论文标题

比较恢复Lofar-Eor 21cm功率谱的前景拆除技术

Comparing Foreground Removal Techniques for Recovery of the LOFAR-EoR 21cm Power Spectrum

论文作者

Hothi, Ian, Chapman, Emma, Pritchard, Jonathan R., Mertens, F. G., Koopmans, L. V. E, Ciardi, B., Gehlot, B. K., Ghara, R., Ghosh, A., Giri, S. K., Iliev, I. T., Jelić, V., Zaroubi, S.

论文摘要

我们比较了在各种实验中使用的各种前景去除技术,这些技术在各种实验中都可以去除明亮的前景,以检测从电离时代中的中性氢的红移21厘米信号。在这项工作中,我们在10晚的Lofar数据中测试了去除技术的性能(FastICA,GMCA和GPR),并研究了恢复21cm信号上最新上限的可能性。有趣的是,我们发现GMCA和FastICA重现了$δ^2_ {21} <$(73)$^2 $^2 $ MK $^2 $ at $ K = 0.075〜H \ MATHRM {CMPC}^{ - 1} $,这是GPR的应用程序。我们还发现,FastICA和GMCA开始偏离\ textIt {k} -scales大于$ \ sim 0.1〜h \ mathrm {cmpc}^{ - 1} $的噪声限制。然后,我们通过模拟复制数据,以通过对各种仪器效应进行测试,以查看FastICA和GMCA的局限性。我们发现,在较大的\ textit {k} -scales上,没有单一的仪器效应,例如主梁效应或模式混合,可以解释FastICA和GMCA的恢复较差。然后,我们测试FastICA和GMCA的比例独立性,发现较低的\ textit {k} -scales可以通过较小数量的独立组件来建模。对于较大的尺度($ k \ gtrsim 0.1〜h \ mathrm {cmpc}^{ - 1} $),需要更多独立的组件来适合前景。我们得出的结论是,Lofar协作当前对GPR的使用是适当的删除技术。它既强大又易于过度适应,将来对GPR的合适优化进行了改进,以产生更深层次的限制。

We compare various foreground removal techniques that are being utilised to remove bright foregrounds in various experiments aiming to detect the redshifted 21cm signal of neutral hydrogen from the Epoch of Reionization. In this work, we test the performance of removal techniques (FastICA, GMCA, and GPR) on 10 nights of LOFAR data and investigate the possibility of recovering the latest upper limit on the 21cm signal. Interestingly, we find that GMCA and FastICA reproduce the most recent 2$σ$ upper limit of $Δ^2_{21} <$ (73)$^2$ mK$^2$ at $k=0.075~ h \mathrm{cMpc}^{-1}$, which resulted from the application of GPR. We also find that FastICA and GMCA begin to deviate from the noise-limit at \textit{k}-scales larger than $\sim 0.1 ~h \mathrm{cMpc}^{-1}$. We then replicate the data via simulations to see the source of FastICA and GMCA's limitations, by testing them against various instrumental effects. We find that no single instrumental effect, such as primary beam effects or mode-mixing, can explain the poorer recovery by FastICA and GMCA at larger \textit{k}-scales. We then test scale-independence of FastICA and GMCA, and find that lower \textit{k}-scales can be modelled by a smaller number of independent components. For larger scales ($k \gtrsim 0.1~h \mathrm{cMpc}^{-1}$), more independent components are needed to fit the foregrounds. We conclude that, the current usage of GPR by the LOFAR collaboration is the appropriate removal technique. It is both robust and less prone to overfitting, with future improvements to GPR's fitting optimisation to yield deeper limits.

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