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arXiv 2305.15499gr-qcastro-ph.COcs.LG
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利用 Pantheon 汇编通过神经网络重建宇宙学

Neural network reconstruction of cosmology using the Pantheon compilation

Transilvania University of Brasov(布拉索夫特兰西瓦尼亚大学);Aristotle University of Thessaloniki(塞萨洛尼基亚里士多德大学);Indian Statistical Institute(印度统计学院);University of Malta(马耳他大学)

Konstantinos F. Dialektopoulos, Purba Mukherjee, Jackson Levi Said, Jurgen Mifsud

AI总结:

本文扩展 ReFANN 人工神经网络方法,使其能处理非高斯数据点和协方差矩阵,用于重建 Pantheon 哈勃图,并与高斯过程结果比较及进行零检验。

摘要:

在这项工作中,我们使用人工神经网络(ANN)中的各种数据集(包括相关数据集)重建哈勃图。利用为具有独立不确定度的数据集构建的 ReFANN,我们将其扩展以包含非高斯数据点,以及具有协方差矩阵的数据集等。此外,我们将结果与从高斯过程得出的现有结果进行比较,并执行零检验以测试宇宙学一致性模型的有效性。

英文摘要:

In this work, we reconstruct the Hubble diagram using various data sets, including correlated ones, in Artificial Neural Networks (ANN). Using ReFANN, that was built for data sets with independent uncertainties, we expand it to include non-Guassian data points, as well as data sets with covariance matrices among others. Furthermore, we compare our results with the existing ones derived from Gaussian processes and we also perform null tests in order to test the validity of the concordance model of cosmology.

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。

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