A method for 21 cm power spectrum estimation in the presence of foregrounds

The technique of 21 cm tomography promises to be a powerful tool for estimating cosmological parameters, constraining the epoch of reionization, and probing the so-called dark ages. However, realizing this promise will require the extraction of a cosmological power spectrum from beneath overwhelming...

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Bibliographic Details
Main Authors: Liu, Adrian Chi-Yan (Contributor), Tegmark, Max Erik (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Physics (Contributor), MIT Kavli Institute for Astrophysics and Space Research (Contributor)
Format: Article
Language:English
Published: American Physical Society, 2011-09-30T12:54:34Z.
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Online Access:Get fulltext
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100 1 0 |a Liu, Adrian Chi-Yan  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Physics  |e contributor 
100 1 0 |a MIT Kavli Institute for Astrophysics and Space Research  |e contributor 
100 1 0 |a Tegmark, Max Erik  |e contributor 
100 1 0 |a Liu, Adrian Chi-Yan  |e contributor 
100 1 0 |a Tegmark, Max Erik  |e contributor 
700 1 0 |a Tegmark, Max Erik  |e author 
245 0 0 |a A method for 21 cm power spectrum estimation in the presence of foregrounds 
260 |b American Physical Society,   |c 2011-09-30T12:54:34Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/66128 
520 |a The technique of 21 cm tomography promises to be a powerful tool for estimating cosmological parameters, constraining the epoch of reionization, and probing the so-called dark ages. However, realizing this promise will require the extraction of a cosmological power spectrum from beneath overwhelmingly large sources of foreground contamination. In this paper, we develop a unified matrix-based framework for foreground subtraction and power spectrum estimation, which allows us to quantify the errors and biases that arise in the power spectrum as a result of foreground subtraction. We find that existing line-of-sight foreground subtraction proposals can lead to substantial mode mixing as well as residual noise and foreground biases, whereas our proposed inverse-variance foreground subtraction eliminates noise and foreground biases, gives smaller error bars, and produces less correlated measurements of the power spectrum. We also numerically confirm the intuitive belief in the literature that 21 cm foreground subtraction is best done using frequency rather than angular information. 
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