Statistical techniques in cosmology

In these lectures I cover a number of topics in cosmological data analysis. I concentrate on general techniques which are common in cosmology, or techniques which have been developed in a cosmological context. In fact they have very general applicability, for problems in which the data are interpreted in the context of a theoretical model, and thus lend themselves to a Bayesian treatment. We consider the general problem of estimating parameters from data, and consider how one can use Fisher matrices to analyse survey designs before any data are taken, to see whether the survey will actually do what is required. We outline numerical methods for estimating parameters from data, including Monte Carlo Markov Chains and the Hamiltonian Monte Carlo method. We also look at Model Selection, which covers various scenarios such as whether an extra parameter is preferred by the data, or answering wider questions such as which theoretical framework is favoured, using General Relativity and braneworld gravity as an example. These notes are not a literature review, so there are relatively few references.Comment: Typos corrected and exercises adde

Generalisations of Fisher Matrices

Fisher matrices play an important role in experimental design and in data analysis. Their primary role is to make predictions for the inference of model parameters - both their errors and covariances. In this short review, I outline a number of extensions to the simple Fisher matrix formalism, covering a number of recent developments in the field. These are: (a) situations where the data (in the form of (x,y) pairs) have errors in both x and y; (b) modifications to parameter inference in the presence of systematic errors, or through fixing the values of some model parameters; (c) Derivative Approximation for LIkelihoods (DALI) - higher-order expansions of the likelihood surface, going beyond the Gaussian shape approximation; (d) extensions of the Fisher-like formalism, to treat model selection problems with Bayesian evidence.Comment: Invited review article for Entropy special issue on 'Applications of Fisher Information in Sciences'. Accepted versio

Weak gravitational lensing: reducing the contamination by intrinsic alignments

Intrinsic alignments of galaxies can mimic to an extent the effects of shear caused by weak gravitational lensing. Previous studies have shown that for shallow surveys with median redshifts z_m = 0.1, the intrinsic alignment dominates the lensing signal. For deep surveys with z_m = 1, intrinsic alignments are believed to be a significant contaminant of the lensing signal, preventing high-precision measurements of the matter power spectrum. In this paper we show how distance information, either spectroscopic or photometric redshifts, can be used to down-weight nearby pairs in an optimised way, to reduce the errors in the shear signal arising from intrinsic alignments. Provided a conservatively large intrinsic alignment is assumed, the optimised weights will essentially remove all traces of contamination. For the Sloan spectroscopic galaxy sample, residual shot noise continues to render it unsuitable for weak lensing studies. However, a dramatic improvement for the slightly deeper Sloan photometric survey is found, whereby the intrinsic contribution, at angular scales greater than 1 arcminute, is reduced from about 80 times the lensing signal to a 10% effect. For deeper surveys such as the COMBO-17 survey with z_m = 0.6, the optimisation reduces the error from a largely systematic 220% error at small angular scales to a much smaller and largely statistical error of only 17% of the expected lensing signal. We therefore propose that future weak lensing surveys be accompanied by the acquisition of photometric redshifts, in order to remove fully the unknown intrinsic alignment errors from weak lensing detections.Comment: 10 pages, 6 figures, MNRAS accepted. Minor changes to match accepted version. RCS and ODT predictions are modifie

Weak lensing: Dark Matter, Dark Energy and Dark Gravity

In this non-specialist review I look at how weak lensing can provide information on the dark sector of the Universe. The review concentrates on what can be learned about Dark Matter, Dark Energy and Dark Gravity, and why. On Dark Matter, results on the confrontation of theoretical profiles with observation are reviewed, and measurements of neutrino masses discussed. On Dark Energy, the interest is whether this could be Einstein's cosmological constant, and prospects for high-precision studies of the equation of state are considered. On Dark Gravity, we consider the exciting prospects for future weak lensing surveys to distinguish General Relativity from extra-dimensional or other gravity theories.Comment: Review paper for GGI Florence meeting on Dark Matte

Objective Bayesian analysis of neutrino masses and hierarchy

Given the precision of current neutrino data, priors still impact noticeably the constraints on neutrino masses and their hierarchy. To avoid our understanding of neutrinos being driven by prior assumptions, we construct a prior that is mathematically minimally informative. Using the constructed uninformative prior, we find that the normal hierarchy is favoured but with inconclusive posterior odds of 5.1:1. Better data is hence needed before the neutrino masses and their hierarchy can be well constrained. We find that the next decade of cosmological data should provide conclusive evidence if the normal hierarchy with negligible minimum mass is correct, and if the uncertainty in the sum of neutrino masses drops below 0.025 eV. On the other hand, if neutrinos obey the inverted hierarchy, achieving strong evidence will be difficult with the same uncertainties. Our uninformative prior was constructed from principles of the Objective Bayesian approach. The prior is called a reference prior and is minimally informative in the specific sense that the information gain after collection of data is maximised. The prior is computed for the combination of neutrino oscillation data and cosmological data and still applies if the data improve.Comment: 15 pages. Minor changes to match accepted version in JCA

Perturbation Theory for BAO reconstructed fields: one-loop results in real-space matter density field

We compute the power spectrum at one-loop order in standard perturbation theory for the matter density field to which a standard Lagrangian Baryonic acoustic oscillation (BAO) reconstruction technique is applied. The BAO reconstruction method corrects the bulk motion associated with the gravitational evolution using the inverse Zel'dovich approximation (ZA) for the smoothed density field. We find that the overall amplitude of one-loop contributions in the matter power spectrum substantially decrease after reconstruction. The reconstructed power spectrum thereby approaches the initial linear spectrum when the smoothed density field is close enough to linear, i.e., the smoothing scale $R_s$ larger than around 10$h^{-1}$Mpc. On smaller $R_s$,however, the deviation from the linear spectrum becomes significant on large scales ($k\lt R_s^{-1}$) due to the nonlinearity in the smoothed density field, and the reconstruction is inaccurate. Compared with N-body simulations, we show that the reconstructed power spectrum at one loop order agrees with simulations better than the unreconstructed power spectrum. We also calculate the tree-level bispectrum in standard perturbation theory to investigate non-Gaussianity in the reconstructed matter density field. We show that the amplitude of the bispectrum significantly decreases for small $k$ after reconstruction and that the tree-level bispectrum agrees well with N-body results in the weakly nonlinear regime.Comment: 18 pages, 7 figures, accepted for publications in PR

Eulerian bias and the galaxy density field

We investigate the effects on cosmological clustering statistics of empirical biasing, where the galaxy distribution is a local transformation of the present-day Eulerian density field. The effects of the suppression of galaxy numbers in voids, and their enhancement in regions of high density, are considered, independently and in combination. We compare results from numerical simulations with the predictions of simple analytic models. We find that the bias is generally scale-dependent, so that the shape of the galaxy power spectrum differs from that of the underlying mass distribution. The degree of bias is always a monotonic function of scale, tending to an asymptotic value on scales where the density fluctuations are linear. The scale dependence is often rather weak, with many reasonable prescriptions giving a bias which is nearly independent of scale. We have investigated whether such an Eulerian bias can reconcile a range of theoretical power spectra with the twin requirements of fitting the galaxy power spectrum and reproducing the observed mass-to-light ratios in clusters. It is not possible to satisfy these constraints for any member of the family of CDM-like power spectra in an Einstein - de Sitter universe when normalised to match COBE on large scales and galaxy cluster abundances on intermediate scales. We discuss what modifications of the mass power spectrum might produce agreement with the observational data.Comment: 14 pages, LaTeX (using mn.sty, epsfig), 17 Postscript figures included. Accepted for publication in MNRA

Combining Size and Shape in Weak Lensing

Weak lensing alters the size of images with a similar magnitude to the distortion due to shear. Galaxy size probes the convergence field, and shape the shear field, both of which contain cosmological information. We show the gains expected in the Dark Energy Figure of Merit if galaxy size information is used in combination with galaxy shape. In any normal analysis of cosmic shear, galaxy sizes are also studied, so this is extra statistical information comes for free and is currently unused. There are two main results in this letter: firstly, we show that size measurement can be made uncorrelated with ellipticity measurement, thus allowing the full statistical gain from the combination, provided that $\sqrt{Area}$ is used as a size indicator; secondly, as a proof of concept, we show that when the relevant modes are noise-dominated, as is the norm for lensing surveys, the gains are substantial, with improvements of about 68% in the Figure of Merit expected when systematic errors are ignored. An approximate treatment of such systematics such as intrinsic alignments and size-magnitude correlations respectively suggests that a much better improvement in the Dark Energy Figure of Merit of even a factor of ~4 may be achieved.Comment: Updated to MNRAS published version and added footnot

Massive Lossless Data Compression and Multiple Parameter Estimation from Galaxy Spectra

We present a method for radical linear compression of datasets where the data are dependent on some number $M$ of parameters. We show that, if the noise in the data is independent of the parameters, we can form $M$ linear combinations of the data which contain as much information about all the parameters as the entire dataset, in the sense that the Fisher information matrices are identical; i.e. the method is lossless. We explore how these compressed numbers fare when the noise is dependent on the parameters, and show that the method, although not precisely lossless, increases errors by a very modest factor. The method is general, but we illustrate it with a problem for which it is well-suited: galaxy spectra, whose data typically consist of $\sim 10^3$ fluxes, and whose properties are set by a handful of parameters such as age, brightness and a parametrised star formation history. The spectra are reduced to a small number of data, which are connected to the physical processes entering the problem. This data compression offers the possibility of a large increase in the speed of determining physical parameters. This is an important consideration as datasets of galaxy spectra reach $10^6$ in size, and the complexity of model spectra increases. In addition to this practical advantage, the compressed data may offer a classification scheme for galaxy spectra which is based rather directly on physical processes.Comment: Minor modifications to match revised version accepted by MNRA