arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2105.03324astro-ph.COastro-ph.IM
收藏

宇宙学参数中张力的非高斯估计

Non-Gaussian estimates of tensions in cosmological parameters

Marco Raveri, Cyrille Doux

摘要:

我们讨论了如何有效地和可靠地估计不同实验的参数确定的一致性和不一致性的水平,充分考虑到非高斯参数后验。我们开发了两个家庭的可扩展的算法,使我们能够执行这种类型的计算在越来越多的维度和不同程度的紧张局势。一类算法依赖于后验分布的核密度估计,而另一类算法依赖于具有归一化流的后验分布的机器学习建模。我们展示了他们的有效性和准确性与一组基准的例子,发现这两种方法同意彼此和真正的紧张局势内$0.5σ$在困难的情况下,一般$0.2σ$或更好。这使我们能够研究暗能量巡天的宇宙学结构集群的不同测量之间的内部一致性水平,以及它们与普朗克卫星的宇宙微波背景测量之间的一致性。

英文摘要:

We discuss how to efficiently and reliably estimate the level of agreement and disagreement on parameter determinations from different experiments, fully taking into account non-Gaussianities in the parameter posteriors. We develop two families of scalable algorithms that allow us to perform this type of calculations in increasing number of dimensions and for different levels of tensions. One family of algorithms rely on kernel density estimates of posterior distributions while the other relies on machine learning modeling of the posterior distribution with normalizing flows. We showcase their effectiveness and accuracy with a set of benchmark examples and find both methods agree with each other and the true tension within $0.5σ$ in difficult cases and generally to $0.2σ$ or better. This allows us to study the level of internal agreement between different measurements of the clustering of cosmological structures from the Dark Energy Survey and their agreement with measurements of the Cosmic Microwave Background from the Planck satellite.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑