Planck Collaboration: The Planck mission axio Fig. 11. The SMICA CMB map (with 3% of the sky replaced by a constrained Gaussian realization). lensing potential φ(ˆn), as well as estimates of itspower spectrum φWF (ˆn) φφ CL . Although noisy, thePlanck lensing potential map represents a projected measurement of all dark matter back to the last scat- tering surface, with considerable statistical power. InFig. 7.2 we plot the Planck lensing map, and in Fig. 7.2 we show an esti- mate of its signal power spectrum. I have no idea why the fig- urenumbers come out tobe 5.3 no matter what I do... - latex expert needed As a tracer of the large scale gravitational potential, the Planck lensing map issignificantly correlated with other tracers Galactic North Galactic South of large scale structure. We show several representative exam- ples of such correlations in Planck Collaboration XVII (2013), Fig. 14. Wiener-filtered lensing potential estimate reconstruction, in Galactic coordinates using orthographic projection. The reconstruction including the NVSS quasar catalog (Condon et al. 1998), the was bandpass filtered to L ∈ [10, 2048]. Note that the lensing recon- MaxBCG cluster catalog (Koester et al. 2007), luminous red struction, while highly statistically significant, is still noise dominated galaxies from SDSS Ross et al. (2011), and a survey of in- for every individual mode, and isat best S/ N 0.7 around L = 30. frared sources from the WISE satellite (Wright et al. 2010). The strength of the correlation between the Planck lensing map and such tracers provides athfairly direct measure of how they trace (Planck Collaboration XV 2013); here we summarize its main dark matter; from10our measurement Patrasof the lensing Workshop,potential, the features. CERN, 3.07.2014 Planck maps provide a mass survey of the intermediate redshift On large scales, the distribution for the angular power spec- Universe, in addition to a survey of the primary CMB tempera- trum cannot be assumed to be a multivariate Gaussian, and the ture and polarization anisotropies. Galactic contamination is most significant. We use the multi- Julien Lesgourgues (EPFL,frequency temperature CERN,maps from LFI LAPThand HFI, in the) range 7.3. Likelihood code 30 < ν < 353 GHz, to separate Galactic foregrounds. This pro- cedure uses a Gibbs sampling method to estimate the CMB map 7.3.1. CMB likelihood and the probability distribution of its power spectrum, p(C |d), for bandpowers at < 50, using thecleanest 87 % of thesky. We We follow a hybrid approach to construct the likelihood for the supplement this ‘low- ’ temperature likelihood with the pixel- Planck temperature data, using an exact likelihood approach at based polarization likelihood at large-scales ( < 23) from the large scales, < 50, and a pseudo-C power spectrum at smaller WMAP 9-year data release (Bennett et al. 2012). These need to 3.07.2014scales, 50 < < 2500. This follows similarCMB,analyses DM,in, WISPse.g., be corrected& Axionsfor the– J.dust Lesgourguescontamination, for which we use the 1 Spergel et al. (2007). The likelihood is described more fully in WMAP procedure. However, we have checked that switching 24 Planck Collaboration: The Planck mission axio Fig. 11. The SMICA CMB map (with 3% of the sky replaced by a constrained Gaussian realization). lensing potential φ(ˆn), as well as estimates of itspower spectrum φWF (ˆn) φφ CL . Although noisy, thePlanck lensing potential map represents a projected measurement of all dark matter back to the last scat- tering surface, with considerable statistical power. InFig. 7.2 we plot the Planck lensing map, and in Fig. 7.2 we show an esti- mate of its signal power spectrum. I have no idea why the fig- urenumbers come out tobe 5.3 no matter what I do... - latex expert needed As a tracer of the large scale gravitational potential, the Planck lensing map issignificantly correlated with other tracers Galactic North Galactic South of large scale structure. We show several representative exam- ples of such correlations in Planck Collaboration XVII (2013), Fig. 14. Wiener-filtered lensing potential estimate reconstruction, in Galactic coordinates using orthographic projection. The reconstruction including the NVSS quasar catalog (Condon et al. 1998), the was bandpass filtered to L ∈ [10, 2048]. Note that the lensing recon- MaxBCG cluster catalog (Koester et al. 2007), luminous red struction, while highly statistically significant, is still noise dominated Temperature galaxiesspectrumfrom SDSS Ross et al. (2011), and a survey of in- for every individual mode, and isat best S/ N 0.7 around L = 30. frared sources from the WISE satellite (Wright et al. 2010). The strength of the correlation between the Planck lensing map and from Marchsuch 2013tracers provides a fairly direct measure of how they trace (Planck Collaboration XV 2013); here we summarize its main dark matter; from our measurement of the lensing potential, the features. Planck maps provide a mass survey of the intermediate redshift On large scales, the distribution for the angular power spec- Universe, in addition to a survey of the primary CMB tempera- trum cannot be assumed to be a multivariate Gaussian, and the ture and polarization anisotropies. Galactic contamination is most significant. We use the multi- frequency temperature maps from LFI and HFI, in the range 7.3. Likelihood code 30 < ν < 353 GHz, to separate Galactic foregrounds. This pro- cedure uses a Gibbs sampling method to estimate the CMB map 7.3.1. CMB likelihood and the probability distribution of its power spectrum, p(C |d), for bandpowers at < 50, using thecleanest 87 % of thesky. We We follow a hybrid approach to construct the likelihood for the supplement this ‘low- ’ temperature likelihood with the pixel- Planck temperature data, using an exact likelihood approach at based polarization likelihood at large-scales ( < 23) from the large scales, < 50, and a pseudo-C power spectrum at smaller WMAP 9-year data release (Bennett et al. 2012). These need to 3.07.2014scales, 50 < < 2500. This follows similarCMB,analyses DM,in, WISPse.g., be corrected& Axionsfor the– J.dust Lesgourguescontamination, for which we use the 2 Spergel et al. (2007). The likelihood is described more fully in WMAP procedure. However, we have checked that switching 24 Planck Collaboration: Gravitational lensing by large-scale structures with Planck maps). To match the power spectrum of these simulations to the Power spectrum estimates at this mask level show consis- power spectrum of the data maps, we find it isnecessary to add tency with the MV reconstruction within two standard devia- extragalactic foreground power following the model in Sect. 4, tions of the measurement uncertainty. The increased sky cover- 2 2 with Acib = 18 µK and Asrc = 28 µK . The resulting simula- age does not bring significant improvements in the error-bars of tions have a power spectrum which agrees with that of the CMB thepower spectrum, however. Using Eq. 20 as an estimate of the map estimate based on the data to better than 2% at l < 2048. power spectrum variance, the larger sky coverage yields only a This could be improved slightly by tailoring a specific correc- 3.5% improvement at L < 40 over the MV result, decreasing tion for each map. We also add homogeneous pixel noise with a down to 0 at L = 400. This could be due to the different weight- Planck Collaboration: The Planck mission level of 12 µK arcmin. If we neglected this power, theagreement ing used in the component separation compared to the one of would be only at the 8% level, primarily due to the noise term the MV map, which results in slightly noisier maps for our pur- (the Acib and Asrc contributions are each at the level of 1 − 2%). pose. While the component separated maps allow for a reduced Due to the procedure which we use to subtract the disconnected mask maintaining arobust lensing potential estimation, they lead noise bias (Eq. 17) from our lensing power spectrum estimates, to a marginal improvement of the power spectrum uncertainties. the inclusion of these components does not significantly affect Nevertheless, their agreement with the MV result isreassuring. our results, but comparison with the values used for our single- frequency simulations in Sect. 4 are a useful indicator of the ex- tent to which the foreground separation algorithms are able to remove extragalactic foreground power in the high- regime. As already discussed, our results on the component- separated CMB maps are presented in Fig. 18. Because the CMB and FFP6 noise components of the foreground-cleaned map simulations are the same as those used to characterize our fiducial lens reconstruction, we can measure the expected scatter between the foreground separated maps and our fidu- cial reconstruction. This scatter will be slightly overestimated because we have not attempted to coherently model the con- tribution to the reconstruction noise from residual diffuse eaxiox- MV, fsky = 0.70 tragalactic foreground power. For the eight bins in 40 ≤ L ≤ 400 on which ourFig. 11.fiducialThe SMICAlikCMBelihoodmap (withis3%based,of the skywereplacedmeasureby a constraineda Gaussian realization). χ2 for the difference between our fiducial reconstruction and 2 the correspondinglensingforepotentialground-cleanedφ(ˆn), as well as estimatesreconstructionof itspower spectrumof χ =φWF (ˆn) φφ (3.14, 4.3, 2.5, 14C.7)L . Althoughfor nilnoisy c, smica, thePlanc, sevemk lensing, potentialand r mapul errepresentsrespec- 2a projected measurement of all dark matter back to the last scat- tively. These