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arXiv 2311.16633astro-ph.COastro-ph.IM
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再电离时期的21厘米信号:利用高斯过程回归对前景去除的机器学习升级

21-cm Signal from the Epoch of Reionization: A Machine Learning upgrade to Foreground Removal with Gaussian Process Regression

Anshuman Acharya, Florent Mertens, Benedetta Ciardi, Raghunath Ghara, Léon V. E. Koopmans, Sambit K. Giri, Ian Hothi, Qing-Bo Ma, Garrelt Mellema, Satyapan Munshi

AI总结:

本文提出用变分自编码器在GRIZZLY模拟上训练的核升级高斯过程回归,以更准确地去除LOFAR前景并提取再电离时期21厘米信号,在多数情况下可在2σ不确定度内恢复信号形状与功率,并分析了红移依赖性。

摘要:

近年来,基于高斯过程回归(GPR)的框架已被开发出来,用于对低频阵列(LOFAR)收集的数据进行前景缓解,以测量再电离时期(EoR)和宇宙黎明时期的21厘米信号功率谱。然而,人们注意到,如果EoR信号协方差被错误估计,通过这种方法可能会产生显著的信号损失。为了获得更好的协方差模型,我们提议使用一个在GRIZZLY模拟上通过基于变分自编码器(VAE)的算法训练得到的核。在这项工作中,我们通过在各种模拟的模拟信号上测试这种基于机器学习的核(VAE核)与GPR结合使用的能力,探索了对应于LOFAR约10晚(约141小时)和约100晚(约1410小时)观测的噪声水平。我们的工作表明,在大多数情况下,使用VAE核有可能在2σ不确定度范围内成功提取21厘米信号,并且与先前使用的协方差模型相比,在形状和功率的恢复上都更好。我们还探讨了在过去GPR应用中识别出的过量噪声分量的作用,并进一步分析了红移对VAE核性能可能存在依赖性的可能性。后者使我们能够为未来在一系列红移处的LOFAR观测做好准备,并与其他望远镜的结果进行比较。

英文摘要:

In recent years, a Gaussian Process Regression (GPR) based framework has been developed for foreground mitigation from data collected by the LOw-Frequency ARray (LOFAR), to measure the 21-cm signal power spectrum from the Epoch of Reionization (EoR) and Cosmic Dawn. However, it has been noted that through this method there can be a significant amount of signal loss if the EoR signal covariance is misestimated. To obtain better covariance models, we propose to use a kernel trained on the {\tt GRIZZLY} simulations using a Variational Auto-Encoder (VAE) based algorithm. In this work, we explore the abilities of this Machine Learning based kernel (VAE kernel) used with GPR, by testing it on mock signals from a variety of simulations, exploring noise levels corresponding to $\approx$10 nights ($\approx$141 hours) and $\approx$100 nights ($\approx$1410 hours) of observations with LOFAR. Our work suggests the possibility of successful extraction of the 21-cm signal within 2$σ$ uncertainty in most cases using the VAE kernel, with better recovery of both shape and power than with previously used covariance models. We also explore the role of the excess noise component identified in past applications of GPR and additionally analyse the possibility of redshift dependence on the performance of the VAE kernel. The latter allows us to prepare for future LOFAR observations at a range of redshifts, as well as compare with results from other telescopes.

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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