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arXiv 1202.4908astro-ph.CO
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真CMB功率谱估计

True CMB Power Spectrum Estimation

P. Paykari, J. L. Starck, M. J. Fadili

摘要:

宇宙微波背景(CMB)功率谱包含了CMB扰动的几乎所有统计信息,是强有力的宇宙学探测工具。由于我们只能在一个天空中观测到宇宙微波背景辐射,因此我们的实验所测得的宇宙微波背景辐射功率谱只是真实的角功率谱的一个实现。在本文中,我们的目标是恢复真正的基本CMB功率谱从一个实现,我们不需要知道的宇宙学参数。首先在离散余弦变换(DCT)和小波变换(WT)两个字典中研究CMB功率谱的稀疏性。CMB功率谱可以用这两个字典中的仅几个百分比的系数来恢复,因此在这些字典中是非常可压缩的。我们研究了这些字典在平滑一组模拟功率谱的性能。在此基础上,我们开发了一种技术,估计真正的底层CMB功率谱的数据,即不需要知道的宇宙学参数。这种平滑的估计谱可以用来模拟具有与正确宇宙学参数的真实CMB模拟相似性质的CMB映射。这使我们能够在给定的项目中进行蒙特卡罗模拟,而不必知道宇宙学参数。开发的IDL代码TOUSI,用于使用稀疏估计的理论功率谱,将与ISAP的下一个版本一起发布。

英文摘要:

The cosmic microwave background (CMB) power spectrum is a powerful cosmological probe as it entails almost all the statistical information of the CMB perturbations. Having access to only one sky, the CMB power spectrum measured by our experiments is only a realization of the true underlying angular power spectrum. In this paper we aim to recover the true underlying CMB power spectrum from the one realization that we have without a need to know the cosmological parameters. The sparsity of the CMB power spectrum is first investigated in two dictionaries; Discrete Cosine Transform (DCT) and Wavelet Transform (WT). The CMB power spectrum can be recovered with only a few percentage of the coefficients in both of these dictionaries and hence is very compressible in these dictionaries. We study the performance of these dictionaries in smoothing a set of simulated power spectra. Based on this, we develop a technique that estimates the true underlying CMB power spectrum from data, i.e. without a need to know the cosmological parameters. This smooth estimated spectrum can be used to simulate CMB maps with similar properties to the true CMB simulations with the correct cosmological parameters. This allows us to make Monte Carlo simulations in a given project, without having to know the cosmological parameters. The developed IDL code, TOUSI, for Theoretical pOwer spectrUm using Sparse estImation, will be released with the next version of ISAP.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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