A re-analysis of the three-year WMAP temperature power spectrum and likelihood

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Abstract
We analyze the three-year WMAP temperature anisotropy data seeking to confirm the power spectrum and likelihoods published by the WMAP team. We apply five independent implementations of four algorithms to the power spectrum estimation and two implementations to the parameter estimation. These results are then exhaustively cross-checked by the five different research groups who performed the work. This provides a high degree of confidence that the results are well understood with respect to both implicit and explicit assumptions. Our single most important result is that we broadly confirm the WMAP power spectrum and analysis. Still, we do find two small but potentially important discrepancies: On large angular scales there is a small power excess in the WMAP spectrum (5-10% at l < 50) primarily due to residual foregrounds and secondarily to numerical and statistical issues. On small angular scales there is a systematic difference between the V- and W-band spectra (few percent at l > 300). The origin of the latter discrepancy has not yet been identified, and this requires further attention. As far as the low-l bias is concerned, most parameters are affected by a few tenths of a sigma. The most important effect is seen in n_s, for which the evidence for n_s /= 1 is weakened by 0.4 sigma: For the combination of WMAP, Acbar and BOOMERanG, the significance of n_s /= 1 drops from ~2.7 sigma to ~2.3 sigma when correcting for this bias. Finally, we propose a few simple improvements to the low-l WMAP likelihood code that alleviate the low-l bias, and also introduce a few important extensions to the Gibbs sampling method that allows for proper sampling of the low signal-to-noise regime.

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