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In this paper we present a novel implementation of Bayesian CMB component separation. We sample from the full posterior distribution using the No-U-Turn Sampler (NUTS), a gradient-based sampling algorithm. Alongside this, we introduce new…

宇宙学与河外天体物理 · 物理学 2024-11-19 R. D. P. Grumitt , Luke R. P. Jew , C. Dickinson

We study the physical limitations placed on CMB temperature and polarization measurements of the initial power spectrum by geometric projection, acoustic physics, gravitational lensing and the joint fitting of cosmological parameters.…

天体物理学 · 物理学 2009-11-10 Wayne Hu , Takemi Okamoto

A functional approximation to implement Bayesian source separation analysis is introduced and applied to separation of the Cosmic Microwave Background (CMB) using WMAP data. The approximation allows for tractable full-sky map…

宇宙学与河外天体物理 · 物理学 2015-03-17 Simon P. Wilson , Jiwon Yoon

This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the…

数据分析、统计与概率 · 物理学 2010-08-30 Nicolas Dobigeon , Said Moussaoui , Martial Coulon , Jean-Yves Tourneret , Alfred O. Hero

We present an application of the fast Independent Component Analysis (FastICA) to the WMAP 3yr data with the goal of extracting the CMB signal. We evaluate the confidence of our results by means of Monte Carlo simulations including CMB,…

天体物理学 · 物理学 2013-01-15 D. Maino , S. Donzelli , A. J. Banday , F. Stivoli , C. Baccigalupi

We present a technique for the blind separation of components in CMB data. The method uses a spectral EM algorithm which recovers simultaneously component templates, their emission law as a function of wavelength, and noise levels. We test…

天体物理学 · 物理学 2009-11-07 H. Snoussi , G. Patanchon , J. F. Macias-Perez , A. Mohammad-Djafari , J. Delabrouille

We present a Bayesian model for multi-resolution CMB component separation based on Wiener filtering and/or computation of constrained realizations, extending a previously developed framework. We also develop an efficient solver for the…

天体物理仪器与方法 · 物理学 2019-07-10 D. S. Seljebotn , T. Bærland , H. K. Eriksen , K. -A. Mardal , I. K. Wehus

We develop the tools necessary to assess the statistical significance of resonant features in the CMB correlation functions, combining power spectrum and bispectrum measurements. This significance is typically addressed by running a large…

宇宙学与河外天体物理 · 物理学 2016-03-02 P. Daniel Meerburg , Moritz Münchmeyer , Benjamin Wandelt

We present a novel estimate of the cosmological microwave background (CMB) map by combining the two latest full-sky microwave surveys: WMAP nine-year and Planck PR1. The joint processing benefits from a recently introduced component…

宇宙学与河外天体物理 · 物理学 2015-06-18 J. Bobin , F. Sureau , J. -L. Starck , A. Rassat , P. Paykari

One of the most tantalizing results from the WMAP experiment is the suggestion that the power at large scales is anomalously low when compared to the prediction of the ``standard'' Lambda-CDM model. The same anomaly, although with somewhat…

天体物理学 · 物理学 2009-11-10 Anastasia Niarchou , Andrew H. Jaffe , Levon Pogosian

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 present a Gibbs sampling solution to the map-making problem for CMB measurements, building on existing destriping methodology. Gibbs sampling breaks the computationally heavy destriping problem into two separate steps; noise filtering…

We discuss an approach to the component separation of microwave, multi-frequency sky maps as those typically produced from Cosmic Microwave Background (CMB) Anisotropy data sets. The algorithm is based on the two step, parametric,…

天体物理学 · 物理学 2009-06-23 R. Stompor , S. Leach , F. Stivoli , C. Baccigalupi

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

We present a blind multi-detector multi-component spectral matching method for all sky observations of the cosmic microwave background, working on the spherical harmonics basis. The method allows to estimate on a set of observation maps the…

天体物理学 · 物理学 2007-05-23 G. Patanchon , H. Snoussi , J. F. Cardoso , J. Delabrouille

The aim of this project is to recover the CMB anisotropies maps in temperature and polarized intensity by means of a deep convolutional neural network (CNN) which, after appropiate training, can remove the foregrounds from Planck and…

宇宙学与河外天体物理 · 物理学 2024-05-29 A. Quintana-Estellés , B. Ruiz-Granados , P. Ruiz-Lapuente

Ever since Cosmic Microwave Background (CMB) signal is being measured by various satellites based observations with increasing experimental accuracies there has been a parallel increase in the demand for a CMB reconstruction technique which…

宇宙学与河外天体物理 · 物理学 2020-04-21 Ujjal Purkayastha , Vipin Sudevan , Rajib Saha

As well as primary fluctuations, CMB temperature maps contain a wealth of additional information in the form of secondary anisotropies. Secondary effects that can be identified with individual objects, such as the thermal and kinetic…

We present a new method for time-efficient and accurate extraction of the power spectrum from future cosmic microwave background (CMB) maps based on properties of peaks and troughs of the Gaussian CMB sky. We construct a statistic…

天体物理学 · 物理学 2009-11-06 A. Kashlinsky , C. Hernández--Monteagudo , F. Atrio--Barandela

We are presenting an Internal Linear Combination (ILC) CMB map, in which the foreground is reduced through harmonic variance minimization. We have derived our method by converting a general form of pixel-space approach into spherical…

天体物理学 · 物理学 2009-09-29 Jaiseung Kim , Pavel Naselsky , Per Rex Christensen