This section describes products that require special processing. Only one such product is available at this time; this section will be expanded with time as more products are added.
We distribute the minimum-variance (MV) lensing potential estimate presented in  as part of the 2014 data release. This map represents an estimate of the CMBCosmic Microwave background lensing potential on approximately 70% of the sky, and also forms the basis for the Planck 2014 lensing likelihood. It is produced using filtered temperature and polarization data from the SMICA DX11 CMBCosmic Microwave background map; its construction is discussed in detail in .
The estimate is contained in a single gzipped tarball named COM_CompMap_Lensing_2048_R0.00.tgz. Its contents are described below.
|dat_klm.fits||HEALPIX FITSFlexible Image Transfer Specification format alm, with||Contains the estimated lensing convergence.|
|mask.fits.gz||HEALPIX FITSFlexible Image Transfer Specification format map, with||Contains the lens reconstruction analysis mask.|
|nlkk.dat||ASCII text file, with columns = (, , )||The approximate noise .(and signal+noise, ) power spectrum of , for the fiducial cosmology used in|
Compton parameter map
We distribute here the Planck full mission Compton parameter maps (y-maps hereafter) obtained using the NILC and MILCA component separation algorithms as described in . We also provide the ILC weights per scale and per frequency that were used to produce these y-maps. IDL routines are also provide to allow the user to apply those weights. Compton parameters produced by keeping either the first or the second half of stable pointing periods are also provide and we call them FIRST and LAST y-maps. Additionally we construct a noise estimates of full mission Planck y-maps from the half difference of the FIRST and LAST y-maps. These estimates are used to construct standard deviation maps of the noise in the full mission Planck y-maps that are also provided. To complement this we also provide the power spectra of the noise estimate maps after correcting for inhomogeneities using the standard deviation maps. We also deliver foreground masks including point-source and galactic masks.