arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.10628astro-ph.CO
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用人工神经网络实现无似然宇宙学约束:在哈勃参数和超新星Ia上的应用

Likelihood-free Cosmological Constraints with Artificial Neural Networks: An Application on Hubble Parameters and SNe Ia

Yu-Chen Wang, Yuan-Bo Xie, Tong-Jie Zhang, Hui-Chao Huang, Tingting Zhang, Kun Liu

摘要:

复杂过程产生的宇宙学数据,如哈勃参数观测数据(OHD)和Ia型超新星观测数据(SNIa),其误差不能用简单的解析概率分布(如高斯分布)精确地模拟。为了从这些数据中约束宇宙学参数,通常使用无似然推理来绕过直接计算似然。在本文中,我们提出了一个新的程序来执行似然自由宇宙学推断使用两个人工神经网络(ANN),掩蔽自回归流(MAF)和去噪自动编码器(DAE)。我们的程序是第一个使用DAE从数据中提取特征,以简化MAF的结构需要估计后验。测试模拟哈勃参数数据与一个简单的高斯似然,该程序显示的能力,从数据中提取的功能和估计后验分布,而不需要易于处理的似然。实验结果表明,该方法能准确逼近真实后验,性能与传统MCMC方法相当,且加入DAE后,MAF在少量仿真时能获得更好的训练效果。我们还讨论了OHD和Pantheon SN Ia数据的应用程序,并使用它们来约束非平坦$Λ$ CDM模型的宇宙学参数。对于Ia型超新星,我们使用拟合的光变曲线参数来寻找$H_0,Ω_m,Ω_Λ$上的约束,类似于相关的工作,使用较少的经验分布。此外,本文还首次将高斯过程引入到OHD模拟过程中。

英文摘要:

The errors of cosmological data generated from complex processes, such as the observational Hubble parameter data (OHD) and the Type Ia supernova (SN Ia) data, cannot be accurately modeled by simple analytical probability distributions, e.g. Gaussian distribution. To constrain cosmological parameters from these data, likelihood-free inference is usually used to bypass the direct calculation of the likelihood. In this paper, we propose a new procedure to perform likelihood-free cosmological inference using two artificial neural networks (ANN), the Masked Autoregressive Flow (MAF) and the denoising autoencoder (DAE). Our procedure is the first to use DAE to extract features from data, in order to simplify the structure of MAF needed to estimate the posterior. Tested on simulated Hubble parameter data with a simple Gaussian likelihood, the procedure shows the capability of extracting features from data and estimating posterior distributions without the need of tractable likelihood. We demonstrate that it can accurately approximate the real posterior, achieve performance comparable to the traditional MCMC method, and the MAF gets better training results for small number of simulation when the DAE is added. We also discuss the application of the proposed procedure to OHD and Pantheon SN Ia data, and use them to constrain cosmological parameters from the non-flat $Λ$CDM model. For SNe Ia, we use fitted light curve parameters to find constraints on $H_0,Ω_m,Ω_Λ$ similar to relevant work, using less empirical distributions. In addition, this work is also the first to use Gaussian process in the procedure of OHD simulation.

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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