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We present the first results from a Bayesian analysis of the WMAP first year data using a Gibbs sampling technique. Using two independent, parallel supercomputer codes we analyze the WMAP Q, V and W bands. The analysis results in a full…

We have developed a fast, accurate and generally applicable method for inferring the power spectrum and its uncertainties from maps of the cosmic microwave background (CMB) in the presence of inhomogeneous and correlated noise. For maps…

天体物理学 · 物理学 2014-10-13 O. Doré , L. Knox , A. Peel

We analyze the three-year WMAP temperature anisotropy data seeking to confirm the power spectrum and likelihoods published by the WMAP team. We apply five independent implementations of four algorithms to the power spectrum estimation and…

Cross-power spectrum is a quadratic estimator between two maps that can provide unbiased estimate of the underlying power spectrum of the correlated signals, which is therefore used for extracting the power spectrum in the WMAP data. In…

宇宙学与河外天体物理 · 物理学 2015-05-20 Lung-Yih Chiang , Fei-Fan Chen

We describe and implement an exact, flexible, and computationally efficient algorithm for joint component separation and CMB power spectrum estimation, building on a Gibbs sampling framework. Two essential new features are 1) conditional…

天体物理学 · 物理学 2010-11-11 H. K. Eriksen , J. B. Jewell , C. Dickinson , A. J. Banday , K. M. Gorski , C. R. Lawrence

Earlier papers introduced a method of accurately estimating the angular cosmic microwave background (CMB) temperature power spectrum based on Gibbs sampling. Here we extend this framework to polarized data. All advantages of the Gibbs…

天体物理学 · 物理学 2008-11-26 D. L. Larson , H. K. Eriksen , B. D. Wandelt , K. M. Gorski , Greg Huey , J. B. Jewell , I. J. O'Dwyer

In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributions. Specifically, we provide means for drawing either approximate or unbiased samples from Gibbs' distributions by introducing low…

机器学习 · 计算机科学 2013-10-01 Tamir Hazan , Subhransu Maji , Tommi Jaakkola

We apply a previously developed Gibbs sampling framework to the foreground corrected 3-yr WMAP polarization data and compute the power spectrum and residual foreground template amplitude posterior distributions. We first analyze the…

天体物理学 · 物理学 2010-11-11 H. K. Eriksen , Greg Huey , A. J. Banday , K. M. Gorski , J. B. Jewell , I. J. O'Dwyer , B. D. Wandelt

We present results from an end-to-end simulation pipeline interferometric observations of cosmic microwave background polarization. We use both maximum-likelihood and Gibbs sampling techniques to estimate the power spectrum. In addition, we…

宇宙学与河外天体物理 · 物理学 2014-07-15 Emory F. Bunn , Ata Karakci , Paul M. Sutter , Le Zhang , Gregory S. Tucker , Peter T. Timbie , Benjamin D. Wandelt

In recent years the goal of estimating different cosmological parameters precisely has set new challenges in the effort to accurately measure the angular power spectrum of CMB. This has required removal of foreground contamination as well…

天体物理学 · 物理学 2008-11-26 Rajib Saha , Simon Prunet , Pankaj Jain , Tarun Souradeep

We present a method designed to estimate the noise power spectrum in the time domain for CMB experiments. The noise power spectrum is extracted from the time ordered data avoiding the contamination coming from sky signal and accounting the…

天体物理学 · 物理学 2009-11-10 Alexandre Amblard , Jean-Christophe Hamilton

We study different variants of the Gibbs sampler algorithm from the perspective of their applicability to the estimation of power spectra of the cosmic microwave background (CMB) anisotropies. These include approaches studied earlier in the…

宇宙学与河外天体物理 · 物理学 2022-05-13 Gabriel Ducrocq , Nicolas Chopin , Josquin Errard , Radek Stompor

We present a method for fast optimal estimation of the temperature angular power spectrum from observations of the cosmic microwave background. We employ a Hamiltonian Monte Carlo (HMC) sampler to obtain samples from the posterior…

天体物理学 · 物理学 2009-11-13 J. F. Taylor , M. A. J. Ashdown , M. P. Hobson

Forthcoming high-resolution observations of the Cosmic Microwave Background (CMB) radiation will generate datasets many orders of magnitude larger than have been obtained to date. The size and complexity of such datasets presents a very…

天体物理学 · 物理学 2016-08-25 Julian Borrill

We propose an efficient Bayesian MCMC algorithm for estimating cosmological parameters from CMB data without use of likelihood approximations. It builds on a previously developed Gibbs sampling framework that allows for exploration of the…

宇宙学与河外天体物理 · 物理学 2016-03-29 Benjamin Racine , Jeffrey B. Jewell , Hans Kristian K. Eriksen , Ingunn K. Wehus

We implement and further refine the recently proposed method (Kashlinsky, Hern\'andez-Monteagudo & Atrio-Barandela, 2001 - KHA) for a time efficient extraction of the power spectrum from future cosmic microwave background (CMB) maps. The…

天体物理学 · 物理学 2009-11-07 C. Hernandez-Monteagudo , A. Kashlinsky , F. Atrio-Barandela

CMB data analysis is in general done through two main steps : map-making of the time data streams and power spectrum extraction from the maps. The latter basically consists in the separation between the variance of the CMB and that of the…

天体物理学 · 物理学 2007-05-23 J. -Ch. Hamilton

The marked power spectrum - a two-point correlation function of a transformed density field - has emerged as a promising tool for extracting cosmological information from the large-scale structure of the Universe. In this work, we present…

The remarkable improvement in the estimates of different cosmological parameters in recent years has been largely spearheaded by accurate measurements of the angular power spectrum of Cosmic Microwave Background (CMB) radiation. This has…

天体物理学 · 物理学 2011-04-11 Tarun Souradeep , Rajib Saha , Pankaj Jain

The growing scarcity of spectrum resources, wideband spectrum sensing is required to process a prohibitive volume of data at a high sampling rate. For some applications, spectrum estimation only requires second-order statistics. In this…

信号处理 · 电气工程与系统科学 2023-11-27 Kaili Jiang , Dechang Wang , Kailun Tian , Hancong Feng , Yuxin Zhao , Junyu Yuan , Bin Tang
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