Constraining the Reionization History using Bayesian Normalizing Flows
Constraining the Reionization History using Bayesian Normalizing Flows
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DOI：
10.1088/2632-2153/aba6f1
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
H'ector J. Hort'ua;Luigi Malagò;Riccardo Volpi
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文献类型：
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作者：
H'ector J. Hort'ua;Luigi Malagò;Riccardo Volpi
即将进行的实验，例如再电离阵列的氢纪元 (HERA) 和平方公里阵列 (SKA)，旨在测量大范围红移的 21 厘米信号，这为增进我们对宇宙再电离本质的理解提供了绝佳的机会。与此同时，此类实验将在处理生成的大量数据方面提出新的挑战，要求开发能够精确估计物理参数及其不确定性的自动化方法。在此交付成果中，我们采用变分推理，特别是贝叶斯神经网络，作为 21 厘米观测中 MCMC 的替代方案，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。最后，我们实现了使用双射器来改进对角高斯近似后验，并能够从 21 cm 数据集中的非高斯信号中提取重要信息。
Upcoming experiments such as Hydrogen Epoch of Reionization Array(HERA) and the Square Kilometre Array (SKA) are intended to measure the 21 cm 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 deliverable 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. Finally, we have implemented the use of bijectors to improve the diagonal Gaussian approximate posteriors and be able to extract significant information from Non-Gaussian signal in the 21 cm dataset.