相关论文: Fast optimal CMB power spectrum estimation with Ha…

Component separation methods mitigate the cross-contamination between different extragalactic and galactic contributions to cosmic microwave background (CMB) data. This is often done by linearly combining CMB maps from different frequency…

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high…

We present a measurement of the angular power spectrum of the cosmic microwave background (CMB) radiation observed at 148 GHz. The measurement uses maps with 1.4' angular resolution made with data from the Atacama Cosmology Telescope (ACT).…

In this paper we demonstrate an efficient method for including both CMB temperature and polarisation data in optimal non-Gaussian estimators. The method relies on orthogonalising the multipoles of the temperature and polarisation maps and…

Observations of the Cosmic Microwave Background (CMB) radiation have made significant contributions to our understanding of cosmology. While temperature observations of the CMB have greatly advanced our knowledge, the next frontier lies in…

WMAP observations have accurately determined the position of the first two peaks and dips in the CMB temperature power spectrum. These encode information on the ratio of the distance to the last scattering surface to the sound horizon at…

We improve predictions of the cosmic microwave background (CMB) power spectrum induced by cosmic strings by using source terms obtained from Nambu-Goto network simulations in an expanding universe. We use three high-resolution cosmic string…

Hybrid Monte-Carlo (HMC) sampling smoother is a fully non-Gaussian four-dimensional data assimilation algorithm that works by directly sampling the posterior distribution formulated in the Bayesian framework. The smoother in its original…

We describe an off-axis microwave telescope for observations of the anisotropy in the cosmic microwave background (CMB) radiation on angular scales between 0.5 deg and 3 deg. The receiver utilizes cryogenic high-electron-mobility transistor…

We present the angular power spectrum derived from the first-year Wilkinson Microwave Anisotropy Probe (WMAP) sky maps. We study a variety of power spectrum estimation methods and data combinations and demonstrate that the results are…

We introduce a new Markov-Chain Monte Carlo (MCMC) approach designed for efficient sampling of highly correlated and multimodal posteriors. Parallel tempering, though effective, is a costly technique for sampling such posteriors. Our…

Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates proposals for a Metropolis-Hastings algorithm by simulating the dynamics of a Hamiltonian system. However, HMC is sensitive to large time…

The amplitude of density perturbations, for the currently-favoured LambdaCDM cosmology, is constrained using the observed properties of galaxy clusters. The catalogue used is that of Ikebe et al. (2002). The cluster temperature to mass…

The advent of high signal-to-noise cosmic microwave background (CMB) anisotropy experiments has allowed detailed studies on the power spectrum of temperature fluctuations. The existence of acoustic oscillations in the anisotropy power…

We revisit the issue of cosmological parameter estimation in light of current and upcoming high-precision measurements of the cosmic microwave background power spectrum. Physical quantities which determine the power spectrum are reviewed,…

Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCMC) algorithm to sample from an unnormalized probability distribution. A leapfrog integrator is commonly used to implement HMC in practice, but its performance can be…

We describe an algorithm to generate temperature and polarization maps of the cosmic microwave background radiation containing non-Gaussianity of arbitrary local type. We apply an optimized quadrature scheme that allows us to predict and…

We present a new blind formulation of the Cosmic Microwave Background (CMB) inference problem. The approach relies on a phenomenological model of the multi-frequency microwave sky without the need for physical models of the individual…

We describe and discuss the application of Gumbel statistics, which model extreme events, to WMAP 5-year measurements of the cosmic microwave background. We find that temperature extrema of the CMB are well modelled by the Gumbel formalism…

The Hamiltonian Monte Carlo (HMC) algorithm is often lauded for its ability to effectively sample from high-dimensional distributions. In this paper we challenge the presumed domination of HMC for the Bayesian analysis of GLMs. By utilizing…