A small fraction of the
data are common with the deep data set (see the papers). There are two
alternate binnings of the power spectrum, which are not independent; in
both the bin width is 200 in l except for the first bin.
* Even binning: 14 bins: l = 0-400, 400-600, 600-800, ..., 2800-3000.
* Odd binning: 14 bins: l = 0-300, 300-500, 500-700, ..., 2700-2900.
The file deep_windows.tar.gz
(compressed tar file) contains 14 files, one for each band:
* joint_3deep_std2_best_window_* (7 files).
* joint_3deep_alt2_best_window_* (6 files).
* joint_3deep_bigbin_best_window_5 (1 file).
The window functions are tabulated in bins of width 90 in l, i.e., l = 1-90, 91-180, ...; and the tabulated values should be divided by 90 to get the quantity plotted in Mason et al.(2003), WB(l)/l.
These files should be interpreted as follows:
There are 7 (std2) or 6 (alt2) entries, one for each band. Column 1 is the band number; column 2 is the band power qB, and column 3 is the uncertainty on the band power (square root of the diagonal element of the covariance matrix). Band powers are dimensionless; multiply by Tcmb2 to put them in temperature units.
Ignore the first 6 lines in this file. The first 7 (std2) or 6 (alt2) columns of the next 7 (std2) or 6 (alt2) lines give the 7 by 7 or 6 by 6 band-power covariance matrix. This is the inverse of the curvature (Hessian) matrix as given in equation 75 of Myers et al., and it can be used directly to give a gaussian approximation to the likelihood function.