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arXiv 2201.08205astro-ph.CO
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宇宙黎明21-cm功率谱的模拟及基于人工神经网络的额外射电模型分类

Emulation of the Cosmic Dawn 21-cm Power Spectrum and Classification of Excess Radio Models Using an Artificial Neural Network

Sudipta Sikder, Rennan Barkana, Itamar Reis, Anastasia Fialkov

AI总结:

提出基于人工神经网络的机器学习方法,模拟宇宙黎明和再电离时期的21-cm功率谱,并利用SKA模拟数据精确约束天体物理参数,同时以87%准确率分类高红移下的额外射电背景模型。

摘要:

下一代射电望远镜有望对氢的宇宙21-cm线进行详细测量。未来21-cm巡天产生的海量数据集将彻底改变我们对早期宇宙时期的理解。我们提出了一种基于人工神经网络(Artificial Neural Network)的机器学习方法,利用模拟来揭示再电离时期和宇宙黎明中的天体物理学。使用一个涵盖极宽21-cm信号范围的七参数天体物理模型,在红移6至30及波数$0.05$至$1 \ \ m{Mpc}^{-1}$范围内,我们以$10 - 20\%$的典型精度模拟了21-cm功率谱。作为一个实际例子,我们使用带有Square Kilometre Array (SKA)乐观噪声模型的功率谱训练了一个模拟器。对模拟SKA数据进行拟合,在宇宙微波背景的光深方面获得了$2.8\%$的典型测量精度,在星系晕的恒星形成效率方面为$34\%$,在星系晕的X射线效率方面为9.6倍。此外,通过我们的建模,我们从模拟SKA数据中重建了真实的21-cm功率谱,典型精度为$15 - 30\%$。除了标准天体物理模型外,我们还考虑了高红移处强额外射电背景的两种奇异可能性。我们使用神经网络来识别21-cm功率谱中存在的射电背景类型,对模拟SKA数据的识别准确率为$87\%$。

英文摘要:

The cosmic 21-cm line of hydrogen is expected to be measured in detail by the next generation of radio telescopes. The enormous dataset from future 21-cm surveys will revolutionize our understanding of early cosmic times. We present a machine learning approach based on an Artificial Neural Network that uses emulation in order to uncover the astrophysics in the epoch of reionization and cosmic dawn. Using a seven-parameter astrophysical model that covers a very wide range of possible 21-cm signals, over the redshift range 6 to 30 and wavenumber range $0.05$ to $1 \ \rm{Mpc}^{-1}$ we emulate the 21-cm power spectrum with a typical accuracy of $10 - 20\%$. As a realistic example, we train an emulator using the power spectrum with an optimistic noise model of the Square Kilometre Array (SKA). Fitting to mock SKA data results in a typical measurement accuracy of $2.8\%$ in the optical depth to the cosmic microwave background, $34\%$ in the star-formation efficiency of galactic halos, and a factor of 9.6 in the X-ray efficiency of galactic halos. Also, with our modeling we reconstruct the true 21-cm power spectrum from the mock SKA data with a typical accuracy of $15 - 30\%$. In addition to standard astrophysical models, we consider two exotic possibilities of strong excess radio backgrounds at high redshifts. We use a neural network to identify the type of radio background present in the 21-cm power spectrum, with an accuracy of $87\%$ for mock SKA data.

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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