The CMB angular power spectrum via component separation: a study on
  Planck data
  We investigate the extent to which foreground cleaned CMB maps can be used to
estimate the cosmological parameters at small scales. We use the SMICA method,
a blind separation technique which works directly at the spectral level. In
this work we focus on the small scales of the CMB angular power spectrum, which
are chiefly affected by noise and extragalactic foregrounds, such as point
sources. We adapt SMICA to use only cross-spectra between data maps, thus
avoiding the noise bias. In this study, performed both on simulations and on
Planck 2015 data, we fit for extragalactic point sources by modeling them as
shot noise of two independent populations. In simulations we correctly recover
the point source emission law, and obtain a CMB angular power spectrum that has
an average foreground residual of one fifth of the CMB power at $\ell \geq$
2200. On Planck data, the recovered point source emission law corresponds to
external estimates, with some offsets at the highest and lowest frequencies,
possibly due to frequency decoherence of point sources. The CMB angular power
spectrum residuals are consistent with what we find in simulations. The
cosmological parameters obtained from the simulations and the data show offsets
up to 1$\sigma$ on average from their expected values. Biases on cosmological
parameters in simulations represent the expected level of bias in Planck data.
The results on cosmological parameters depend on the detail of the foreground
residual contamination in the spectrum, and need a tailored modeling of the
likelihood foreground model.
