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arXiv 2501.12928astro-ph.IMastro-ph.CO
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BayesEoR:基于干涉可见度的贝叶斯21厘米功率谱估计

BayesEoR: Bayesian 21-cm Power Spectrum Estimation from Interferometric Visibilities

Peter H. Sims, Jacob Burba, Jonathan C. Pober

AI总结:

BayesEoR是一个GPU加速、MPI兼容的Python包,利用贝叶斯框架联合拟合21厘米EoR功率谱和前景模型,并通过MultiNest计算贝叶斯证据,支持模型选择。

摘要:

BayesEoR是一个GPU加速、兼容MPI的Python包,用于从再电离时代(EoR)的干涉观测中估计红移21厘米发射的功率谱。利用贝叶斯框架,BayesEoR联合拟合21厘米EoR功率谱和“前景”模型(指我们与宇宙学信号之间明亮的污染发射),并对观测这些信号的仪器进行正向建模。为了执行采样,我们使用MultiNest [arXiv:1402.0004],该工具在分析中计算贝叶斯证据。因此,BayesEoR也可用作模型选择的工具[参见例如arXiv:1701.03384]。

英文摘要:

BayesEoR is a GPU-accelerated, MPI-compatible Python package for estimating the power spectrum of redshifted 21-cm emission from interferometric observations of the Epoch of Reionization (EoR). Utilizing a Bayesian framework, BayesEoR jointly fits for the 21-cm EoR power spectrum and a "foreground" model, referring to bright, contaminating emission between us and the cosmological signal, and forward models the instrument with which these signals are observed. To perform the sampling, we use MultiNest [arXiv:1402.0004], which calculates the Bayesian evidence as part of the analysis. Thus, BayesEoR can also be used as a tool for model selection [see e.g. arXiv:1701.03384].

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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