Bridge Simulation and Metric Estimation on Landmark Manifolds

We present an inference algorithm and connected Monte Carlo based estimation procedures for metric estimation from landmark configurations distributed according to the transition distribution of a Riemannian Brownian motion arising from the Large Deformation Diffeomorphic Metric Mapping (LDDMM) metric. The distribution possesses properties similar to the regular Euclidean normal distribution but its transition density is governed by a high-dimensional PDE with no closed-form solution in the nonlinear case. We show how the density can be numerically approximated by Monte Carlo sampling of conditioned Brownian bridges, and we use this to estimate parameters of the LDDMM kernel and thus the metric structure by maximum likelihood

Testing physical models for dipolar asymmetry with CMB polarization

The cosmic microwave background (CMB) temperature anisotropies exhibit a large-scale dipolar power asymmetry. To determine whether this is due to a real, physical modulation or is simply a large statistical fluctuation requires the measurement of new modes. Here we forecast how well CMB polarization data from \Planck\ and future experiments will be able to confirm or constrain physical models for modulation. Fitting several such models to the \Planck\ temperature data allows us to provide predictions for polarization asymmetry. While for some models and parameters \Planck\ polarization will decrease error bars on the modulation amplitude by only a small percentage, we show, importantly, that cosmic-variance-limited (and in some cases even \Planck) polarization data can decrease the errors by considerably better than the expectation of $\sqrt 2$ based on simple $\ell$-space arguments. We project that if the primordial fluctuations are truly modulated (with parameters as indicated by \Planck\ temperature data) then \Planck\ will be able to make a 2$\sigma$ detection of the modulation model with 20--75\% probability, increasing to 45--99\% when cosmic-variance-limited polarization is considered. We stress that these results are quite model dependent. Cosmic variance in temperature is important: combining statistically isotropic polarization with temperature data will spuriously increase the significance of the temperature signal with 30\% probability for \Planck.Comment: 18 pages, 11 figures, 2 tables. Version updated to match PRD versio

Bayesian inference on the sphere beyond statistical isotropy

We present a general method for Bayesian inference of the underlying covariance structure of random fields on a sphere. We employ the Bipolar Spherical Harmonic (BipoSH) representation of general covariance structure on the sphere. We illustrate the efficacy of the method as a principled approach to assess violation of statistical isotropy (SI) in the sky maps of Cosmic Microwave Background (CMB) fluctuations. SI violation in observed CMB maps arise due to known physical effects such as Doppler boost and weak lensing; yet unknown theoretical possibilities like cosmic topology and subtle violations of the cosmological principle, as well as, expected observational artefacts of scanning the sky with a non-circular beam, masking, foreground residuals, anisotropic noise, etc. We explicitly demonstrate the recovery of the input SI violation signals with their full statistics in simulated CMB maps. Our formalism easily adapts to exploring parametric physical models with non-SI covariance, as we illustrate for the inference of the parameters of a Doppler boosted sky map. Our approach promises to provide a robust quantitative evaluation of the evidence for SI violation related anomalies in the CMB sky by estimating the BipoSH spectra along with their complete posterior.Comment: 16 pages, 6 figure

Hierarchical Cosmic Shear Power Spectrum Inference

We develop a Bayesian hierarchical modelling approach for cosmic shear power spectrum inference, jointly sampling from the posterior distribution of the cosmic shear field and its (tomographic) power spectra. Inference of the shear power spectrum is a powerful intermediate product for a cosmic shear analysis, since it requires very few model assumptions and can be used to perform inference on a wide range of cosmological models \emph{a posteriori} without loss of information. We show that joint posterior for the shear map and power spectrum can be sampled effectively by Gibbs sampling, iteratively drawing samples from the map and power spectrum, each conditional on the other. This approach neatly circumvents difficulties associated with complicated survey geometry and masks that plague frequentist power spectrum estimators, since the power spectrum inference provides prior information about the field in masked regions at every sampling step. We demonstrate this approach for inference of tomographic shear $E$-mode, $B$-mode and $EB$-cross power spectra from a simulated galaxy shear catalogue with a number of important features; galaxies distributed on the sky and in redshift with photometric redshift uncertainties, realistic random ellipticity noise for every galaxy and a complicated survey mask. The obtained posterior distributions for the tomographic power spectrum coefficients recover the underlying simulated power spectra for both $E$- and $B$-modes.Comment: 16 pages, 8 figures, accepted by MNRA

Image Sampling with Quasicrystals

We investigate the use of quasicrystals in image sampling. Quasicrystals produce space-filling, non-periodic point sets that are uniformly discrete and relatively dense, thereby ensuring the sample sites are evenly spread out throughout the sampled image. Their self-similar structure can be attractive for creating sampling patterns endowed with a decorative symmetry. We present a brief general overview of the algebraic theory of cut-and-project quasicrystals based on the geometry of the golden ratio. To assess the practical utility of quasicrystal sampling, we evaluate the visual effects of a variety of non-adaptive image sampling strategies on photorealistic image reconstruction and non-photorealistic image rendering used in multiresolution image representations. For computer visualization of point sets used in image sampling, we introduce a mosaic rendering technique.Comment: For a full resolution version of this paper, along with supplementary materials, please visit at http://www.Eyemaginary.com/Portfolio/Publications.htm

On the Statistical Significance of the Bulk Flow Measured by the PLANCK Satellite

A recent analysis of data collected by the Planck satellite detected a net dipole at the location of X-ray selected galaxy clusters, corresponding to a large-scale bulk flow extending at least to $z\sim 0.18$, the median redshift of the cluster sample. The amplitude of this flow, as measured with Planck, is consistent with earlier findings based on data from the Wilkinson Microwave Anisotropy Probe (WMAP). However, the uncertainty assigned to the dipole by the Planck team is much larger than that found in the WMAP studies, leading the authors of the Planck study to conclude that the observed bulk flow is not statistically significant. We here show that two of the three implementations of random sampling used in the error analysis of the Planck study lead to systematic overestimates in the uncertainty of the measured dipole. Random simulations of the sky do not take into account that the actual realization of the sky leads to filtered data that have a 12% lower root-mean-square dispersion than the average simulation. Using rotations around the Galactic pole (the Z axis), increases the uncertainty of the X and Y components of the dipole and artificially reduces the significance of the dipole detection from 98-99% to less than 90% confidence. When either effect is taken into account, the corrected errors agree with those obtained using random distributions of clusters on Planck data, and the resulting statistical significance of the dipole measured by Planck is consistent with that of the WMAP results.Comment: A & A, in pres

Sparse Inpainting and Isotropy

Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.Comment: 18 pages, 6 figures. v3: matches version published in JCAP; formatting changes and single typo correction only. Code available from http://zuserver2.star.ucl.ac.uk/~smf/code.htm