Nonparametric inference for the cosmic microwave background

被引:20
作者
Genovese, CR [1 ]
Miller, CJ
Nichol, RC
Arjunwadkar, M
Wasserman, L
机构
[1] Carnegie Mellon Univ, Dept Stat, Pittsburgh, PA 15213 USA
[2] Carnegie Mellon Univ, Dept Phys, Pittsburgh, PA 15213 USA
基金
美国国家科学基金会;
关键词
confidence sets; nonparametric regression; cosmology;
D O I
10.1214/088342304000000161
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The cosmic microwave background (CMB), which permeates the entire Universe, is the radiation left over from just 380,000 years after the Big Bang. On very large scales, the CMB radiation field is smooth and isotropic, but the existence of structure in the Universe-stars, galaxies, clusters of galaxies, ... -suggests that the field should fluctuate on smaller scales. Recent observations, from the Cosmic Microwave Background Explorer to the Wilkinson Microwave Anisotropy Probe, have strikingly confirmed this prediction. CMB fluctuations provide clues to the Universe's structure and composition shortly after the Big Bang that are critical for testing cosmological models. For example, CMB data can be used to determine what portion of the Universe is composed of ordinary matter versus the mysterious dark matter and dark energy. To this end, cosmologists usually summarize the fluctuations by the power spectrum, which gives the variance as a function of angular frequency. The spectrum's shape, and in particular the location and height of its peaks, relates directly to the parameters in the cosmological models. Thus, a critical statistical question is how accurately can these peaks be estimated. We use recently developed techniques to construct a nonparametric confidence set for the unknown CMB spectrum. Our estimated spectrum, based on minimal assumptions, closely matches the model-based estimates used by cosmologists, but we can make a wide range of additional inferences. We apply these techniques to test various models and to extract confidence intervals on cosmological parameters of interest. Our analysis shows that, even without parametric assumptions, the first peak is resolved accurately with current data but that the second and third peaks are not.
引用
收藏
页码:308 / 321
页数:14
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