从CMB能谱中提取信息位
Teasing bits of information out of the CMB energy spectrum
摘要：
来自黑体的宇宙微波背景（CMB）频谱图--通常被称为频谱失真--编码了早期宇宙（红移z <几x 10 ^6）的热历史信息。虽然信号通常被表征为mu和y型失真，但在中间红移10^4 < z < 3 x 10^5处也可以产生较小的残余（非y/非mu）失真。在这里，我们构造了一组新的观测量，mu_k，它描述了这个残余失真的主要成分。主成分与温度偏移、y型和mu型失真正交，并根据其可检测性进行排名，从而压缩CMB光谱提供的所有有价值的信息。该方法提供了一种有效的方法来分析给定的实验设置的光谱失真，并可以应用到广泛的能量释放的情况。作为一个例子，我们讨论了小尺度声波和衰变/湮灭粒子的PIXIE型实验的耗散所造成的频谱失真签名的分析。我们提供预测的模型参数和检测限在每种情况下的预期测量不确定性。我们还表明，PIXIE型实验原则上可以区分耗散能量释放粒子衰变的一个几乎标度不变的原始功率谱与小运行。因此，未来的CMB光谱学提供了一个独特的探测原始宇宙中的物理过程。
英文摘要：
Departures of the Cosmic Microwave Background (CMB) frequency spectrum from a blackbody - commonly referred to as spectral distortions - encode information about the thermal history of the early Universe (redshift z < few x 10^6). While the signal is usually characterized as mu- and y-type distortion, a smaller residual (non-y/non-mu) distortion can also be created at intermediate redshifts 10^4 < z < 3 x 10^5. Here, we construct a new set of observables, mu_k, that describes the principal components of this residual distortion. The principal components are orthogonal to temperature shift, y- and mu-type distortion, and ranked by their detectability, thereby delivering a compression of all valuable information offered by the CMB spectrum. This method provides an efficient way of analyzing the spectral distortion for given experimental settings, and can be applied to a wide range of energy-release scenarios. As an illustration, we discuss the analysis of the spectral distortion signatures caused by dissipation of small-scale acoustic waves and decaying/annihilating particles for a PIXIE-type experiments. We provide forecasts for the expected measurement uncertainties of model parameters and detections limits in each case. We furthermore show that a PIXIE-type experiments can in principle distinguish dissipative energy release from particle decays for a nearly scale-invariant primordial power spectrum with small running. Future CMB spectroscopy thus offers a unique probe of physical processes in the primordial Universe.
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