论文标题

限制原始非高斯性的基本限制

Fundamental limits on constraining primordial non-Gaussianity

论文作者

Kalaja, Alba, Meerburg, P. Daniel, Pimentel, Guilherme L., Coulton, William R.

论文摘要

我们研究宇宙差异限制,以限制各种理论动机形状的原始非高斯性。我们考虑了对2D和3D调查的一般论点,并特别强调了CMB。可以用一个信号到噪声来测量比例不变的$ n $ n $ point相关器,该信噪比与观察到的模式数量的平方根天真缩放。这种直觉通常是有两个原因而失败的。首先,由于最后散射表面在短距离上的模糊,信噪比缩放降低。这种模糊是由投影和阻尼的组合引起的,但是信号的丧失并不是由于指数衰减所致,因为信号和噪声都同样受阻。其次,在挤压和倒塌(以$ n> 3 $)限制中,$ n $ - 点相关器的行为可以通过分辨率来增强信噪比的缩放,即使降低了MOMMA范围来探测这些限制。我们提供所有$ n $ - 点相关器的分析估计。我们表明,模糊会影响等边的形状比挤压的形状更大。我们讨论在什么条件下可以利用折叠限制中的乐观量表。最后,我们通过对局部,正交和等边双光线以及局部三角谱的信号到噪声的数值计算来确认我们的分析估计值。我们还表明,在强度数据中增加极化会增强等边样光谱的缩放。

We study the cosmic variance limit on constraining primordial non-Gaussianity for a variety of theory-motivated shapes. We consider general arguments for 2D and 3D surveys, with a particular emphasis on the CMB. A scale-invariant $N$-point correlator can be measured with a signal-to-noise that naively scales with the square root of the number of observed modes. This intuition generally fails for two reasons. First, the signal-to-noise scaling is reduced due to the blurring of the last scattering surface at short distances. This blurring is caused by the combination of projection and damping, but the loss of signal is not due to exponential decay, as both signal and noise are equally damped. Second, the behavior of the $N$-point correlator in the squeezed and collapsed (for $N>3$) limits can enhance the scaling of the signal-to-noise with the resolution, even with a reduced range of momenta probing these limits. We provide analytic estimates for all $N$-point correlators. We show that blurring affects equilateral-like shapes much more than squeezed ones. We discuss under what conditions the optimistic scalings in the collapsed limit can be exploited. Lastly, we confirm our analytical estimates with numerical calculations of the signal-to-noise for local, orthogonal and equilateral bispectra, and local trispectra. We also show that adding polarization to intensity data enhances the scaling for equilateral-like spectra.

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