当前位置: X-MOL 学术Mach. Learn. Sci. Technol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Constraining the Reionization History using Bayesian Normalizing Flows
Machine Learning: Science and Technology ( IF 6.013 ) Pub Date : 2020-09-06 , DOI: 10.1088/2632-2153/aba6f1
Héctor J. Hortúa , Luigi Malagò , Riccardo Volpi

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...

中文翻译:

使用贝叶斯归一化流约束电离历史

即将到来的实验,例如电离阵列的氢时代(HERA)和平方公里阵列(SKA),旨在在广泛的红移范围内测量21 cm信号,这代表了增进我们对宇宙电离性质的理解的不可思议的机会。同时,这类实验将在处理生成的大量数据时提出新的挑战,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。在此交付物中,我们采用变分推断,尤其是贝叶斯神经网络,作为21厘米观测中的MCMC的替代方法,以报告有关宇宙学和天文学参数的可靠估计,并评估它们之间的相关性。最后,
更新日期:2020-09-08
down
wechat
bug