Joshua S. Dillon, Adrian Liu, Christopher L. Williams, Jacqueline N. Hewitt, Max Tegmark, Edward H. Morgan, Alan M. Levine, Miguel F. Morales, Steven J. Tingay, Gianni Bernardi, Judd D. Bowman, Frank H. Briggs, David Emrich, Daniel A. Mitchell, Divya Oberoi, Thiagaraj Prabu, Randall Wayth, Rachel L. Webster
We present a technique for bridging the gap between idealized inverse covariance weighted quadratic estimation of 21 cm power spectra and the real-world challenges presented universally by interferometric observation. By carefully evaluating various estimators and adapting our techniques for large but incomplete data sets, we develop an optimal power spectrum estimation framework that preserves the so-called "EoR window" and keeps track of estimator errors and covariances. We apply our method to observations from the 32-tile prototype of the Murchinson Widefield Array to demonstrate the importance of a judicious analysis technique. Lastly, we apply our method to investigate the dependence of the clean EoR window on frequency--especially the frequency dependence of the so-called "wedge" feature--and establish upper limits on the power spectrum from z=6.2 to z=11.7. Our lowest limit is Delta(k) < 0.3 Kelvin at 95% confidence at a comoving scale k = 0.046 Mpc^-1 and z = 9.5.
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http://arxiv.org/abs/1304.4229
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