Steven Murray

nIFTy cosmology: Galaxy/halo mock catalogue comparison project on clustering statistics

We present a comparison of major methodologies of fast generating mock halo or galaxy catalogues. The comparison is done for two-point (power spectrum and two-point correlation function in real and redshift space), and the three-point clustering statistics (bispectrum and three-point correlation function). The reference catalogues are drawn from the BigMultiDark N-body simulation. Both friend-of-friends (including distinct haloes only) and spherical overdensity (including distinct haloes and subhalos) catalogues have been used with the typical number density of a large volume galaxy surveys. We demonstrate that a proper biasing model is essential for reproducing the power spectrum at quasi-linear and even smaller scales. With respect to various clustering statistics, a methodology based on perturbation theory and a realistic biasing model leads to very good agreement with N-body simulations. However, for the quadrupole of the correlation function or the power spectrum, only the method based on semi-N-body simulation could reach high accuracy (1percent level) at small scales, i.e. r 0.15hMpc −1 . Full N-body solutions will remain indispensable to produce reference catalogues. Nevertheless, we have demonstrated that the more efficient approximate solvers can reach a fewpercent accuracy in terms of clustering statistics at the scales interesting for the large-scale structure analysis. This makes them useful for massive production aimed at covariance studies, to scan large parameter spaces, and to estimate uncertainties in data analysis techniques, such as baryon acoustic oscillation reconstruction, redshift distortion measurements, etc.

How well do we know the halo mass function

The parameters governing the standardCold Dark Matter cosmological model have been constrained with unprecedented accuracy by precise measurements of the cosmic microwave background by the Wilkinson Microwave Anisotropy Probe (WMAP) and Planck satellites. Each new data release has refined further our knowledge of quan- tities - such as the matter density parameter M - that are imprinted on the dark matter halo mass function (HMF), a powerful probe of dark matter and dark energy models. In this letter we trace how changes in the cosmological parameters over the last decade have influenced uncertainty in our knowledge of the HMF. We show that this uncertainty has reduced significantly since the 3 rd WMAP data release, but the rate of this reduction is slowing. This is limited by uncertainty in the normalisation σ8, whose influence is most pronounced at the high mass end of the mass function. Interestingly, we find that the accuracy with which we can constrain the HMF in terms of the cosmological parameters has now reached the point at which it is comparable to the scatter in HMF fitting functions. This suggests that the power of the HMF as a precision probe of dark matter and dark energy hinges on more accurate deter- mination of the theoretical HMF. Finally, we assess prospects of using the HMF to differentiate between Cold and Warm Dark Matter models based on ongoing improve- ments in measurements of M, and we comment briefly on optimal survey strategies for constraining dark matter and dark energy models using the HMF.

halogen: a tool for fast generation of mock halo catalogues

We present a simple method of generating approximate synthetic halo catalogues: HALOGEN. This method uses a combination of

An Improved Statistical Point-source Foreground Model for the Epoch of Reionization

2^{nd}

An Empirical Mass Function Distribution

-order Lagrangian Perturbation Theory (2LPT) in order to generate the large-scale matter distribution, analytical mass functions to generate halo masses, and a single-parameter stochastic model for halo bias to position haloes. HALOGEN represents a simplification of similar recently published methods. Our method is constrained to recover the 2-point function at intermediate (

Modelling galaxy populations in the era of big data

10Mpc/h<r<50Mpc/h

HMFcalc: An online tool for calculating dark matter halo mass functions

) scales, which we show is successful to within 2 per cent. Larger scales (

Characterization of the ionosphere above the Murchison Radio Observatory using the Murchison Widefield Array

\sim100Mpc/h

Eddington's demon: inferring galaxy mass functions and other distributions from uncertain data

) are reproduced to within 15 per cent. We compare several other statistics (e.g. power spectrum, point distribution function, redshift space distortions) with results from N-Body simulations to determine the validity of our method for different purposes. One of the benefits of HALOGEN is its flexibility, and we demonstrate this by showing how it can be adapted to varying cosmologies and simulation specifications. A driving motivation for the development of such approximate schemes is the need to compute covariance matrices and study the systematic errors for large galaxy surveys, which requires thousands of simulated realisations. We discuss the applicability of our method in this context, and conclude that it is well suited to mass production of appropriate halo catalogues. The code is publicly available at this https URL

The effect of baseline layouts on the EoR foreground wedge: a semi-analytical approach.

We present a sophisticated statistical point-source foreground model for low-frequency radio Epoch of Reionization (EoR) experiments using the 21 cm neutral hydrogen emission line. Motivated by our understanding of the low-frequency radio sky, we enhance the realism of two model components compared with existing models: the source count distributions as a function of flux density and spatial position (source clustering), extending current formalisms for the foreground covariance of 2D power spectral modes in 21 cm EoR experiments. The former we generalise to an arbitrarily broken power-law, and the latter to an arbitrary isotropically-correlated field. This paper presents expressions for the modified covariance under these extensions, and shows that for a more realistic source spatial distribution, extra covariance arises in the EoR window which was previously unaccounted for. Failure to include this contribution can yield bias in the final power spectrum and under-estimate uncertainties, potentially leading to a false detection of signal. The extent of this effect is uncertain, owing to ignorance of physical model parameters, but we show that it is dependent on the relative abundance of faint sources, to the effect that our extension will become more important for future deep surveys. Finally, we show that under some parameter choices, ignoring source clustering can lead to false detections on large scales, due to both the induced bias and an artificial reduction in the estimated measurement uncertainty.