High‐Resolution Observations of the Cosmic Microwave Background Power Spectrum with ACBAR

Kuo, Chao-Lin; Ade, Peter A. R.; Bock, J. J.; Cantalupo, C. M.; Daub, M. D.; Goldstein, Jonathan; Holzapfel, W. L.; Lange, A. E.; Lueker, M.; Newcomb, M.; Peterson, Jeffrey B.; Ruhl, J. E.; Runyan, M. C.; Torbet, E.

doi:10.1086/379783

Cited by 335 publications

(292 citation statements)

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“…For all cases, we analyze two data combinations; the WMAP data alone, and the combination of WMAP, ACBAR (Kuo et al 2004), and BOOMERANG (Montroy et al 2005;Piacentini et al 2005;Jones et al 2005). Marginalization of Sunyaev-Zel'dovich was not included.…”

Section: Cosmological Parametersmentioning

confidence: 99%

A Reanalysis of the 3 YearWilkinson Microwave Anisotropy ProbeTemperature Power Spectrum and Likelihood

Eriksen

Huey

Saha

et al. 2007

ApJ

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We analyze the 3 yr Wilkinson Microwave Anisotropy Probe (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 two implementations to the parameter estimation. Our single most important result is that we broadly confirm the WMAP power spectrum and analysis. Still, we do find two small but potentially important discrepancies. On large angular scales there is a small power excess in the WMAP spectrum (5%Y10% at ' P 30) primarily due to likelihood approximation issues between 13 ' P30. On small angular scales there is a systematic difference between the V-and W-band spectra (few percent at ' k300). Recently, the latter discrepancy was explained by Huffenberger et al. (2006) in terms of oversubtraction of unresolved point sources. As far as the low-' bias is concerned, most parameters are affected by a few tenths of a . The most important effect is seen in n s . For the combination of WMAP, ACBAR, and BOOMERANG, the significance of n s 6 ¼ 1 drops from $2.7 to $2.3 when correcting for this bias. We propose a few simple improvements to the low-' WMAP likelihood code, and introduce two important extensions to the Gibbs sampling method that allows for proper sampling of the low signal-to-noise ratio regime. Finally, we make the products from the Gibbs sampling analysis publicly available, thereby providing a fast and simple route to the exact likelihood without the need of expensive matrix inversions. Subject headingg s: cosmic microwave background -cosmology: observations -methods: numerical

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Section: Cosmological Parametersmentioning

confidence: 99%

A Reanalysis of the 3 YearWilkinson Microwave Anisotropy ProbeTemperature Power Spectrum and Likelihood

Eriksen

Huey

Saha

et al. 2007

ApJ

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“…We use a Markov-Chain Monte-Carlo (MCMC) technique with a modified form of CosmoMC [8]. We use the CMB data from first year WMAP [9], ACBAR [10], CBI [11] and VSA [12], the galaxy power spectrum from SDSS [13] and Lyman−α [14]. Each MCMC analysis included approximately 10 5 points, with an acceptance efficiency of approximately 45%.…”

Section: Figmentioning

confidence: 99%

Parametrizing the power spectrum: beyond the truncated Taylor expansion

Abazajian

Kadota

Stewart

2005

J. Cosmol. Astropart. Phys.

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The power spectrum is traditionally parameterized by a truncated Taylor series: ln P(k) = ln P * +It is reasonable to truncate the Taylor series if |n ′ * ln(k/k * )| ≪ |n * − 1|, but it is not if |n ′ * ln(k/k * )| |n * − 1|. We argue that there is no good theoretical reason to prefer |n ′ * | ≪ |n * −1|, and show that current observations are consistent with |nThus, there are regions of parameter space, which are both theoretically and observationally relevant, for which the traditional truncated Taylor series parameterization is inconsistent, and hence it can lead to incorrect parameter estimations. Motivated by this, we propose a simple extension of the traditional parameterization, which uses no extra parameters, but that, unlike the traditional approach, covers well motivated inflationary spectra with |n ′ * | ∼ |n * − 1|. Our parameterization therefore covers not only standard-slow-roll inflation models but also a much wider class of inflation models. We use this parameterization to perform a likelihood analysis for the cosmological parameters.

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“…For this purpose we have calculated likelihood regions for two data sets: first the WMAP one-year temperature power spectrum data [19], second the WMAP one-year temperature and TE power spectra [20] complemented by the ACBAR data points [21] extending to l ≃ 1500. The above tests are performed for flat and open cosmologies.…”

Section: B Cmbwarp Mcmc and Parameter Estimationmentioning

confidence: 99%

Fast cosmological parameter estimation from microwave background temperature and polarization power spectra

et al. 2004

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We improve the algorithm of Kosowsky, Milosavljevic, and Jimenez (2002) for computing power spectra of the cosmic microwave background. The present algorithm computes not only the temperature power spectrum but also the E-mode polarization and the temperature-polarization cross power spectra, providing the accuracy required for current cosmological parameter estimation. We refine the optimum set of cosmological parameters for computing the power spectra as perturbations around a fiducial model, leading to an accuracy better than 0.5% for the temperature power spectrum throughout the region of parameter space within WMAP's first-year 3σ confidence region. This accuracy is comparable to the difference between the widely-used CMBFAST code (Seljak and Zaldarriaga 1996) and Boltzmann codes. Our algorithm (CMBwarp) makes possible a full exploration of the likelihood region for eight cosmological parameters in about one hour on a laptop computer. We provide the code to compute power spectra as well as the Markov Chain Monte Carlo algorithm for cosmological parameters estimation at http://www.physics.upenn.edu/∼raulj/CMBwarp.

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High‐Resolution Observations of the Cosmic Microwave Background Power Spectrum with ACBAR

Cited by 335 publications

References 50 publications

A Reanalysis of the 3 YearWilkinson Microwave Anisotropy ProbeTemperature Power Spectrum and Likelihood

A Reanalysis of the 3 YearWilkinson Microwave Anisotropy ProbeTemperature Power Spectrum and Likelihood

Parametrizing the power spectrum: beyond the truncated Taylor expansion

Fast cosmological parameter estimation from microwave background temperature and polarization power spectra

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