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arXiv 2204.04491astro-ph.CO
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用于全天平均21厘米信号提取的贝叶斯证据驱动似然选择

Bayesian evidence-driven likelihood selection for sky-averaged 21-cm signal extraction

K. H. Scheutwinkel, W. Handley, E. de Lera Acedo

AI总结:

该研究针对无似然推理中寻找数据集真实似然近似的问题,通过注入不同非高斯噪声的全天平均21厘米信号模拟实验,验证了贝叶斯证据可用于似然选择,提出噪声结构未知时可用广义正态似然近似真实似然。

摘要:

我们证明贝叶斯证据可用于找到数据集真实似然函数的良好近似,这是无似然推理(likelihood-free inference, LFI)范式的目标。作为具体示例,我们使用正演模拟的全天平均21厘米信号天线温度数据集,在其中人工注入了多种具有物理动机的噪声结构。我们发现,当噪声分布偏离高斯情况(例如异方差辐射噪声或重尾噪声)时,高斯似然的表现很差。对于这些非高斯噪声结构,我们表明广义正态似然的贝叶斯证据量级与注入噪声的真实似然函数相当,且全天平均21厘米信号恢复效果也相近。因此我们提出,当先验未知噪声结构时,广义正态似然函数可作为真实似然函数的良好近似。

英文摘要:

We demonstrate that the Bayesian evidence can be used to find a good approximation of the ground truth likelihood function of a dataset, a goal of the likelihood-free inference (LFI) paradigm. As a concrete example, we use forward modelled sky-averaged 21-cm signal antenna temperature datasets where we artificially inject noise structures of various physically motivated forms. We find that the Gaussian likelihood performs poorly when the noise distribution deviates from the Gaussian case e.g. heteroscedastic radiometric or heavy-tailed noise. For these non-Gaussian noise structures, we show that the generalised normal likelihood is on a similar Bayesian evidence scale with comparable sky-averaged 21-cm signal recovery as the ground truth likelihood function of our injected noise. We therefore propose the generalised normal likelihood function as a good approximation of the true likelihood function if the noise structure is a priori unknown.

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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