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arXiv 2202.05949astro-ph.CO
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基于估计协方差矩阵的张量-标量比推断的偏差

Bias on Tensor-to-Scalar Ratio Inference With Estimated Covariance Matrices

Dominic Beck, Ari Cukierman, W. L. Kimmy Wu

摘要:

我们研究基于模拟的带功率协方差矩阵常用于宇宙学参数的推断,如张量标量比$r$的估计。我们发现,$r$的上限可能会有几十个百分点的偏差。当模拟实现的数量与观测量的数量相似时,对上限的低估是最严重的。协方差矩阵估计的收敛可能需要比可观测量的数量大一个数量级的模拟次数,这可能意味着$\mathcal{O}(10\ 000)$模拟。这被发现是由于估计的带功率协方差矩阵中的蒙特卡罗噪声,特别是由虚假的非零非对角元素引起的$r$的后验概率的额外分散。我们表明,矩阵条件可以是一个可行的缓解策略的情况下,可以合法的协方差假设。

英文摘要:

We investigate simulation-based bandpower covariance matrices commonly used in cosmological parameter inferences such as the estimation of the tensor-to-scalar ratio $r$. We find that upper limits on $r$ can be biased low by tens of percent. The underestimation of the upper limit is most severe when the number of simulation realizations is similar to the number of observables. Convergence of the covariance-matrix estimation can require a number of simulations an order of magnitude larger than the number of observables, which could mean $\mathcal{O}(10\ 000)$ simulations. This is found to be caused by an additional scatter in the posterior probability of $r$ due to Monte Carlo noise in the estimated bandpower covariance matrix, in particular, by spurious non-zero off-diagonal elements. We show that matrix conditioning can be a viable mitigation strategy in the case that legitimate covariance assumptions can be made.

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