论文标题
使用变异推理从21 cm信号中估算参数
Parameters Estimation from the 21 cm signal using Variational Inference
论文作者
论文摘要
即将进行的实验，例如电离阵列（HERA）和平方公里阵列（SKA）等实验，旨在测量在广泛的红移范围内的21cm信号，这代表了我们对宇宙校正本质的理解，这是一个难以置信的机会。同时，这些实验将在处理大量生成的数据时提出新的挑战，呼吁开发能够精确估计物理参数及其不确定性的自动化方法。在本文中，我们采用了变异推断，尤其是贝叶斯神经网络，作为21 cm观测中MCMC的替代方法，以报告宇宙学和天体物理参数的可靠估计，并评估它们之间的相关性。
Upcoming experiments such as Hydrogen Epoch of Reionization Array (HERA) and Square Kilometre Array (SKA) are intended to measure the 21cm signal over a wide range of redshifts, representing an incredible opportunity in advancing our understanding about the nature of cosmic Reionization. At the same time these kind of experiments will present new challenges in processing the extensive amount of data generated, calling for the development of automated methods capable of precisely estimating physical parameters and their uncertainties. In this paper we employ Variational Inference, and in particular Bayesian Neural Networks, as an alternative to MCMC in 21 cm observations to report credible estimations for cosmological and astrophysical parameters and assess the correlations among them.
