1.  Final plots

  • Bicep correction of masks list
  • LiB and Bicep correction of null spectra results and thus correlations in covariance matrix :
to do :

2.  tensor to scalar ratio error

LiB
BICComming
  • LiB tensor to scalar error is lower than C2 isotropic optimization (previously NaMaster), Exept for 0.1 muK, where the error is rising.
  • Looking at the covariance matrix diagonals, xpure is lower than C2, as expected from the optimization process. But the off diagonal terms in the correlation matrix are actually higher, hence a higher error on the tensor to scalar ratio :
kll diagonalsXpure correlation matrixC2 correlation matrix
  • proposition :
    • The PCG is optimized for variance minimization. And not sigma r (or covariance) minimization ?
    • after correction, some simulaiton were null, thus increasing correlations.

3.  xpure VS NaMaster

BB
EB

4.  LiB :

  • PCG at 5 muK
  • 3 types of masks bins :
    • 10 => [2,10,20,50]
    • 20 => [2,20,50]
    • 30 => [2,30,50]
  • 2 noises entry :
    • first one is for the mask apodization
    • seconde one is for noise simulation
  • m2 = mode 2 xpure (pure BB, standard EE).

4.1  external cmb maps

cross + external cmb maps
xpure + external cmb maps
xpure generated cl
mll
  • notes :
    • Simulated spectra from input inpCellfile are sistematically biased in x2pure. Solution : use external maps (generated via healpy/pix). In that case, one must compute spectra up to the lmax used for cmb generated maps (synfast(lmax=...)).

5.  BICEP :

  • pixwindow must be corrected for the reconstructed spectra at nside 512. Since the xQML analysis is done on a nside=128 map, we multiply the beam spectrum b_\ell by the correcting factor b_\ell \rightarrow b_\ell p_\ell^{128}/p_\ell^{512} , where p_\ell^{nside} is the pixel window function.
cl noise weighting
cl No noise weighting
  • PCG using wieghted noise map :

We demand the total variance of the map to be \displaystyle \frac{\sigma^2}{n_{pix}} [\mu K\cdot arcmin] = \frac 1 {\sum_i 1/ V_i} which is equal to \displaystyle \frac{\sigma^2}{n_{pix}} if V_i = \sigma^2 (white noise case). Now we demand a weighting of the variance such that V_i = \beta w_i^{-1} with \beta the normalisation factor. Injecting V_i gives \beta = \displaystyle \frac{\sigma^2}{n_{pix}} \sum_i w_i ad finally \displaystyle V_i = \frac{\sigma^2}{ w_i} \frac{\sum_j w_j}{n_{pix}} .

normalized hit mapweighted noise map
  • min value of hit map inside mask : 0.1733
  • max value : 1.73