4.7 Article

Comparing foreground removal techniques for recovery of the LOFAR-EoR 21 cm power spectrum

期刊

出版社

OXFORD UNIV PRESS
DOI: 10.1093/mnras/staa3446

关键词

methods: data analysis; methods: statistical; techniques: interferometric; cosmology: observations; cosmology: theory; dark ages, reionization, first stars

资金

  1. Royal Society Dorothy Hodgkin Fellowship
  2. Royal Society Enhancement Award
  3. European Research Council under ERC grant [638743]
  4. SKA-NL Roadmap grant from the Dutch ministry of OCW
  5. Science and Technology Facilities Council [ST/I000976/1, ST/T000473/1]
  6. Southeast Physics Network (SEP-Net)
  7. Croatian Science Foundation [IP-2018-01-2889]
  8. National Science Foundation [AST-1836019]
  9. STFC [ST/T000473/1, ST/I000976/1] Funding Source: UKRI
  10. European Research Council (ERC) [638743] Funding Source: European Research Council (ERC)

向作者/读者索取更多资源

The study compares the performance of foreground removal techniques in detecting the redshifted 21 cm signal of neutral hydrogen, finding that GMCA and FastICA are able to reproduce the latest upper limit signal but deviate from noise-limit at larger k-scales. It is also discovered that fewer independent components are needed for lower k-scales, while more are required for larger scales. Additionally, the study concludes that the current usage of GPR by the LOFAR collaboration is the appropriate removal technique.
We compare various foreground removal techniques that are being utilized to remove bright foregrounds in various experiments aiming to detect the redshifted 21 cm 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 21 cm signal. Interestingly, we find that GMCA and FastICA reproduce the most recent 2s upper limit of Delta(2)(21) <(73)(2) mK(2) at k= 0.075 hcMp(c-)1, which resulted from the application of GPR. We also find that FastICA and GMCA begin to deviate from the noise-limit at k-scales larger than similar to 0.1 hcMpc(-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 k-scales. We then test scale-independence of FastICA and GMCA, and find that lower k-scales can be modelled by a smaller number of independent components. For larger scales (k >= 0.1 hcMpc(-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 optimization to yield deeper limits.

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