arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2104.00595astro-ph.CO
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用于哈勃参数、增长率和距离模数的神经网络重建

Neural network reconstructions for the Hubble parameter, growth rate and distance modulus

Isidro Gómez-Vargas, Ricardo Medel Esquivel, Ricardo García-Salcedo, J. Alberto Vázquez

AI总结:

本文提出一种基于人工神经网络、低理论假设的宇宙学函数重建方法,可在小样本上重建哈勃参数、增长率和距离模数,并首次用变分自编码器生成合成协方差矩阵。

摘要:

本文介绍了一种利用人工神经网络,基于观测测量结果重建宇宙学函数的新方法,该方法仅需极少的理论和统计假设。通过使用神经网络,我们可以生成观测数据集的计算模型,随后将其与原始数据集进行比较,以验证我们方法的一致性。这一方法论甚至适用于小规模数据集。具体而言,我们使用来自宇宙计时器、$fσ_8$测量以及Ia型超新星距离模数的数据对所提出的方法进行了测试。此外,我们利用Ia型超新星汇编的系统协方差矩阵,介绍了一种通过变分自编码器生成合成协方差矩阵的初步方法。

英文摘要:

This paper introduces a new approach to reconstruct cosmological functions using artificial neural networks based on observational measurements with minimal theoretical and statistical assumptions. By using neural networks, we can generate computational models of observational datasets, and then we compare them with the original ones to verify the consistency of our method. This methodology is applicable to even small-size datasets. In particular, we test the proposed method with data coming from cosmic chronometers, $fσ_8$ measurements, and the distance modulus of the Type Ia supernovae. Furthermore, we introduce a first approach to generate synthetic covariance matrices through a variational autoencoder, using the systematic covariance matrix of the Type Ia supernova compilation.

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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