We present a series of results from a clustering analysis of the first data release of the Visible and Infrared Survey Telescope for Astronomy (VISTA) Deep Extragalactic Observations (VIDEO) survey. VIDEO is the only survey currently capable of probing the bulk of stellar mass in galaxies at redshifts corresponding to the peak of star formation on degree scales. Galaxy clustering is measured with the two-point correlation function, which is calculated using a non parametric kernel based density estimator. We use our measurements to investigate the connection between the galaxies and the host dark matter halo using a halo occupation distribution methodology, deriving bias, satellite fractions, and typical host halo masses for stellar masses between 10 9.35 M and 10 10.85 M , at redshifts 0.5 < z < 1.7. Our results show typical halo mass increasing with stellar mass (with moderate scatter) and bias increasing with stellar mass and redshift consistent with previous studies. We find the satellite fraction increased towards low redshifts, from ∼ 5% at z ∼ 1.5, to ∼ 20% at z ∼ 0.6. We combine our results to derive the stellar mass to halo mass ratio for both satellites and centrals over a range of halo masses and find the peak corresponding to the halo mass with maximum star formation efficiency to be ∼ 2×10 12 M , finding no evidence for evolution.

We present forecasts for constraints on cosmological models that can be obtained using the forthcoming radio continuum surveys: the wide surveys with the Low Frequency Array (LOFAR) for radio astronomy, the Australian Square Kilometre Array Pathfinder (ASKAP) and the Westerbork Observations of the Deep Apertif Northern Sky (WODAN). We use simulated catalogues that are appropriate to the planned surveys in order to predict measurements obtained with the source autocorrelation, the cross‐correlation between radio sources and cosmic microwave background (CMB) maps (the integrated Sachs–Wolfe effect), the cross‐correlation of radio sources with foreground objects resulting from cosmic magnification, and a joint analysis together with the CMB power spectrum and supernovae (SNe). We show that near‐future radio surveys will bring complementary measurements to other experiments, probing different cosmological volumes and having different systematics. Our results show that the unprecedented sky coverage of these surveys combined should provide the most significant measurement yet of the integrated Sachs–Wolfe effect. In addition, we show that the use of the integrated Sachs–Wolfe effect will significantly tighten the constraints on modified gravity parameters, while the best measurements of dark energy models will come from galaxy autocorrelation function analyses. Using a combination of the Evolutionary Map of the Universe (EMU) and WODAN to provide a full‐sky survey, it will be possible to measure the dark energy parameters with an uncertainty of {σ(w0) = 0.05, σ(wa) = 0.12} and the modified gravity parameters {σ(η0) = 0.10, σ(μ0) = 0.05}, assuming Planck CMB+SN (current data) priors. Finally, we show that radio surveys would detect a primordial non‐Gaussianity of fNL= 8 at 1σ, and we briefly discuss other promising probes.

Accurate photometric redshifts are a lynchpin for many future experiments to pin down the cosmological model and for studies of galaxy evolution. In this study, a novel sparse regression framework for photometric redshift estimation is presented. Synthetic dataset simulating the Euclid survey and real data from SDSS DR12 are used to train and test the proposed models. We show that approaches which include careful data preparation and model design offer a significant improvement in comparison with several competing machine learning algorithms. Standard implementations of most regression algorithms use the minimization of the sum of squared errors as the objective function. For redshift inference, this induces a bias in the posterior mean of the output distribution, which can be problematic. In this paper we directly minimize the target metric ∆z = (z s − z p )/(1 + z s ) and address the bias problem via a distribution-based weighting scheme, incorporated as part of the optimization objective. The results are compared with other machine learning algorithms in the field such as Artificial Neural Networks (ANN), Gaussian Processes (GPs) and sparse GPs. The proposed framework reaches a mean absolute ∆z = 0.0026(1 + z s ), over the redshift range of 0 z s 2 on the simulated data, and ∆z = 0.0178(1 + z s ) over the entire redshift range on the SDSS DR12 survey, outperforming the standard ANNZ used in the literature. We also investigate how the relative size of the training sample affects the photometric redshift accuracy. We find that a training sample of >30 per cent of total sample size, provides little additional constraint on the photometric redshifts, and note that our GP formalism strongly outperforms ANNZ in the sparse data regime for the simulated data set.

Radio continuum surveys have, in the past, been of restricted use in cosmology. Most studies have concentrated on cross-correlations with the cosmic microwave background to detect the integrated Sachs-Wolfe effect, due to the large sky areas that can be surveyed. As we move into the SKA era, radio continuum surveys will have sufficient source density and sky area to play a major role in cosmology on the largest scales. In this chapter we summarise the experiments that can be carried out with the SKA as it is built up through the coming decade. We show that the SKA can play a unique role in constraining the non-Gaussianity parameter to σ f NL ∼ 1, and provide a unique handle on the systematics that inhibit weak lensing surveys. The SKA will also provide the necessary data to test the isotropy of the Universe at redshifts of order unity and thus evaluate the robustness of the cosmological principle. Thus, SKA continuum surveys will turn radio observations into a central probe of cosmological research in the coming decades.Advancing Astrophysics with the Square Kilometre Array

Quantifying how the baryonic matter traces the underlying dark matter distribution is key to both understanding galaxy formation and our ability to constrain the cosmological model. Using the cross-correlation function of radio and near-infrared galaxies, we present a largescale clustering analysis of radio galaxies to z ∼ 2.2. We measure the angular auto-correlation function of K s < 23.5 galaxies in the VIDEO-XMM3 field with photometric redshifts out to z = 4 using VIDEO and CFHTLS photometry in the near-infrared and optical. We then use the cross-correlation function of these sources with 766 radio sources at S 1.4 > 90 µJy to infer linear bias of radio galaxies in four redshift bins. We find that the bias evolves from b = 0.57 ± 0.06 at z ∼ 0.3 to 8.55 ± 3.11 at z ∼ 2.2. Furthermore, we separate the radio sources into subsamples to determine how the bias is dependent on the radio luminosity, and find a bias which is significantly higher than predicted by the simulations of Wilman et al., and consistent with the lower luminosity but more abundant FR-I population having a similar bias to the highly luminous but rare FR-IIs. Our results are suggestive of a higher mass, particularly for FR-I sources than assumed in simulations, especially towards higher redshift.