1-point function (PDF) of a spatial field, together with the ‘essential’ clustering statistics of the Gaussianized field, which are invariant to a local transformation. In cosmology, if the tracer sampling is sufficient, this achieves two important goals. First, it can greatly multiply the Fisher information, which is negligible on nonlinear scales in the usual δ statistics. Second, it decouples clustering statistics from a local bias description for tracers such as galaxies.

We utilize the clustering properties of the Luminous Red Galaxies (LRGs) and the growth rate data in order to constrain the growth index (γ) of the linear matter fluctuations based on a standard χ2 joint likelihood analysis between theoretical expectations and data. We find a value of γ=0.56± 0.05, perfectly consistent with the expectations of the ΛCDM model, and Ωm0 =0.29± 0.02, in very good agreement with the latest Planck results. Our analysis provides significantly more stringent growth index constraints with respect to previous studies as indicated by the fact that the corresponding uncertainty is only ∼ 0.09 γ.

We study the statistical inference of the cosmological dark matter density field from non-Gaussian, non-linear and non-Poisson biased distributed tracers. We have implemented a Bayesian posterior sampling computer-code solving this problem and tested it with mock data based on N-body simulations.