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We analyze the three-year WMAP temperature anisotropy data seeking to confirmthe power spectrum and likelihoods published by the WMAP team. We apply fiveindependent implementations of four algorithms to the power spectrum estimationand two implementations to the parameter estimation. Our single most importantresult is that we broadly confirm the WMAP power spectrum and analysis. Still,we do find two small but potentially important discrepancies: On large angularscales there is a small power excess in the WMAP spectrum (5-10% at l<~30)primarily due to likelihood approximation issues between 13 <= l <~30. On smallangular scales there is a systematic difference between the V- and W-bandspectra (few percent at l>~300). Recently, the latter discrepancy was explainedby Huffenberger et al. (2006) in terms of over-subtraction of unresolved pointsources. As far as the low-l bias is concerned, most parameters are affected bya few tenths of a sigma. The most important effect is seen in n_s. For thecombination of WMAP, Acbar and BOOMERanG, the significance of n_s =/ 1 dropsfrom ~2.7 sigma to ~2.3 sigma when correcting for this bias. We propose a fewsimple improvements to the low-l WMAP likelihood code, and introduce twoimportant extensions to the Gibbs sampling method that allows for propersampling of the low signal-to-noise regime. Finally, we make the products fromthe Gibbs sampling analysis publically available, thereby providing a fast andsimple route to the exact likelihood without the need of expensive matrixinversions.

A Reanalysis of the 3 YearWilkinson Microwave Anisotropy ProbeTemperature Power Spectrum and Likelihood