Systematic Effects of Foreground Removal in 21cm Surveys of Reionization
Nada Petrovic, S. Peng Oh

TL;DR
This paper investigates the systematic biases introduced by foreground removal in 21cm reionization surveys, proposing methods to mitigate bias and improve power spectrum estimation accuracy.
Contribution
It demonstrates how large-scale mode removal biases the power spectrum, proposes marginalization techniques, and analyzes the impact on the 21cm PDF and reionization stages.
Findings
Large-scale mode removal causes bias in power spectrum estimates.
Marginalizing over small k reduces bias but increases variance.
Foreground removal distorts the 21cm PDF, especially in late reionization stages.
Abstract
It is well-known that foreground subtraction in 21cm surveys removes large scale power. We investigate associated systematic biases. We show that removing line-of-sight fluctuations on large scales aliases into suppression of the 3D power spectrum across a broad range of scales. This bias can be eliminated by marginalizing over small k in the 1D power spectrum; however, the unbiased estimator will have unavoidably larger variance. We also show that Gaussian realizations of the power spectrum permit accurate and extremely rapid Monte-Carlo simulations for error analysis; repeated realizations of the fully non-Gaussian field are unnecessary. We perform Monte-Carlo maximum-likelihood simulations of foreground removal which yield unbiased, minimum variance estimates of the power spectrum in agreement with Fisher matrix estimates. Foreground removal also distorts the 21cm PDF, reducing the…
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