论文标题
通过结合偏斜频谱和功率谱，我们可以学到什么？
What Can We Learn by Combining the Skew Spectrum and the Power Spectrum?
论文作者
论文摘要
大规模结构的聚类为宇宙微波背景各向异性的测量提供了互补的信息，该信息通过功率谱和密度扰动的双光谱。然而，由于复杂的模型以及测量信号及其协方差的计算成本，提取双光谱信息比从功率谱中提取的双光谱信息更具挑战性。为了克服这些问题，我们采用了代理统计量偏斜频谱，这是密度场及其二次场的跨光谱。通过将大型平滑过滤器应用于密度场，我们表明该理论非常适合模拟。由于光谱及其完整的协方差估计为$ n $ body的“模拟”宇宙，因此我们对宇宙学参数表现出色。结果表明，将偏斜频谱添加到电源频谱中，参数的$1σ$边缘化错误$ b_1^2a_s，n_s $和$ f _ {\ rm nl}^{\ rm nl}^{\ rm loc} $分别减少了$ 31 \％，22 \％，44 \％\％$。这是标题中提出的问题的答案，并表明偏斜频谱将是一种快速有效的方法，可以访问功率谱测量中包含的互补信息，尤其是对于即将到来的宽阔场景勘测而言。
Clustering of the large scale structure provides complementary information to the measurements of the cosmic microwave background anisotropies through power spectrum and bispectrum of density perturbations. Extracting the bispectrum information, however, is more challenging than it is from the power spectrum due to the complex models and the computational cost to measure the signal and its covariance. To overcome these problems, we adopt a proxy statistic, skew spectrum which is a cross-spectrum of the density field and its quadratic field. By applying a large smoothing filter to the density field, we show the theory fits the simulations very well. With the spectra and their full covariance estimated from $N$-body simulations as our "mock" Universe, we perform a global fits for the cosmological parameters. The results show that adding skew spectrum to power spectrum the $1σ$ marginalized errors for parameters $ b_1^2A_s, n_s$ and $f_{\rm NL}^{\rm loc}$ are reduced by $31\%, 22\%, 44\%$, respectively. This is the answer to the question posed in the title and indicates that the skew spectrum will be a fast and effective method to access complementary information to that enclosed in the power spectrum measurements, especially for the forthcoming generation of wide-field galaxy surveys.
