Features in the Primordial Spectrum from WMAP: A Wavelet Analysis

Features in the Primordial Spectrum from WMAP: A Wavelet Analysis
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WMAP 原始频谱的特征:小波分析

DOI:
10.1103/physrevd.75.123502
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发表时间:
2006
期刊:
影响因子:
5
通讯作者:
R. Rangarajan
R. Rangarajan
中科院分区:
物理与天体物理2区
文献类型:
--
作者:
A. Shafieloo;T. Souradeep;P. Manimaran;P. Panigrahi;R. Rangarajan

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对宇宙微波背景中各向异性的精确测量使我们能够准确地研究给定一组宇宙学参数的原始功率谱的形式。在之前的一篇论文中[A.Shafieloo和T.SouraDeep,Phys.Rev.D70,043523(2004年)。],我们实现了一种改进的(误差敏感的)Richardson-Lucy去卷积算法,对第一年的WMAP数据测量的角功率谱进行解卷积,以确定假设协调的宇宙模型的原始功率谱。恢复的光谱的似然性远远好于标度不变量或最佳拟合的无标度谱(相对于哈里森-泽尔多维奇谱的{增量}lnL{近似相等}25,以及相对于n{亚S}=0.95时的幂定律谱的{增量}lnL{近似相等}11)。本文利用离散小波变换(DWT)分别对恢复频谱的局部特征进行分解,以研究它们对恢复的角功率谱的影响和意义,进而研究它们对恢复的似然比的影响。我们表明,除了地平线尺度上的红外截止外,地平线周围原始功率谱的相关特征对提高可能性有显著的影响。强特征在地平线尺度上局部化。
Precise measurements of the anisotropies in the cosmic microwave background enable us to do an accurate study on the form of the primordial power spectrum for a given set of cosmological parameters. In a previous paper [A. Shafieloo and T. Souradeep, Phys. Rev. D 70, 043523 (2004).], we implemented an improved (error sensitive) Richardson-Lucy deconvolution algorithm on the measured angular power spectrum from the first year of WMAP data to determine the primordial power spectrum assuming a concordance cosmological model. This recovered spectrum has a likelihood far better than a scale invariant, or, 'best fit' scale free spectra ({delta}lnL{approx_equal}25 with respect to the Harrison-Zeldovich spectrum, and, {delta}lnL{approx_equal}11 with respect to the power law spectrum with n{sub s}=0.95). In this paper we use the discrete wavelet transform (DWT) to decompose the local features of the recovered spectrum individually to study their effect and significance on the recovered angular power spectrum and hence the likelihood. We show that besides the infrared cutoff at the horizon scale, the associated features of the primordial power spectrum around the horizon have a significant effect on improving the likelihood. The strong features are localized at the horizon scale.
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