Issue 
A&A
Volume 594, October 2016
Planck 2015 results



Article Number  A15  
Number of page(s)  28  
Section  Cosmology (including clusters of galaxies)  
DOI  https://doi.org/10.1051/00046361/201525941  
Published online  20 September 2016 
Planck 2015 results
XV. Gravitational lensing
^{1} APC, AstroParticule et Cosmologie, Université Paris Diderot, CNRS/IN2P3, CEA/lrfu, Observatoire de Paris, Sorbonne Paris Cité, 10 rue Alice Domon et Léonie Duquet, 75205 Paris Cedex 13, France
^{2} Aalto University Metsähovi Radio Observatory and Dept of Radio Science and Engineering, PO Box 13000, 00076 AALTO, Finland
^{3} African Institute for Mathematical Sciences, 6–8 Melrose Road, Muizenberg, 7945 Cape Town, South Africa
^{4} Agenzia Spaziale Italiana Science Data Center, via del Politecnico snc, 00133 Roma, Italy
^{5} Aix Marseille Université, CNRS, LAM (Laboratoire d’Astrophysique de Marseille), UMR 7326, 13388 Marseille, France
^{6} Astrophysics Group, Cavendish Laboratory, University of Cambridge, J J Thomson Avenue, Cambridge CB3 0HE, UK
^{7} Astrophysics & Cosmology Research Unit, School of Mathematics, Statistics & Computer Science, University of KwaZuluNatal, Westville Campus, Private Bag X54001, Durban 4000, South Africa
^{8} Atacama Large Millimeter/submillimeter Array, ALMA Santiago Central Offices, Alonso de Cordova 3107, Vitacura, Casilla 763 0355, Santiago, Chile
^{9} CGEE, SCS Qd 9, Lote C, Torre C, 4° andar, Ed. Parque Cidade Corporate, CEP 70308200, Brasília, DF, Brazil
^{10} CITA, University of Toronto, 60 St. George St., Toronto, ON M5S 3H8, Canada
^{11} CNRS, IRAP, 9 Av. colonel Roche, BP 44346, 31028 Toulouse Cedex 4, France
^{12} CRANN, Trinity College, Dublin 2, Ireland
^{13} California Institute of Technology, Pasadena, CA 91125 California, USA
^{14} Centre for Theoretical Cosmology, DAMTP, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
^{15} Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Plaza San Juan, 1, planta 2, 44001 Teruel, Spain
^{16} Computational Cosmology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 California, USA
^{17} Consejo Superior de Investigaciones Científicas (CSIC), 28006 Madrid, Spain
^{18} DSM/Irfu/SPP, CEASaclay, 91191 GifsurYvette Cedex, France
^{19} DTU Space, National Space Institute, Technical University of Denmark, Elektrovej 327, 2800 Kgs. Lyngby, Denmark
^{20} Département de Physique Théorique, Université de Genève, 24, Quai E. Ansermet, 1211 Genève 4, Switzerland
^{21} Departamento de Astrofísica, Universidad de La Laguna (ULL), 38206 La Laguna, Tenerife, Spain
^{22} Departamento de Física, Universidad de Oviedo, Avda. Calvo Sotelo s/n, 33003 Oviedo, Spain
^{23} Department of Astronomy and Astrophysics, University of Toronto, 50 Saint George Street, Toronto, Ontario ONMSR 0A3, Canada
^{24} Department of Astrophysics/IMAPP, Radboud University Nijmegen, PO Box 9010, 6500 GL Nijmegen, The Netherlands
^{25} Department of Physics & Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver BC V6T 1Z4, British Columbia, Canada
^{26} Department of Physics and Astronomy, Dana and David Dornsife College of Letter, Arts and Sciences, University of Southern California, Los Angeles, CA 90089, USA
^{27} Department of Physics and Astronomy, University College London, London WC1E 6BT, UK
^{28} Department of Physics and Astronomy, University of Sussex, Brighton BN1 9QH, UK
^{29} Department of Physics, Florida State University, Keen Physics Building, 77 Chieftan Way, Tallahassee, Florida FL 32306, USA
^{30} Department of Physics, Gustaf Hällströmin katu 2a, University of Helsinki, 00100 Helsinki, Finland
^{31} Department of Physics, Princeton University, Princeton, New Jersey NJ 08544, USA
^{32} Department of Physics, University of California, Berkeley, California CA 93106, USA
^{33} Department of Physics, University of California, Santa Barbara, California CA 93106, USA
^{34} Department of Physics, University of Illinois at UrbanaChampaign, 1110 West Green Street, Urbana, Illinois, USA
^{35} Dipartimento di Fisica e Astronomia G. Galilei, Università degli Studi di Padova, via Marzolo 8, 35131 Padova, Italy
^{36} Dipartimento di Fisica e Scienze della Terra, Università di Ferrara, via Saragat 1, 44122 Ferrara, Italy
^{37} Dipartimento di Fisica, Università La Sapienza, P. le A. Moro 2, Roma, Italy
^{38} Dipartimento di Fisica, Università degli Studi di Milano, via Celoria, 16, Milano, Italy
^{39} Dipartimento di Fisica, Università degli Studi di Trieste, via A. Valerio 2, Trieste, Italy
^{40} Dipartimento di Matematica, Università di Roma Tor Vergata, via della Ricerca Scientifica, 1, Roma, Italy
^{41} Discovery Center, Niels Bohr Institute, Blegdamsvej 17, Copenhagen, Denmark
^{42} Discovery Center, Niels Bohr Institute, Copenhagen University, Blegdamsvej 17, Copenhagen, Denmark
^{43} European Southern Observatory, ESO Vitacura, Alonso de Cordova 3107, Vitacura, Casilla 19001, Santiago, Chile
^{44} European Space Agency, ESAC, Planck Science Office, Camino bajo del Castillo, s/n, Urbanización Villafranca del Castillo, 28692 Villanueva de la Cañada, Madrid, Spain
^{45} European Space Agency, ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands
^{46} Facoltà di Ingegneria, Università degli Studi eCampus, via Isimbardi 10, 22060 Novedrate (CO), Italy
^{47} Gran Sasso Science Institute, INFN, viale F. Crispi 7, 67100 L’ Aquila, Italy
^{48} HGSFP and University of Heidelberg, Theoretical Physics Department, Philosophenweg 16, 69120 Heidelberg, Germany
^{49} Helsinki Institute of Physics, Gustaf Hällströmin katu 2, University of Helsinki, 00100 Helsinki, Finland
^{50} INAF–Osservatorio Astronomico di Padova, Vicolo dell’Osservatorio 5, 35141 Padova, Italy
^{51} INAF–Osservatorio Astronomico di Roma, via di Frascati 33, Monte Porzio Catone, 00136 Roma, Italy
^{52} INAF–Osservatorio Astronomico di Trieste, via G.B. Tiepolo 11, 34131 Trieste, Italy
^{53} INAF/IASF Bologna, via Gobetti 101, 40127 Bologna, Italy
^{54} INAF/IASF Milano, via E. Bassini 15, 28 Milano, Italy
^{55} INFN, Sezione di Bologna, via Irnerio 46, 40126 Bologna, Italy
^{56} INFN, Sezione di Roma 1, Università di Roma Sapienza, Piazzale Aldo Moro 2, 00185 Roma, Italy
^{57} INFN, Sezione di Roma 2, Università di Roma Tor Vergata, via della Ricerca Scientifica, 1 Roma, Italy
^{58} INFN/National Institute for Nuclear Physics, via Valerio 2, 34127 Trieste, Italy
^{59} IPAG: Institut de Planétologie et d’Astrophysique de Grenoble, Université Grenoble Alpes, IPAG, 38000 Grenoble, France
^{60} CNRS, IPAG, 38000 Grenoble, France
^{61} IUCAA, Post Bag 4, Ganeshkhind, Pune University Campus, 411 007 Pune, India
^{62} Imperial College London, Astrophysics group, Blackett Laboratory, Prince Consort Road, London, SW7 2AZ, UK
^{63} Infrared Processing and Analysis Center, California Institute of Technology, Pasadena, CA 91125, USA
^{64} Institut Néel, CNRS, Université Joseph Fourier Grenoble I, 25 rue des Martyrs, 38401 SaintMartind’Hères, Grenoble, France
^{65} Institut Universitaire de France, 103 bd SaintMichel, 75005 Paris, France
^{66} Institut d’Astrophysique Spatiale, CNRS (UMR8617) Université ParisSud 11, Bâtiment 121, 91400 Orsay, France
^{67} Institut d’Astrophysique de Paris, CNRS (UMR7095), 98 bis Boulevard Arago, 75014 Paris, France
^{68} Institut für Theoretische Teilchenphysik und Kosmologie, RWTH Aachen University, 52056 Aachen, Germany
^{69} Institute for Space Sciences, 077125 BucharestMagurale, Romania
^{70} Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
^{71} Institute of Theoretical Astrophysics, University of Oslo, Blindern, 0316 Oslo, Norway
^{72} Instituto de Astrofísica de Canarias, C/Vía Láctea s/n, La Laguna, 38205 Tenerife, Spain
^{73} Instituto de Física de Cantabria (CSICUniversidad de Cantabria), Avda. de los Castros s/n, Santander, Spain
^{74} Istituto Nazionale di Fisica Nucleare, Sezione di Padova, via Marzolo 8, 35131 Padova, Italy
^{75} Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, California CA 91125, USA
^{76} Jodrell Bank Centre for Astrophysics, Alan Turing Building, School of Physics and Astronomy, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK
^{77} Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
^{78} Kavli Institute for Cosmology Cambridge, Madingley Road, Cambridge, CB3 0HA, UK
^{79} Kazan Federal University, 18 Kremlyovskaya St., 420008 Kazan, Russia
^{80} LAL, Université ParisSud, CNRS/IN2P3, 91400 Orsay, France
^{81} LERMA, CNRS, Observatoire de Paris, 61 Avenue de l’Observatoire, 75014 Paris, France
^{82} Laboratoire AIM, IRFU/Service d’Astrophysique – CEA/DSM – CNRS – Université Paris Diderot, Bât. 709, CEASaclay, 91191 GifsurYvette Cedex, France
^{83} Laboratoire Traitement et Communication de l’Information, CNRS (UMR 5141) and Télécom ParisTech, 46 rue Barrault, 75634 Paris Cedex 13, France
^{84} Laboratoire de Physique Subatomique et Cosmologie, Université GrenobleAlpes, CNRS/IN2P3, 53 rue des Martyrs, 38026 Grenoble Cedex, France
^{85} Laboratoire de Physique Théorique, Université ParisSud 11 & CNRS, Bâtiment 210, 91405 Orsay, France
^{86} Lawrence Berkeley National Laboratory, Berkeley, California CA 94720, USA
^{87} Lebedev Physical Institute of the Russian Academy of Sciences, Astro Space Centre, 84/32 Profsoyuznaya st., 117997 Moscow, GSP7, Russia
^{88} MaxPlanckInstitut für Astrophysik, KarlSchwarzschildStr. 1, 85741 Garching, Germany
^{89} McGill Physics, Ernest Rutherford Physics Building, McGill University, 3600 rue University, Montréal, QC, H3A 2T8, Canada
^{90} National University of Ireland, Department of Experimental Physics, Maynooth, Co. Kildare, Ireland
^{91} Nicolaus Copernicus Astronomical Center, Bartycka 18, 00716 Warsaw, Poland
^{92} Niels Bohr Institute, Blegdamsvej 17, 2100 Copenhagen, Denmark
^{93} Niels Bohr Institute, Copenhagen University, Blegdamsvej 17, 2100 Copenhagen, Denmark
^{94} Nordita (Nordic Institute for Theoretical Physics), Roslagstullsbacken 23, 106 91 Stockholm, Sweden
^{95} Optical Science Laboratory, University College London, Gower Street, London WC1E 6BT, UK
^{96} SISSA, Astrophysics Sector, via Bonomea 265, 34136, Trieste, Italy
^{97} School of Physics and Astronomy, Cardiff University, Queens Buildings, The Parade, Cardiff, CF24 3AA, UK
^{98} School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK
^{99} Sorbonne UniversitéUPMC, UMR7095, Institut d’Astrophysique de Paris, 98 bis Boulevard Arago, 75014 Paris, France
^{100} Space Research Institute (IKI), Russian Academy of Sciences, Profsoyuznaya Str, 84/32, 117997 Moscow, Russia
^{101} Space Sciences Laboratory, University of California, Berkeley, California, USA
^{102} Special Astrophysical Observatory, Russian Academy of Sciences, Nizhnij Arkhyz, Zelenchukskiy region, 369167 KarachaiCherkessian Republic, Russia
^{103} Stanford University, Dept of Physics, Varian Physics Bldg, 382 via Pueblo Mall, Stanford, California CA 94305, USA
^{104} SubDepartment of Astrophysics, University of Oxford, Keble Road, Oxford OX1 3RH, UK
^{105} The Oskar Klein Centre for Cosmoparticle Physics, Department of Physics, Stockholm University, AlbaNova, 106 91 Stockholm, Sweden
^{106} Theory Division, PHTH, CERN, 1211, Geneva 23, Switzerland
^{107} UPMC Univ Paris 06, UMR7095, 98 bis Boulevard Arago, 75014 Paris, France
^{108} Université de Toulouse, UPSOMP, IRAP, 31028 Toulouse Cedex 4, France
^{109} Universities Space Research Association, Stratospheric Observatory for Infrared Astronomy, MS 23211, Moffett Field, CA 94035, USA
^{110} University of Granada, Departamento de Física Teórica y del Cosmos, Facultad de Ciencias, 18010 Granada, Spain
^{111} University of Granada, Instituto Carlos I de Física Teórica y Computacional, 18010 Granada, Spain
^{112} Warsaw University Observatory, Aleje Ujazdowskie 4, 00478 Warszawa, Poland
^{⋆}
Corresponding author: D. Hanson, email: duncan.hanson@gmail.com
Received: 23 February 2015
Accepted: 15 December 2015
We present the most significant measurement of the cosmic microwave background (CMB) lensing potential to date (at a level of 40σ), using temperature and polarization data from the Planck 2015 fullmission release. Using a polarizationonly estimator, we detect lensing at a significance of 5σ. We crosscheck the accuracy of our measurement using the wide frequency coverage and complementarity of the temperature and polarization measurements. Public products based on this measurement include an estimate of the lensing potential over approximately 70% of the sky, an estimate of the lensing potential power spectrum in bandpowers for the multipole range 40 ≤ L ≤ 400, and an associated likelihood for cosmological parameter constraints. We find good agreement between our measurement of the lensing potential power spectrum and that found in the ΛCDM model that best fits the Planck temperature and polarization power spectra. Using the lensing likelihood alone we obtain a percentlevel measurement of the parameter combination σ_{8}Ω^{0.25}_{m} = 0.591 ± 0.021. We combine our determination of the lensing potential with the Emode polarization, also measured by Planck, to generate an estimate of the lensing Bmode. We show that this lensing Bmode estimate is correlated with the Bmodes observed directly by Planck at the expected level and with a statistical significance of 10σ, confirming Planck’s sensitivity to this known sky signal. We also correlate our lensing potential estimate with the largescale temperature anisotropies, detecting a crosscorrelation at the 3σ level, as expected because of dark energy in the concordance ΛCDM model.
Key words: gravitational lensing: weak / cosmological parameters / cosmic background radiation / largescale structure of Universe / cosmology: observations
© ESO, 2016
1. Introduction
The cosmic microwave background (CMB) gives us a direct measurement of the early Universe when it first became transparent to radiation just 375 000 years after the Big Bang. It contains distinct signatures of the later Universe as well, which were imprinted by the process of gravitational lensing. The CMB photons that we observe today last scattered approximately 14 billion years ago, and have travelled most of the way across the observable Universe to reach us. During their journey, their paths were distorted by the gravitational tug of intervening matter, a subtle effect that may be measured statistically with high angular resolution, lownoise observations of the CMB, such as those provided by Planck. In this paper, we present lensing measurements that are based on the fullmission 2015 data release. We produce the most powerful measurement of CMB lensing to date with a 2.5% constraint on the amplitude of the lensing potential power spectrum (or alternatively, a 40σ detection of lensing effects).
The effect of lensing is to remap the CMB fluctuations, so that the observed anisotropy in direction is in fact the unlensed, “primordial” anisotropy in the direction , where is the CMB lensing potential defined by (e.g. Lewis & Challinor 2006) (1)Here, χ is conformal distance (with χ_{∗} ≈ 14 000 Mpc denoting the distance to the CMB lastscattering surface). The angulardiameter distance f_{K}(χ) depends on the curvature of the Universe, and is given by (2)Finally, is the (Weyl) gravitational potential at conformal distance χ along the direction at conformal time η (the conformal time today is denoted as η_{0}). The lensing potential is an integrated measure of the mass distribution back to the lastscattering surface. The power spectrum of the lensing potential probes the matter power spectrum, which is sensitive to “latetime” parameters that modify the growth of structure such as neutrino mass (Smith et al. 2009). The amplitude of lensing effects is also a sensitive probe of geometrical parameters, such as the curvature of the Universe.
The lensinduced remapping imprints distinctive statistical signatures onto the observed CMB fluctuations, which can be mined for cosmological information in a process known as lens reconstruction (Okamoto & Hu 2003). The past several years have been seen dramatic improvements in CMB lensing measurements, moving from first detections in crosscorrelation (Smith et al. 2007; Hirata et al. 2008) to cosmologically useful measurements of the lensing potential power spectrum (Das et al. 2011; van Engelen et al. 2012; Planck Collaboration XVII 2014). Recently, groundbased experiments have been able to detect the effects of lensing in polarization data as well (Hanson et al. 2013; POLARBEAR Collaboration 2014a,b; van Engelen et al. 2015).
The results in this paper extend our earlier results in Planck Collaboration XVII (2014). These were based on the March 2013 Planck^{1} nominalmission data release, which contains approximately 15 months of temperature data alone. The additional information in the fullmission dataset, with 30 months of Planck HFI temperature and polarization data (Planck Collaboration VII 2016; Planck Collaboration VIII 2016), allows us to improve our reconstruction noise levels by roughly a factor of two. Approximately half of this improvement comes from lower noise levels in temperature, and the other half from inclusion of polarization data. The improved lensing map is included as part of the Planck 2015 public data release (Planck Collaboration I 2016), as well as an estimate of the lensing potential power spectrum and associated likelihoods. In this paper we describe the creation of these products, as well as first science results based on them. We highlight the following science results.

We detect lensing Bmodes in the Planck data at a significance of 10σ, using both a crosscorrelation approach with the cosmic infrared background (CIB) as a tracer of the lensing potential, as well as a CMBonly approach using the TTEB trispectrum. This provides an important confirmation that Planck is sensitive to this known source of Bmodes on intermediate and small scales.

We make an improved measurement of the 3point function (bispectrum) that is induced in the CMB by the correlation between lensing and the integrated SachsWolfe (ISW) effect, the latter being sourced by latetime acceleration. The lensingISW bispectrum is now detected at the 3σ level.

Using only lensing information (along with wellmotivated priors), we constrain the parameter combination to roughly 3%.
The organization of this paper is as follows. In Sect. 2 we give a summary of our analysis pipeline for the fullmission data. Our analysis is very similar to that presented in Planck Collaboration XVII (2014), with straightforward extensions to polarization data, as well as a few additional improvements, and so we have deferred most technical details to appendices. In Sect. 3 we present our main results, including the lensing potential map, bandpower estimates of the lensing potential power spectrum, and implications for cosmological parameters. In Sect. 4 we present a suite of consistency and null tests to verify our bandpower estimates, and in Sect. 5 we conclude. A series of appendices provide more technical details of our analysis pipeline, modeling of the CIB, and dependence of the lensing power spectrum on ΛCDM parameters.
2. Data and methodology
Here we give a brief overview of the procedure that we use to measure the CMB lensing potential and its power spectrum from the Planck maps. We defer the detailed technical aspects of our pipeline to Appendix A.
Our main results are based on a foregroundcleaned map of the CMB synthesized from the raw Planck 2015 fullmission frequency maps using the SMICA code (Planck Collaboration IX 2016). This foregroundcleaned map combines all nine frequency bands from 30 GHz to 857 GHz with scaledependent coefficients chosen to provide unit response to the CMB with minimal variance; for more details see Planck Collaboration IX (2016)^{2}. On the small scales where lensing effects are most important (multipoles above ℓ = 1000), most of the CMB information in the foregroundcleaned maps originates with the 143 GHz and 217 GHz channel data. These channels have beams that are approximately Gaussian with fullwidthathalfmaximum (FWHM) parameters of 7′ and 5′, respectively. Their noise is approximately white for 1000 ≲ ℓ ≲ 3000, with levels of 30 μK arcmin (60 μK arcmin) in temperature (polarization) at 143 GHz, and 40 μK arcmin (95 μK arcmin) at 217 GHz.
We reconstruct the CMB lensing potential using quadratic estimators that exploit the statistical anisotropy induced by lensing, following Okamoto & Hu (2003). Neglecting the lensing of primordial Bmodes there are five possible estimators, denoted by , , , , and , which are based on various correlations of the CMB temperature (T) and polarization (E and B). In addition, we can form a minimumvariance estimator that combines all five estimators, which we denote as . In Fig. 1 we plot the lens reconstruction noise levels for these estimators. The most powerful estimator is TT, although the TE and EE estimators are also useful on large angular scales.
Fig. 1
Lens reconstruction noise levels for the TT, TE, EE, EB, and TB estimators applied to the SMICA fullmission CMB map. The noise level for their minimumvariance combination (MV) is also shown. The fiducial ΛCDM theory power spectrum used in our Monte Carlo simulations is plotted as the black solid line. 
The quadratic lensing estimators take inversevariance filtered CMB multipoles as input. We obtain these using a filter that masks the Galaxy and point sources, and also bandpass filters the data in harmonic space to 100 ≤ ℓ ≤ 2048. For our baseline analysis, we start with the masks used in the analysis of Planck Collaboration XVII (2014), with a Galaxy mask that removes 30.2% of the sky and a pointsource mask that removes an additional 0.7% of the sky. This removes the brightest SunyaevZel’dovich (SZ) clusters as described in Planck Collaboration XVII (2014); contamination of the lensing reconstruction from residual unresolved SZ is expected to be at the percent level for Planck (van Engelen et al. 2014), even without component separation^{3}. This residual signal, and that from unresolved pointsources, is further reduced by a correction that we make to the power spectrum of the reconstructed lensing potential (see Appendix A.3). Finally, we apply SMICAspecific temperature and polarization masks described in Planck Collaboration IX (2016). Combining all three sets of masks leaves a total of 67.3% of the sky for analysis.
We estimate the power spectrum of the lensing potential using the auto and crossspectra of the quadratic lensing estimators^{4}. These spectra probe the 4point function of the lensed CMB, specifically the connected (trispectrum) part of the 4point function that is sourced by lensing. They also contain contributions from the disconnected part of the 4point function (which is nonzero even in the absence of lensing effects). We estimate this contribution and subtract it, as well as several other smaller bias terms, obtaining an estimate of the power spectrum. This procedure is discussed in Appendix A.3. For cosmological parameter constraints, we use a Gaussian loglikelihood in bandpowers of the estimated lensing power spectrum, given by (3)Here, bins are indexed by i and j; is the bandpower binning function for the ith bin, and Σ is a covariance matrix for the bin estimates. Paired upper/lower indices are summed over. The is the theoretical expectation value of the estimated for the set of cosmological and nuisance parameters under consideration. This generally differs from the theory spectrum at the same cosmological parameters due to the way that our power spectrum estimates are normalized, and corrected for additional trispectrum couplings, with a fiducial model. Both the binning function and are discussed in Appendix C. The binning function is chosen to have unit response to a fiducial theory spectrum , and so we denote (4)as the amplitude of the power spectrum for a particular bin relative to the fiducial expectation (with Â = 1 for ).
To characterize the variance of our lensing potential estimates, as well as to estimate several bias terms, we use simulated Planck maps. These are based on the Full Focal Plane 8 (FFP8) Monte Carlo simulation set described in Planck Collaboration XII (2016). As discussed there, the Planck maps were effectively renormalized by approximately 2–3% in power in the time between the generation of FFP8 and the final Planck fullmission maps. To account for this, we rescale the CMB component of the simulations by a factor of 1.0134 before analysis. The FFP8 simulations do not include contributions from residual foregrounds (Galactic dust, as well as unmasked point sources), and also underestimate the noise power spectra by several percent at highℓ. We account for this missing power simply by adding coloured Gaussian noise to the simulations to make their TT, EE, and BB power spectra agree with the data. This approach implicitly assumes that any nonGaussianity of these residual components does not couple significantly to our lensing estimates. We perform consistency tests in Sect. 4 to check the validity of these assumptions.
Throughout this paper we use a spatiallyflat fiducial cosmology with baryon density given by ω_{b} = Ω_{b}h^{2} = 0.0222, cold dark matter (CDM) density ω_{c} = Ω_{c}h^{2} = 0.1203, neutrino energy density ω_{ν} = Ω_{ν}h^{2} = 0.00064 (corresponding to two massless neutrinos and one massive with mass 0.06 eV), Hubble constant H_{0} = 100h km s^{1} Mpc^{1} with h = 0.6712, spectral index of the power spectrum of the primordial curvature perturbation n_{s} = 0.96, amplitude of the primordial power spectrum (at k = 0.05 Mpc^{1}) A_{s} = 2.09 × 10^{9}, and Thomson optical depth through reionization τ = 0.065. These cosmological parameters also form the basis for the FFP8 Monte Carlo simulation set. In addition to rescaling the FFP8 maps as already discussed, we have also adjusted the power spectra of the fiducial model by rescaling the CMB temperature and polarization spectra by a factor of 1.0134^{2}, and the temperaturelensing crosscorrelation by 1.0134. We have not applied any scaling to the fiducial lensing power spectrum. Our reconstruction methodology (in particular, the renormalization corrections, addition of foreground power, and realizationdependent bias corrections that we apply, discussed in Appendix C) renders the cosmological interpretation of our lensing estimates insensitive to errors in the fiducial model power spectra and simulations.
3. Results
In this section, we provide a summary of the first science results obtained with the minimumvariance lens reconstruction from the Planck fullmission data. The lensing potential map is presented in Sect. 3.1, and this is combined in Sect. 3.2 with the Emode polarization measured by Planck to obtain a map of the expected Bmode polarization due to lensing. We further show that this is correlated with the Bmodes measured by Planck at the expected level. In Sect. 3.3, we crosscorrelate the reconstructed lensing potential with the largeangle temperature anisotropies to measure the correlation sourced by the ISW effect. Finally, the power spectrum of the lensing potential is presented in Sect. 3.4. We use the associated likelihood alone, and in combination with that constructed from the Planck temperature and polarization power spectra (Planck Collaboration XI 2016), to constrain cosmological parameters in Sect. 3.5.
3.1. Lensing potential
In Fig. 2 we plot the Wienerfiltered minimumvariance lensing estimate, given by (5)where is the lensing potential power spectrum in our fiducial model and is the noise power spectrum of the reconstruction. As we shall discuss in Sect. 4.5, the lensing potential estimate is unstable for L< 8, and so we have excluded those modes for all analyses in this paper, as well as in the MV lensing map.
As a visual illustration of the signaltonoise level in the lensing potential estimate, in Fig. 3 we plot a simulation of the MV reconstruction, as well as the input φ realization used. The reconstruction and input are clearly correlated, although the reconstruction has considerable additional power due to noise. As can be seen in Fig. 1, even the MV reconstruction only has S/N ≈ 1 for a few modes around L ≈ 50.
Fig. 2
Lensing potential estimated from the SMICA fullmission CMB maps using the MV estimator. The power spectrum of this map forms the basis of our lensing likelihood. The estimate has been Wiener filtered following Eq. (5), and bandlimited to 8 ≤ L ≤ 2048. 
Fig. 3
Simulation of a Wienerfiltered MV lensing reconstruction (upper) and the input φ realization (lower), filtered in the same way as the MV lensing estimate. The reconstruction and input are clearly correlated, although the reconstruction has considerable additional power due to noise. 
The MV lensing estimate in Fig. 2 forms the basis for a public lensing map that we provide to the community (Planck Collaboration I 2016). The raw lensing potential estimate has a very red power spectrum, with most of its power on large angular scales. This can cause leakage issues when cutting the map (for example to crosscorrelate with an additional mass tracer over a small portion of the sky). The lensing convergence κ defined by (6)has a much whiter power spectrum, particularly on large angular scales. The reconstruction noise on κ is approximately white as well (Bucher et al. 2012). For this reason, we provide a map of the estimated lensing convergence κ rather than the lensing potential φ.
3.2. Lensing Bmode power spectrum
The oddparity Bmode component of the CMB polarization is of great importance for earlyuniverse cosmology. At first order in perturbation theory it is not sourced by the scalar fluctuations that dominate the temperature and polarization anisotropies, and so the observation of primordial Bmodes can be used as a uniquely powerful probe of tensor (gravitational wave) or vector perturbations in the early Universe. A detection of Bmode fluctuations on degree angular scales, where the signal from gravitational waves is expected to peak, has recently been reported at 150 GHz by the BICEP2 collaboration (Ade et al. 2014). Following the joint analysis of BICEP2 and Keck Array data (also at 150 GHz) and the Planck polarization data, primarily at 353 GHz (BICEP2/Keck Array and Planck Collaborations 2015), it is now understood that the Bmode signal detected by BICEP2 is dominated by Galactic dust emission. The joint analysis gives no statisticallysignificant evidence for primordial gravitational waves, and establishes a 95% upper limit r_{0.05}< 0.12. This still represents an important milestone for Bmode measurements, since the direct constraint from the Bmode power spectrum is now as constraining as indirect, and modeldependent, constraints from the TT spectrum (Planck Collaboration XIII 2016).
In addition to primordial sources, the effect of gravitational lensing also generates Bmode polarization. The displacement of lensing mixes Emode polarization into Bmode as (Smith et al. 2009) (7)where is a weight function and the bracketed term is a Wigner3j symbol. On scales ℓ_{B} ≲ 1000 the lensing Bmode power spectrum resembles that of white noise, with a level of about 5 μK arcmin. This lensing power acts as a potential source of confusion for the measurement of primordial Bmodes, which can be estimated and ultimately removed in a process of delensing. Given an estimate for the lensing potential φ and the Emode polarization measured by Planck we can synthesize a lensed Bmode map for this purpose using Eq. (7). The 5 μK arcmin level of the lensing Bmode power spectrum is an order of magnitude lower than the Planck 2015 noise levels, and so delensing does not significantly improve our Bmode measurements; however, the crosscorrelation of the lensing Bmode template with the observed Bmode sky provides a useful check on the ability of Planck to measure this known source of Bmodes.
We show the results of such a crosscorrelation in Fig. 4, finding good agreement with the expected lensing Bmode power spectrum. In addition to our fiducial MV lensing potential estimate, which uses both temperature and polarization data, we have also estimated the lensing Bmode power spectrum using the TTonly lensing estimator to measure φ, as well as the CIB fluctuations measured by the 545 GHzPlanck channel^{5}. We see good agreement in all cases with the expected power; constraining the overall amplitude of the lensing Bmode power spectrum Â^{B} (relative to the predicted spectrum in our fiducial model) for a large bin from 8 ≤ ℓ_{B} ≤ 2048 we measure amplitudes of for the three estimates, each corresponding to a roughly 10σ detection of lensing Bmode power in the Planck data. The shape of the crosscorrelation is also in good agreement with expectation. Taking the bins in Fig. 4 as independent, forming a χ^{2} relative to the theory model and comparing to the distribution from simulations we obtain probabilitytoexceed (PTE) values of 48%, 71%, and 78% using the MV, TT, and CIB lensing estimates, respectively.
Fig. 4
Measurement of the lensing Bmode power spectrum, using crosscorrelation with estimates of the lensing potential as discussed in Sect. 3.2. The theoretical lensing Bmode power spectrum, for the parameters of the fiducial cosmological model of Sect. 2, is plotted as dashed black. Green squares and blue triangles are results using the TT and MV φ reconstructions, respectively, to construct the lensinduced Bmode template, while red circles use the CIB (from the Planck545 GHz channel) to construct a proxy for φ. Lensing Bmode power is detected with the expected scale dependence and amplitude at a significance level of approximately 10σ. 
3.3. LensingISW bispectrum
As photons travel towards us from the last scattering surface, they are not only deflected by gravitational lensing, they also receive net red/blueshifts from gravitational potentials that evolve if they are crossed at late times. This phenomenon, known as the integrated SachsWolfe (ISW) effect, is believed to generate anisotropies in the observed CMB temperature on large (ℓ ≲ 100) angular scales. It is of particular interest because the decay of gravitational potentials, which produces the ISW effect, does not occur during matter domination, but only at redshifts z ≲ 2 when dark energy becomes dynamically important. The ISW effect can be detected statistically by crosscorrelating the observed temperature anisotropies with a tracer of structure at these redshifts. Here we use the lensing potential, which is wellmatched to the ISW effect (Hu 2002). Current Planck results incorporating additional external tracers of largescale structure are summarized in Planck Collaboration XXI (2016).
Fig. 5
LensingISW bispectrum on large angular scales. The crossspectrum between the MV lensing potential estimate and the temperature anisotropy is plotted for bins of width ΔL = 15, covering the multipole range L = 8–98. The dashed line shows the predicted crossspectrum in the fiducial model. The lensingISW bispectrum is detected at just over 3σ significance. 
In Fig. 5 we plot the crosscorrelation between the MV lens reconstruction and the CMB temperature. This crosscorrelation probes the bispectrum, or threepoint correlation function of the CMB, which is due to the correlation of the lensing and ISW effects. The measurement is noisy, due to a combination of noise in the lens reconstruction and cosmic variance in the temperature (which ultimately limits the detection of the lensingISW bispectrum to about 9σ; Lewis et al. 2011).
To determine the overall detection significance for the crosscorrelation, we use the minimumvariance bispectrum estimator (8)where N^{Tφ} is a normalization determined from simulations^{6}. For the MV lens reconstruction, using 8 ≤ L ≤ 100 we measure an amplitude (9)which is consistent with the theoretical expectation of unity and nonzero at just over 3σ. Using the TTonly lensing estimate rather than the MV lensing estimate in the crosscorrelation, we obtain (10)Using simulations, we measure an rms difference between the TT and MV bispectrum amplitudes of 0.18 (roughly equal to the quadrature difference of their error bars, which is ). Therefore the difference of amplitudes, , is compatible with the expected scatter.
In Planck Collaboration XVII (2014), using the TT lensing estimator on the multipole range 10 ≤ L ≤ 100 we measured a somewhat higher value for the lensingISW bispectrum amplitude of ^{7}. We expect the difference with respect to the 2015 TT measurement to have standard deviation of approximately , and so the observed difference of 0.85−0.68 = 0.17 is reasonable.
3.4. Lensing potential power spectrum
In Fig. 6 we plot our estimate of the lensing potential power spectrum obtained from the MV reconstruction, as well as several earlier measurements. We see good agreement with the shape in the fiducial model, as well as earlier measurements (detailed comparisons with the 2013 spectrum are given later in this section). In Sect. 4 we perform a suite of internal consistency and null tests to check the robustness of our lensing spectrum to different analysis and data choices.
We estimate the lensing potential power spectrum in bandpowers for two sets of bins: a “conservative” set of eight uniformlyspaced bins with ΔL = 45 in the range 40 ≤ L ≤ 400; and an “aggressive” set of 18 bins that are uniformly spaced in L^{0.6} over the multipole range 8 ≤ L ≤ 2048. The conservative bins cover a multipole range where the estimator signaltonoise is greatest. They were used for the Planck 2013 lensing likelihood described in Planck Collaboration XVII (2014). The aggressive bins provide good sensitivity to the shape of the lensing power on both large and small scales, however they are more easily biased by errors in the meanfield corrections (which are large at L< 40) and the disconnected noise bias corrections (at L> 400). Results for the bandpower amplitudes , defined in Eq. (4), are given in Table 1 for both sets of multipole bins.
Nearly all of the internal consistency tests that we present in Sect. 4 are passed at an acceptable level. There is, however, mild evidence for a correlated feature in the curlmode nulltest, centred around L ≈ 500. The L range covered by this feature includes 638 ≤ L ≤ 762, for which the (gradient) lensing reconstruction bandpower is 3.6σ low compared to the predicted power of the fiducial model. In tests of the sensitivity of parameter constraints to lensing multipole range, described later in Sect. 3.5.4, we find shifts in some parameters of around 1σ in going from the conservative to aggressive range, with negligible improvement in the parameter uncertainty. Around half of these shifts come from the outlier noted above. For this reason, we adopt the conservative multipole range as our baseline here, and in other Planck 2015 papers, when quoting constraints on cosmological parameters. However, where we quote constraints on amplitude parameters in this paper, we generally give these for both the aggressive and conservative binning. The aggressive bins are also used for all of the bandpower plots in this paper.
Fig. 6
Planck 2015 fullmission MV lensing potential power spectrum measurement, as well as earlier measurements using the Planck 2013 nominalmission temperature data (Planck Collaboration XVII 2014), the South Pole Telescope (SPT, van Engelen et al. 2012), and the Atacama Cosmology Telescope (ACT, Das et al. 2014). The fiducial ΛCDM theory power spectrum based on the parameters given in Sect. 2 is plotted as the black solid line. 
Estimating an overall lensing amplitude (following Eq. 4) relative to our fiducial theoretical model, for a single bin over both the aggressive and conservative multipole ranges we find These measurements of the amplitude of the lensing power spectrum both have precision of 2.5%, and are nonzero at 40σ. Given the measured amplitude, it is clear that our overall lensing amplitude estimate is consistent with our fiducial ΛCDM model (which has A = 1). The shape of our measurement is also in reasonable agreement with the fiducial model. Marginalizing over an overall amplitude parameter, for the 19 bins plotted in Fig. 6 we obtain a χ^{2} with respect to our fiducial model of 28 (with N_{d.o.f.} = 18), with a corresponding PTE of 6%. A large portion of this χ^{2} is driven by the outlier bandpower with 638 ≤ L ≤ 762. Removing this bandpower gives a χ^{2} of 16.5 (N_{d.o.f.} = 17), with a PTE of 49%. For the more conservative multipole range 40 ≤ L ≤ 400, using eight linear bins we obtain χ^{2} of 8.9 (N_{d.o.f.} = 7), with a PTE of 26%.
Finally, we note that the lensing bandpowers measured here are in good agreement with the Planck 2013 results (Planck Collaboration XVII 2014). The only clear visual difference between the two measurements in Fig. 6 is for the L = 40–65 bin, where the 2013 bandpower is significantly higher than the current measurement. As previously discussed in Planck Collaboration XVII (2014), this bin amplitude was sensitive to the choice of foreground cleaning, and decreased when using a componentseparated map for the reconstruction (as is done here), and so this difference is expected. To compare measurements in further detail, we calculate a χ^{2} for the difference between our 2013 and 2015 MV lensing bandpower estimates assuming a diagonal covariance as (12)where L_{b} indexes the bandpower bins. We approximate the variance of the differences of the bandpowers as the difference of the variances in forming this χ^{2}. Using the conservative bins for 40 ≤ L ≤ 400, which were used for the 2013 lensing likelihood, we obtain a value of (N_{d.o.f.} = 8), with a corresponding PTE of 22%. The 2013 result is temperature only; if we compare it directly to the 2015 TT lensing bandpowers, we obtain a value of χ^{2} = 7.2 (N_{d.o.f.} = 8, PTE = 52%). We note that some care should be taken when comparing the measured lensing amplitudes between 2013 and 2015, because the fiducial spectra against which they are measured differ significantly. In 2013, for example, we measured . However, this was measured with respect to a fiducial that is between 6% and 7% higher than the one used here in this multipole range. Renormalizing the 2013 measurements to determine the amplitude for the 2015 fiducial cosmology, we obtain 1.005 ± 0.043, which can be compared directly to the measurement of for 2015. It is interesting to note that there is a 2% difference in power between the Planck 2013 and 2015 maps from HFI due to a change in the calibration, as well as an improved beam model (Planck Collaboration VII 2016). This shift does not couple to the lensing reconstruction; lensing is a geometric effect, and so an overall change in the calibration does not affect the lens reconstruction procedure, provided that a consistent power spectrum is used to analyse the maps, as is done here.
Lensing potential power spectrum estimates from the MV lens reconstruction.
3.5. Likelihoods and cosmological parameter constraints
In this section we discuss cosmological implications of the Planck lensing potential power spectrum estimate. We use a Gaussian likelihood in the measured bandpowers, with corrections to account for errors in the fiducial CMB power spectra that are used to normalize and debias the lensing estimates. This procedure is described in more detail in Appendix C.
In the following subsections, we discuss the information that can be gleaned from the lensing potential power spectrum alone, or in conjunction with the Planck 2015 CMB power spectrum likelihood. Some of the results in this section summarize the parts of Planck Collaboration XIII (2016) that are directly related to lensing.
3.5.1. Constraints from CMB lensing alone
Fig. 7
Parameter constraints from CMB lensing alone in the ΛCDM model (samples, colourcoded by the value of the Hubble constant) using the minimal priors described in the text; grey bands give the corresponding 1σ and 2σ constraints using the approximate fit of Eq. (13). Solid coloured contours show 68% and 95% constraints when additional information is included: BAO data from SDSS and 6DF (Anderson et al. 2014; Ross et al. 2015; Beutler et al. 2011; blue), and the same but fixing the CMB acousticscale parameter θ_{MC} to a CMB power spectrum fit (red; θ_{MC} = 1.0408). Solid black contours show the constraint from the Planck CMB power spectra (Planck temperature+lowℓ polarization). 
The measurement of the lensing power spectrum is obtained from the 4point function of the observed CMB anisotropies, and hence depends in general on both the lensing and CMB power spectra (see Appendix C). However the latter are now well measured by Planck, and there is only a weak dependence on cosmological parameters given the observed spectra (mainly via different cosmological models changing foreground parameters, and hence the amplitude of underlying CMB power spectra). In this section we fix the CMB power spectra to a ΛCDM Planck TT+lowP best fit (i.e. using TT on all scales and lowℓ polarization), and consider the conditional probability of different cosmological parameters given only the lensing reconstruction power spectrum.
Fig. 8
Lensing potential power spectra in the base ΛCDM model drawn from the posterior of the lensingonly likelihood (left) and the Planck TT+lowP likelihood (right), coloured by the value of the dark matter density Ω_{c}h^{2}. For fixed CMB power spectra (i.e. the righthand plot) the lensing power spectrum shape is nearly fixed, but the amplitude increases as the matter density increases across the acousticscale degeneracy (the accompanying reduction in A_{s} is subdominant). With lensing reconstruction data, as Ω_{c}h^{2} varies the lensing spectrum changes shape, with the amplitude around the bestmeasured L ≈ 150, remaining pinned by the accuracy of the measurement there. A combination of the lensing and Planck TT+lowP data measures the matter density significantly better, with the higher values in the righthand plot being excluded. Note that the colour scales on the plots are different. 
To avoid marginalizing over very unrealistic values of poorly constrained parameters, we adopt several wellmotivated priors when considering constraints from lensing alone.

The optical depth to reionization is fixed to τ = 0.07, because lensing deflections are independent of reionization (and scattering and subsequent lensing from sources at reionization is negligible).

The baryon density is given a Gaussian 1σ prior Ω_{b}h^{2} = 0.0223 ± 0.0009, as measured independently from big bang nucleosynthesis models combined with quasar absorption line observations (Pettini & Cooke 2012).

The scalar spectral index is given a broad prior n_{s} = 0.96 ± 0.02; results are only weakly sensitive to this choice, within plausible bounds.

A tophat prior is used for the reduced Hubble constant, 0.4 <h< 1. This limits the extent of the parameter degeneracy, but does not affect the results over the region of interest for joint constraints.
In addition to the priors above, we adopt the same sampling priors and methodology as Planck Collaboration XIII (2016)^{8}, using CosmoMC and camb for sampling and theoretical predictions (Lewis & Bridle 2002; Lewis et al. 2000). In the ΛCDM model, as well as Ω_{b}h^{2} and n_{s}, we sample A_{s}, Ω_{c}h^{2}, and the (approximate) acousticscale parameter θ_{MC}. Alternatively, we can think of our lensingonly results as constraining the subspace of Ω_{m}, H_{0}, and σ_{8}. Figure 7 shows the corresponding constraints from CMB lensing, along with tighter constraints from combining with additional external baryon acoustic oscillation (BAO) data, compared to the constraints from the Planck CMB power spectra. The contours overlap in a region of acceptable Hubble constant values, and hence are compatible. To show the multidimensional overlap region more clearly, the red contours show the lensing constraint when restricted to a reduceddimensionality space with θ_{MC} fixed to the value accurately measured by the CMB power spectra; the intersection of the red and black contours gives a clearer visual indication of the consistency region in the Ω_{m}–σ_{8} plane.
The lensingonly constraint defines a band in the Ω_{m}–σ_{8} plane, with the wellconstrained direction corresponding approximately to the constraint (13)This parameter combination is measured with approximately 3.5% precision.
The dependence of the lensing potential power spectrum on the parameters of the ΛCDM model is discussed in detail in Appendix E; see also Pan et al. (2014). Here, we aim to use simple physical arguments to understand the parameter degeneracies of the lensingonly constraints. In the flat ΛCDM model, the bulk of the lensing signal comes from high redshift (z> 0.5) where the Universe is mostly matterdominated (so potentials are nearly constant), and from lenses that are still nearly linear. For fixed CMB (monopole) temperature, baryon density, and n_{s}, in the ΛCDM model the broad shape of the matter power spectrum is determined mostly by one parameter, k_{eq} ≡ a_{eq}H_{eq} ∝ Ω_{m}h^{2}. The matter power spectrum also scales with the primordial amplitude A_{s}; keeping A_{s} fixed, but increasing k_{eq}, means that the entire spectrum shifts sideways so that lenses of the same typical potential depth Ψ_{lens} become smaller. Theoretical ΛCDM models that keep ℓ_{eq} ≡ k_{eq}χ_{∗} fixed will therefore have the same number (proportional to k_{eq}χ_{∗}) of lenses of each depth along the line of sight, and distant lenses of the same depth will also maintain the same angular correlation on the sky, so that the shape of the spectrum remains roughly constant. There is therefore a shape and amplitude degeneracy where ℓ_{eq} ≈ constant, A_{s} ≈ constant, up to corrections from subdominant changes in the detailed lensing geometry, changes from latetime potential decay once dark energy becomes important, and nonlinear effects. In terms of standard ΛCDM parameters around the bestfit model, , with the powerlaw dependence on Ω_{m} only varying slowly with Ω_{m}; the constraint defines the main dependence of H_{0} on Ω_{m} seen in Fig. 7.
The argument above for the parameter dependence of the lensing power spectrum ignores the effect of baryon suppression on the smallscale amplitude of the matter power spectrum (e.g. Eisenstein & Hu 1998). As discussed in Appendix E, this introduces an explicit dependence of the lensing power spectrum on Ω_{m}h^{2}. However, since the parameter dependence of ℓ_{eq} is close to (Ω_{m}h^{2})^{1 / 2}, we can still think of A_{s} and ℓ_{eq} as giving the dominant dependence of on parameters.
Fig. 9
Constraints on the rms lensing deflection angle ⟨ d^{2} ⟩ ^{1 / 2} from Planck TT+lowP CMB power spectra in the base ΛCDM model (samples, colour coded by the value of the Hubble constant), showing that the inferred lens deflection amplitude is strongly correlated with the parameter combination . The solid black contour is the constraint from CMB lensing only, which prefers lower values of the lensing amplitude, giving the joint constraint shown by the red contours. Since the lensing reconstruction mainly constrains scales smaller than the peak of the deflection angle power spectrum, there is an additional degeneracy that makes ⟨ d^{2} ⟩ ^{1 / 2} slightly anticorrelated with the measurement of lensing power on smaller scales, which is approximately proportional to (the square of) 
In practice the shape of is not measured perfectly, and there is also some degeneracy between A_{s}, which directly increases the amplitude, and ℓ_{eq}, which increases the amplitude via an increase in the number of lenses along the line of sight and indirectly by changing the depth of potential wells of a given size. On small angular scales with L>ℓ_{eq}, Ψ_{lens} becomes a strong function of scale due to the falloff in the matter power spectrum for k>k_{eq}. For a fixed comoving scale, the amplitude increases if k_{eq} becomes larger since this reduces the amount of decay of the gravitational potential during radiation domination (Pan et al. 2014), and also lessens the impact of baryon suppression. This increases the dependence of on ℓ_{eq} beyond the linear scaling from the number of lenses, and on small scales , where n_{L}> 0 determines the actual Ldependence (inherited from the scale dependence of the smallscale matter power spectrum and the effect of baryon suppression). For Planck, the lensing spectrum is best measured at L ~ 200: since n_{L} increases with L, making A_{s} larger and smaller to keep the power constant at L ~ 200 leads to an increase in power at lower L and a decrease in power at higher L (see Fig. 8). The lensing amplitude at the peak L ≈ 50 (and hence the rms deflection angle), is therefore slightly anticorrelated with the amount of smallscale power (as shown by the lensing only constraint in Fig. 9; see discussion below).
The actual threeparameter constraint is close to (14)which, in terms of A_{s}, ℓ_{eq}, and h is independent of h, as expected, given the parameter dependence of discussed above. Recall that the fairly high powerlaw dependence with ℓ_{eq} comes from the rapid falloff in the matter power spectrum with scale (and from the dependence on Ω_{m}h^{2} of baryon suppression effects). Approximately the same scaling will also affect other measures of smallscale power, for example σ_{8}. From Eq. (E.20), for parameter values of interest σ_{8} and A_{s} are related approximately by (15)The parameter direction is therefore close to the direction in which A_{s} (and hence ) is constant for fixed ℓ_{eq}. The combination is also not that far from of Eq. (14), which remains nearly constant as ℓ_{eq} and A_{s} vary within their degeneracy. The degeneracy direction shown in Fig. 7 and the constraint of Eq. (13)are therefore significantly tighter than the corresponding constraint in the A_{s}–Ω_{m} plane, because σ_{8} absorbs some of the degeneracy between matter density and primordial power at a fixed scale.
In terms of σ_{8}, a welldetermined threeparameter combination is^{9}(16)at 2.3% precision. In terms of power, the 4.6% precision of the square of this parameter combination is still weaker than the template amplitude measurement of Eq. (11a). This is expected, given the approximation of a powerlaw fit, and that in Eq. (16)we have also marginalized over variations in n_{s} and Ω_{b}h^{2}. Marginalizing out the Ω_{m}h^{2} dependence in Eq. (16), the constraint on of Eq. (13)is further degraded to 3.5%.
We can also quantify the amplitude of lensing by the rms deflection angle ⟨ d^{2} ⟩ ^{1 / 2}, which we define via (17)This is approximately an integral over all scales. It is proportional to the primordial fluctuation power A_{s}, and scales roughly as a power law in ℓ_{eq}. A simple argument based on the number of lenses along the line of sight would suggest ⟨ d^{2} ⟩ ∝ A_{s}ℓ_{eq}. This is almost correct, in particular the strong dependence of on ℓ_{eq} integrates out when forming ⟨ d^{2} ⟩ (see Eq. (E.4)). However, there is a further dependence on Ω_{m} and h due to the effects of baryon suppression and the latetime decay of the gravitational potential, which is important at lowL; we find approximately (18)by taking derivatives of computed with CAMB. The rms is measured at 2.4% precision to be (19)So far we have only considered a ΛCDM model. However, for most generalizations that are close to ΛCDM, the same scaling also approximately holds, and the constraint direction of is only weakly modeldependent, although the centroid can shift and also the relation between Ω_{m} and h. For example, for a model with massless sterile neutrinos parameterized by N_{eff}, the constraint of Eq. (13)is virtually identical, but parameter samples shift towards systematically higher Ω_{m}h^{2} than in ΛCDM to keep the equality scale roughly constant when N_{eff}> 3.046. As a further example, in models with three massive (active) neutrinos with degenerate masses, marginalizing over the mass of massive neutrinos shifts the constraint slightly down, giving (20)Note that in the neutrino case nonlinear corrections from halofit are unreliable away from Ω_{m} ≈ 0.3, which can affect this result at around the 0.01 level.
3.5.2. Joint parameter constraints
The smallscale CMB power spectra are only directly sensitive to the primordial power A_{s} after damping by reionization, through the combination A_{s}e^{− 2τ}. However lensing also smooths the power spectra at the several percent level, which allows the amplitude A_{s} to be constrained via the amplitude of the smoothing effect (and a small transfer of power to small scales), even when τ is not constrained precisely^{10}. The lensing smoothing can be thought of as approximately a convolution of the deflection angle power spectrum with the CMB power spectrum, and hence is mostly sensitive to scales around the peak of the deflection angle power spectrum. We can quantify the amplitude of the smoothing approximately by the meansquared deflection angle ⟨ d^{2} ⟩ from Eq. (17).
The CMB power spectra determine the angular acoustic scale θ_{∗} very accurately, which can be used to eliminate the hdependence from ⟨ d^{2} ⟩, considered as a function σ_{8}, h, and Ω_{m}. Empirically, we find that in the ΛCDM model the CMB power spectrum posterior has , close to the combination measured more directly by lensing reconstruction^{11}; see Fig. 9. For a flat ΛCDM model the constraint on the latter parameter is (21)The amplitude information here is coming from a combination of the lensing smoothing effect, and information from the CMB power spectrum amplitude combined with the constraint on τ from lowℓ polarization. The lensingonly constraint of Eq. (15)is slightly weaker, but somewhat lower than this (consistent at just over 1σ); see Fig. 7. The combined constraint is tighter and restricted to slightly lower values compared to the Planck power spectrum measurement: (22)Here, for joint constraints we no longer apply the additional priors used in the lensingonly analysis, and allow for the full linearized CMB power spectrum dependence of the lensing renormalization in the likelihood. The overlap region in the Ω_{m}–σ_{8} plane also excludes some of the lower Hubble constant values allowed by the power spectrum data, giving a slight shift of the mean in the direction of the higher values preferred by some local measurements (Riess et al. 2011; Humphreys et al. 2013; Efstathiou 2014), as shown in Fig. 9. This could be an additional indication that the lower Hubble constant values from the CMB spectra are at least partly a random statistical fluctuation, in which case joint constraints should be usefully closer to the truth. The joint constraint gives (23)about 0.5σ higher than Planck TT+lowP alone, and in good agreement with the constraint from Planck TT+lowP+ BAO data.
The measurement of the fluctuation amplitude from lensing, combined with the amplitude of the CMB power spectrum on intermediate and small scales, can also be used to constrain the reionization optical depth independently of lowℓ polarization. The CMB power spectra themselves provide a weak constraint via the lensing smoothing, which is significantly improved by using the additional information in the lensing reconstruction. Figure 10 shows the constraint when Planck CMB temperature power spectrum constraints, without lowℓ polarization, are combined with the lensing likelihood. We find (24)corresponding to a detection of a nonzero optical depth at more than 2σ, and a corresponding reionization redshift (68%). These results are consistent with the baseline Planck power spectrum constraints including lowℓ polarization, which give τ = 0.078 ± 0.019 (Planck TT+lowP; 68%). Both results indicate a consistent downward shift in mean compared to the central value from WMAP (τ = 0.089 ± 0.014; Hinshaw et al. 2013), in agreement with lower predicted values based on recent results for the integrated luminosity function (e.g. Finkelstein et al. 2015). There is a corresponding downward shift in σ_{8}, with joint constraint (25)For further discussion see Planck Collaboration XIII (2016).
Fig. 10
Constraints on the reionization (midpoint) redshift z_{re} and σ_{8} from a combination of Planck CMB power spectra and the lensing reconstruction, excluding lowℓ polarization information. The grey band denotes the approximate z_{re}< 6.5 region excluded by observations of the spectra of highredshift quasars (Fan et al. 2006). The lensing data significantly shrink the allowed region of parameter space, preferring lower values of the reionization redshift, in good agreement with the measurement from Planck lowℓ polarization. 
3.5.3. Constraints on extensions to the ΛCDM model
Fig. 11
Constraints on the curvature and rms lensing deflection angle from Planck TT+lowP CMB power spectra (samples, colour coded by the value of the Hubble constant), showing that the closeduniverse models favoured at the 2σ level have high lensing power that is inconsistent with the lensing reconstruction result. The combined lensing result (black contours) is much tighter, and provides additional information on top of the constraint when adding BAO data (red contours); both joint constraints, and the combined filled contours, are consistent with a flat universe. 
The CMB power spectra only weakly constrain extended models that provide multiple ways to combine latetime parameters to give the same observed angular diameter distance to recombination. The main Planck CMB power spectrum constraints on these parameters are driven largely by the impact of lensing smoothing on the acoustic peaks, which weakly breaks the geometric degeneracy. As discussed in detail in Planck Collaboration XIII (2016), the power spectrum results appear to prefer larger lensing smoothing than in ΛCDM (in this release, slightly above the 2σ level). It is therefore particularly useful to study joint constraints with the lensing likelihood, which constrains the lensing amplitude more directly and more tightly. Since the lensingreconstruction power spectrum has an amplitude that peaks slightly lower than the ΛCDM predictions from the CMB power spectra, the joint constraint eliminates a large region of parameter space with positive curvature allowed by the power spectrum results. For example, Fig. 11 shows the constraints on the curvature parameter Ω_{K} in nonflat models; the high lensing amplitudes, as show in the figure by the rms deflection angle, are ruled out by the lensing reconstruction, and the joint constraint is consistent with a flat universe (Ω_{K} = 0). The improvement in error bars is dramatic, with the CMB power spectra giving (26)and the joint constraint (27)This measurement of curvature from the CMB alone has subpercent precision. External data can also break the geometric degeneracy; the joint BAO contours are shown in Fig. 11 and are consistent. Since the BAO results are independent of fluctuation amplitude, the lensingreconstruction joint constraint adds significant additional information, shrinking the allowed parameter space compared to Planck+BAO alone.
Fig. 12
Summary of lensing power spectrum consistency tests. Upper panel: comparison of the lensing power spectra for the baseline MV reconstruction and several alternative estimators with different data/analysis choices discussed in the text. Lower panels: detailed plots of the difference between each alternative reconstruction and the MV power spectrum, in units of the MV error bars (σ). The ± 1σ band is indicated in grey. Error bars on individual data points give the standard deviation of the expected difference from the MV reconstruction for each case, estimated using Monte Carlo simulations. 
In addition to curvature, CMB results for dark energy models also suffer a geometric degeneracy, and lensing results can similarly provide additional information; for applications to a variety of dark energy models and modified gravity see Planck Collaboration XIV (2016). Lensing can also improve the limits on many other parameters, though in a less dramatic fashion if the CMB power spectra are themselves able to place a good constraint. Full results for many combinations are available online in the grid tables and parameter chains^{12}.
3.5.4. Sensitivity to Lrange
The results in the previous subsections were obtained using our conservative baseline likelihood, with eight bins in the multipole range 40 ≤ L ≤ 400. We now briefly discuss how joint constraints from the Planck power spectra and lensing change for different cuts of the lensing data.
Using the first nine bins of the aggressive binning described in Table 1, covering 8 ≤ L ≤ 425, results from Planck TT+lowP+lensing are consistent at the 0.1σ level with the conservative likelihood, in both ΛCDM and ΛCDM+ ∑ m_{ν} cosmologies. Extending the range to higher L, the Planck TT+lensing constraint on the optical depth is stable, however the measured amplitude shifts downwards by about 1σ with a negligible change in the uncertainty. About half of this comes from including the outlying bin at 638 ≤ L ≤ 762. In the ΛCDM+∑ m_{ν} model, this preference for lower amplitudes on smaller scales pulls joint neutrino mass constraints up by about 1σ. The baseline result is (28)but using the full Lrange the posterior peaks slightly (though not significantly) away from zero: (29)As discussed in Sect. 4, the multipole range 300 ≲ L ≲ 900 may be unreliable, specifically due to the failure of the TTTT curl test. A priority for future analysis should be to develop a trustworthy likelihood over the full multipole range.
4. Consistency and null tests
With multiple frequency bands and the combination of both temperature and polarization data, the Planck fullmission data set provides many opportunities to test internally the lens reconstruction. In this section we present consistency and null tests for the MV lensing potential presented in Sect. 3.
For consistency tests, we perform lens reconstruction using different data/analysis choices than those used for our fiducial MV reconstruction, obtaining an alternative measurement of the lensing potential power spectrum , which we then compare to the baseline MV reconstruction. To compare the test and MV reconstructions more quantitatively, we use a χ^{2} statistic for the difference between the bandpowers, which is calculated as (30)where Σ_{test} is a diagonal covariance matrix for the difference obtained from simulations of the two lens reconstructions. We compare this χ^{2} to a set of values determined from simulations, to estimate a PTE. We do this for several possible choices of binning: a single bin for either the conservative (40 ≤ L ≤ 400) likelihood range, or the lowL and highL ranges (8 ≤ L< 40 and 400 <L ≤ 2048, respectively) that are additionally included in the aggressive likelihood; and the individual bins that span the conservative and highL multipole ranges. The singlebin tests check for broadband differences between the two reconstructions, while the multiplebin tests provide a more sensitive check on shape differences or inaccuracies in the error bars for the two reconstructions.
We group the consistency tests as follows, based on the issues which they are designed to probe.

“Individual estimator crosses” that test for consistency amongthe five quadratic estimators used to measure φ.

“Foregrounds tests” that use singlefrequency maps or different componentseparation methods to test for possible foreground contamination.

“Noise tests” that check that our power spectrum estimates are not biased by instrumental noise, using crossestimators designed to avoid noise biases.

“Analysis choices” that test our sensitivity to choices such as the unmasked sky fraction and multipole range.

“Biashardened estimators’ in which we use lensing estimators that should be less sensitive to certain systematic and instrumental effects.
In Fig. 12 we plot several of the most important consistency tests, as well as their difference from the MV reconstruction. The χ^{2} values associated with the full set of consistency tests are presented in Table 2. The tests themselves are described in more detail in Sects. 4.1–4.5.
Amplitude fits for a suite of alternative reconstructions of the lensing power spectrum and comparison to the baseline MV lensing power spectrum estimate.
Null tests for a set of lens reconstructions based on maps that should contain no CMB signal, or estimators that should be orthogonal to the lensing signal.
Fig. 13
Power spectrum null tests discussed in Sect. 4.6. Curl null tests are denoted by ψ. The individual null test power spectra have been scaled by the factors indicated in each panel for visual purposes. The fiducial (gradientmode) lensing power spectrum is shown as the dashed line in each panel. 
Fig. 14
Grid of lensing potential power spectra for the 15 possible auto and crossspectra of the five quadratic lensing estimators obtained from the SMICA foregroundcleaned maps. The measurements are arranged in order of their total S/N for a measurement of the lensing amplitude. The MV power spectrum estimate, which combines the individual crossspectra and is the basis for our fiducial lensing likelihood, is plotted in the topleft panel. 
In addition to consistency tests, we also perform several null tests in which we apply our lens reconstruction and power spectrum estimation procedure to maps that have been differenced to remove sky signal, or use curlmode lensing estimators that should have zero contribution from the gradient lensing potential. We check that the overall amplitude (for a template given by the fiducial theory spectrum ) is consistent with zero, and also evaluate a χ^{2} computed as (31)where Σ_{null} is a diagonal covariance matrix measured from simulations of the particular null test. A PTE for each null test is computed by comparison to simulations in the same way as for the consistency tests. The null power spectra are plotted in Fig. 13, and summary statistics are given in Table 3. The null tests are discussed in more detail in Sect. 4.6.
4.1. Individual estimator crosses
With the foregroundcleaned temperature and polarization maps from SMICA we can form five distinct lensing estimators, and 15 distinct auto and crossspectra. We plot all of these spectra individually in Fig. 14. The lensing amplitudes, as well as PTEs for the differences from the MV reconstruction are given in Table 2. There is a wide variation in the sensitivity of the different estimators, although we see high (greater than 5σ significance) detections of lensing in nine of the spectra^{13}.
As a further test, we combine several of the spectra with the highest S/N that only use estimator crosses (TT × EE, TT × TE, TT × EB, and EE × TE) into a single “crosses” estimator. This combination of crossspectra has an orderofmagnitude smaller disconnected noise bias correction than the autospectrum of the fiducial MV estimator, with , compared to for the baseline MV lensing reconstruction. The results of this test are presented in Fig. 12 and Table 2. With the crosses power spectrum estimates we are able to test that the lensing amplitude is not biased by errors in the disconnected noise bias correction at around the 2% level.
We also form a combined power spectrum estimate using only polarization data (the EE and EB estimators). This “Pol. only” estimator gives an amplitude estimate for the conservative multipole range of Â = 0.76 ± 0.15, a 5σ detection of lensing at a level consistent with the fiducial MV reconstruction.
4.2. Foreground tests
The SMICA CMB map that is used for our baseline MV reconstruction combines multiple frequency bands as a function of angular scale to reject foregrounds and to produce a single highfidelity estimate of the CMB. We can also compare to a simpler approach based on individual frequency maps, which we clean by subtracting a dust template. Our baseline results in Planck Collaboration XVII (2014) were based on this approach using 143 GHz and 217 GHz data, with the 857 GHz map projected out during the filtering process as a dust template. In this work we repeat this analysis. We use 857 GHz as a dust template in temperature, and no cleaning in polarization. The results of this test are presented in Fig. 12 and Table 2 with the labels “143 GHz” and “217 GHz”, where it can be seen that we obtain lensing bandpowers that are consistent with the MV reconstruction for the conservative multipole range. In the simulations used to characterize these reconstructions, we add coloured Gaussian noise to the 143 GHz and 217 GHz temperature map simulations, designed to mimic the power of unsubtracted extragalactic foregrounds. For this we use the same source model used in Planck Collaboration XVII (2014). We also add Gaussian power to the polarization maps, designed to mimic the power due to Galactic dust contamination that can be seen on large angular scales. The resulting temperature and polarization simulations have power spectra that agree with those of the data at the percent level in power.
We can also test the stability of the lensing reconstruction to the choice of the SMICA componentseparation method over other alternatives such as NILC, SEVEM, and Commander. The results of these comparisons are also shown in Table 2; all are very consistent with the SMICAbased reconstruction over the conservative multipole range, although the PTEs for the overall amplitude at highL (400 <L ≤ 2048) are low for NILC and Commander. For the SEVEM and Commander analyses, polarization maps were not available at the high resolution used here (N_{side} = 2048), and so we have limited the comparisons to temperature only.
As a test of Galactic contamination, we also repeat our baseline analysis (performed on a fraction f_{sky} = 0.7 of the sky) using a more aggressive (f_{sky} = 0.8) and more conservative (f_{sky} = 0.6) Galactic mask. These masks are constructed by thresholding maps from the Planck highfrequency channels to achieve the desired sky fraction. In Table 2 we see that for both alternative sky fractions we estimate lensing bandpowers that are consistent with the baseline.
As a final and very powerful test of highfrequency foreground contamination, we also perform a lens reconstruction in which we use the 353 GHz map as one of the four legs in the trispectrum used to estimate . Based on the approximate frequency scaling of dust (Gispert et al. 2000) we expect that any dust foreground bias “b_{dust}” to the lensing power spectrum estimate that we see in this test will scale roughly as (32)and so this should provide a sensitive test for such contamination in the SMICA map. The 353 GHz map has significantly more power than CMB+noiseonly simulations, due primarily to Galactic dust, and temperature fluctuations from the CIB. To obtain reasonable error bars from the 353 GHz simulations used to characterize this reconstruction, as with the other reconstructions we add an isotropic Gaussian sky signal designed to match the simulation power spectrum to a smoothed power spectrum of the data. When filtering the 353 GHz map, we use the same parameters as used for the SMICA map (except for the beam transfer function), to have the same weight given to different angular scales in the quadratic lensing estimators. As with the 143 GHz and 217 GHz analyses, we project out the 857 GHz map as a dust template from the 353 GHz map in temperature, but make no attempt to subtract foreground contamination in polarization. As can be seen in Fig. 12 and Table 2, we obtain good agreement between the “one leg 353 GHz” reconstruction and the MV lensing power spectrum. Lensing power is detected at 20σ even using 353 GHz as one of the four estimator legs. The stability of the lens reconstruction in the presence of the foreground power at 353 GHz is very striking. As a further test for the stability of the polarizationbased lens reconstructions, we can also use the “Pol. only” estimator from Sect. 4.1 with one leg given by the 353 GHz data. This reconstruction is very noisy, however we do obtain an overall amplitude as well as bandpowers that are consistent with the MV “Pol. only” reconstruction, as shown in Table 2.
4.3. Noise tests
As mentioned in Sect. 2, the FFP8 simulations on which our results are based underestimate the noise power by several percent at high ℓ. We have dealt with this problem in our simulations by including isotropic Gaussian power to make up for the difference. This neglects the fact that the missing noise component most likely has an anisotropic distribution (which could lead to errors in the meanfield subtraction), and could be nonGaussian as well. To test that our treatment of the missing noise component is adequate, we have performed several tests. Most stringently, we form quadratic lensing estimates using crosscorrelation between pairs of maps with independent noise realizations. We construct both a frequency cross 143 GHz × 217 GHz, as well as a cross between the first and second halves of the mission data. These estimators have no noise mean field, and the lensing power spectrum estimated from them is insensitive to nonGaussianity of the instrument noise. The results of this analysis are given in Table 2, where we see results that are consistent with the MV power spectrum estimate.
As an additional test, we have recreated the analysis of Planck Collaboration XVII (2014), where the missing noise power was accounted for by scaling the noise component of the FFP8 simulations by a small factor. This approach reasonably supposes that the anisotropy of the missing noise component is the same as that of the modeled component. This test is labelled “Noise scaling” in Table 2. Again, we see a power spectrum measurement with an amplitude that is very similar to the MV estimate (although we note that because the only difference in this test is the construction of the simulations that are used, the expected scatter between the “Noise scaling” and baseline approaches is difficult to determine).
4.4. Analysis choices
We test the stability of our reconstruction to several analysis choices, rerunning the baseline analysis implementing the following changes one at a time.

Use a highpass filter in temperature and polarization with ℓ_{min} = 1000 ratherthan ℓ_{min} = 100, as adopted in the baseline analysis.

Use a lowpass filter with ℓ_{max} = 1500 rather than ℓ_{max} = 2048, as used in the baseline analysis.

Apodize the lensing convergence estimate before power spectrum estimation, following the analysis in Planck Collaboration XVII (2014). This change reduces the size of the Monte Carlo correction, but does not produce a significant change in the lensing bandpower estimates.
The results of these tests are given in Table 2.
4.5. Biashardened estimators
Throughout this paper, we have used quadratic estimators optimized to detect the anisotropic covariance between CMB modes that is induced by (fixed) lensing. These estimators are biased by nonlensing sources of such mode coupling, including effects such as galaxy and pointsource masking, inhomogeneous instrumental noise, and beam asymmetries. As discussed in Sect. 2, we estimate these biases using Monte Carlo simulations and subtract them as a “meanfield” contribution from the estimated lensing potential (as in Eq. (A.17)). A complementary method, which is less reliant on the quality of the Monte Carlo simulations used to perform this debiasing, is the biashardening procedure advocated by Namikawa et al. (2013). In this approach, one constructs a new quadratic lensing estimator that is orthogonalized to worrisome sources of bias. The construction of these estimators is reviewed in Appendix B. In the analysis here we focus on temperatureonly biashardened estimators to investigate the effects of biashardening, because meanfield corrections are much smaller in polarization than in temperature. We denote these estimators as , where X denotes the bias(es) that are orthogonalized against. Here, we consider the biases due to masking (M), inhomogeneous noise (N), and the shot noise due to unresolved point sources (S). As discussed in Osborne et al. (2014), the sourcehardened estimator also reduces contamination from effects such as the crosscorrelation between the unresolved sources and the lensing potential.
Fig. 15
Upper: power spectrum of the lowL meanfield (measured from Monte Carlo simulations) for the TT lensing estimator as well as several biashardened estimators. The meanfield power spectrum of the TE lensing estimator is plotted for comparison, and the fiducial lensing potential power spectrum is shown in dashed black. To avoid visual clutter we have not shown the sourcehardened estimator TT−S; its spectrum is midway between the noisehardened (TT−N) and maskhardened (TT−M) cases. Lower: power spectra of the differences between the TT lensing estimate and the biashardened reconstructions or the TE reconstruction (with the same colour scheme as in the top panel). The expected power spectra of the differences, estimated from simulations, are plotted as black dotted lines. 
Meanfield corrections are largest at lowL (the power spectrum of the meanfield correction is larger than the fiducial lensing potential power spectrum for L ≲ 100), and so we begin by looking closely at these scales. In Fig. 15 we plot the power spectra of the meanfields for each of the biashardened estimators. In the lower panel we also plot the difference between each estimator and the nonbiashardened TT estimator. For L ≥ 8, we see agreement between all of the biashardened estimators and the nonbiashardened reconstruction, regardless of the much smaller meanfield corrections for the former. We have also used the biashardened estimators to measure the lensing potential power spectrum on smaller scales. In Fig. 12 and Table 2 we report lensing bandpower estimates using the biashardened estimators, which we find to be consistent with the baseline MV reconstruction for L ≤ 400.
4.6. Null tests
In addition to the consistency tests described above, we have also performed several direct null tests, either using estimators or maps for which the lensing signal is expected to be zero. These are plotted in Fig. 13, and significance statistics for each are given in Table 3. We discuss them in more detail below.
Halfring difference:
the Planck observation strategy consists of scanning the sky in rings with an opening angle of approximately 85°. Each scan ring is observed for between 39 and 65 minutes, and then the satellite is repointed. This leads to a natural “halfring” null test in which we run our standard lensing estimator on the halfdifference of maps constructed from the first and second half of each scanning ring, which should consist entirely of noise. The PTE for the spectrum of the lensing reconstruction estimated from these halfring difference maps is rather low for the conservative multipole range (see Table 3). Since the maps should be only noise by construction, to obtain an acceptable χ^{2} for the power spectra of reconstructions based on such maps requires very good modeling of the instrument noise. As can be seen from the factor of 200 scaling applied in Fig. 13, the impact on the reconstructed of minor errors in the noise modeling that likely drive the low PTEs for the null test should be negligible.
Halfmission difference:
noise that is correlated between rings (for example, due to glitches and destriping) can be nulled in the halfring difference. Another differencing approach, which avoids this problem, is to divide the entire mission into two halves that are then differenced. This halfmission difference requires a slightly larger analysis mask, to account for the fact that the sky coverage is imperfect in both the first and second halves of the mission and some pixels are missed. The PTEs for the halfmission test are reasonable.
Curlmode estimators:
in general, deflections due to lensing can be decomposed into gradient (φ) and curl (ψ) modes. The curl modes are expected to be consistent with zero for lensing by scalar perturbations at Planck reconstruction noise levels. We construct curlmode power spectrum estimators, analogous to those used for the gradient lensing modes, and measure their power spectra. Fullsky weight functions for these estimators are presented in Namikawa et al. (2012). Note that although they do not couple directly to the lensing potential, the curl power spectra do receive “N1”type contributions from the lensing trispectrum, which we estimate analytically and subtract as with the power spectrum estimates. We do not make “PS” or “MC” corrections for the curlmode power spectra. In Table 3 we report estimated amplitudes for a signal with the shape of the fiducial lensing power spectrum in the curl estimates. These amplitudes are all consistent with zero at better than 2σ, with the exception of the TT reconstruction over the highL range (400 <L ≤ 2048). Indeed, there is mild evidence for a broad feature in the TT curlmode reconstruction centred around L = 500 (see Fig. 13), which drives the low PTE of the TT curlmode power spectrum over the highL multipole range. Considering TT alone, the average curl power is low by about 2.9σ in the highL range, which is suggestive but not clearly unacceptable given the number of tests performed. Combined with the polarization reconstructions, which on their own appear consistent, the MV estimate remains low but only at 2σ over the highL range. Since the origin of this feature in the curlmode spectrum is currently not understood, we have chosen to adopt the conservative multipole range for the baseline Planck 2015 lensing likelihood. This multipole range has little overlap with that covered by the curlmode feature and was determined in advance of the curl result as it retains the majority of the S/N in the reconstruction.
5. Conclusions
We have presented a first analysis of gravitational lensing in the Planck 2015 fullmission data set. Using temperature and polarization data, we make the most powerful measurement of CMB lensing to date, with an overall significance of around 40σ. This paper is accompanied by the public release of the lensing potential map (as well as simulations that can be used to characterize the map, e.g. for crosscorrelation analyses with external data) and a conservative lensing bandpower likelihood.
We have performed a number of tests to verify the internal consistency of the lens reconstruction, in particular we have tested the compatibility between individual temperature and polarization estimators, sensitivity to various analysis choices, and possible foreground contamination.
Given Planck’s unique fullsky coverage, the fullmission maps are unlikely to be displaced as the most powerful data set for cosmological lensing analysis for the next several years (until high signaltonoise E and Bmode polarization measurements can be made on sufficiently large sky fractions). There is some room, however, for improvement on the analysis presented here. The inversevariance filtering we have used does not account for variations in the instrument noise level across the sky, which could be incorporated to reduce the reconstruction noise (particularly in polarization, where instrumental noise dominates the signal). We have highpass filtered all results to L ≥ 8, due to concerns about the lensing meanfield correction. A more aggressive analysis could potentially remove this cut. The conservative L ≤ 400 cut that we make in our baseline likelihood, imposed because of mild evidence of a broad feature in curlmode tests beyond this range, might also be relaxed in a future analysis, although only with minor improvements in the statistical power of the likelihood.
The microwave background is a unique source for lensing studies, as the most distant and wellunderstood source plane that can be observed. The measurement presented here represents an integrated measurement of the total matter distribution in the entire observable Universe, and another powerful test of the veracity of the concordance ΛCDM cosmological model.
Planck (http://www.esa.int/Planck) is a project of the European Space Agency (ESA) with instruments provided by two scientific consortia funded by ESA member states and led by Principal Investigators from France and Italy, telescope reflectors provided through a collaboration between ESA and a scientific consortium led and funded by Denmark, and additional contributions from NASA (USA).
We mask clusters with S/N ≥ 5 in the Planck cluster catalogue that accompanied the 2013 release (PSZ1; Planck Collaboration XXIX 2014). The mass limit is redshift dependent, with a highredshift limit at 80% completeness of M_{500} ≈ 6 × 10^{14}M_{⊙}. If we conservatively adopt this mass limit (we certainly mask lowredshift clusters to lower masses), the results of van Engelen et al. (2014) suggest that the SZ trispectrum at 143 GHz gives a positive bias of at most a few percent in the reconstructed lensing power spectrum for multipoles L < 2000, while the lensingSZSZ bispectrum gives a negative bias of similar size. The SZ signal is much smaller in the 217 GHz temperature data, which is around the null of the (thermal) SZ effect.
To calculate the scaling between the CIB map and the lensing potential φ, we model the CIB using the simple model of Hall et al. (2010) for its redshift and frequency dependence. Further details are given in Appendix D.
The amplitude quoted in Planck Collaboration XVII (2014) is actually , however this is measured with respect to a slightly different fiducial cosmology than the one used here. Those measurements have been renormalized to the fiducial model used for this paper with a factor of 1.09.
This direction differs from that in Eq. (14), since the orthogonal direction in the A_{s}–ℓ_{eq} plane is not that much worse constrained, so the Fisher information in the threeparameter space is not fully dominated by a single direction.
The smoothing effect of lensing in the PlanckTT power spectrum is detected at around 10σ (Planck Collaboration XIII 2016). It is also detected at lower significance in the polarization power spectra.
The empirical parameter dependence of ⟨ d^{2} ⟩ ^{1 / 2} on Ω_{m} for samples from the CMB power spectrum posterior differs from what one would infer from Eqs. (15)and (18). These equations assume fixed n_{s} and Ω_{b}h^{2}, which is appropriate for our lensingonly constraints with the assumed priors, but for the CMB samples the fractional dispersion in n_{s} and Ω_{b} is comparable to that in σ_{8}, h, and Ω_{m} and so cannot be neglected.
In the 2015 Planck data release, there is some evidence in the polarization power spectra for uncorrected systematic effects associated with temperaturetopolarization leakage (Planck Collaboration XIII 2016; Planck Collaboration XI 2016). Although we find no evidence for inconsistencies in the polarizationbased lens reconstructions, the statistical errors are large and may obscure small systematic effects that are subdominant for the current analysis. Leakage from the CMB temperature anisotropies could impact the lensing analysis in several ways. First, the disconnected (Gaussian) part of the CMB 4point function is modified, and this would affect both our meanfield and N0 corrections (see Appendices A.2 and A.3). These corrections are made using a mix of data and simulations, such that even if the simulations do not fully capture the leakage (note they do include leakage due to mainbeam mismatch, for example), the additive bias to the lensing power spectrum is only secondorder in any unmodelled effects. The second way that leakage can affect the lensing power spectrum is through changes to the lensing response functions in Eq. (A.13), which propagate through to normalization errors. This should be partially captured by a MonteCarlo correction that we make to the lensing power spectrum. We have not attempted to quantify more carefully any residual effects of temperaturetopolarization leakage, since, ultimately, the polarization data carries relatively little weight in our final MV reconstruction.
A sample implementation of these weights can be found at http://github.com/dhanson/quicklens in the file quicklens/qest/lens.py
Acknowledgments
The Planck Collaboration acknowledges the support of: ESA; CNES and CNRS/INSUIN2P3INP (France); ASI, CNR, and INAF (Italy); NASA and DoE (USA); STFC and UKSA (UK); CSIC, MINECO, JA, and RES (Spain); Tekes, AoF, and CSC (Finland); DLR and MPG (Germany); CSA (Canada); DTU Space (Denmark); SER/SSO (Switzerland); RCN (Norway); SFI (Ireland); FCT/MCTES (Portugal); ERC and PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www.cosmos.esa.int/web/planck/planckcollaboration. Some of the results in this paper have been derived using the HEALPix package. We acknowledge support from the Science and Technology Facilities Council [grant number ST/L000652/1]. The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/20072013)/ ERC Grant Agreement No. [616170]. Part of this work was undertaken on the STFC DiRAC HPC Facilities at the University of Cambridge funded by UK BIS National Einfrastructure capital grants.
References
 Ade, P., Aikin, R. W., Barkats, D., et al. 2014, Phys. Rev. Lett., 112, 241101 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Anderson, L., Aubourg, É., Bailey, S., et al. 2014, MNRAS, 441, 24 [NASA ADS] [CrossRef] [Google Scholar]
 Beutler, F., Blake, C., Colless, M., et al. 2011, MNRAS, 416, 3017 [NASA ADS] [CrossRef] [Google Scholar]
 BICEP2/Keck Array and Planck Collaborations 2015, Phys. Rev. Lett., 114, 101301 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Bucher, M., Carvalho, C. S., Moodley, K., & Remazeilles, M. 2012, Phys. Rev. D, 85, 043016 [NASA ADS] [CrossRef] [Google Scholar]
 Bunn, E. F., Fisher, K. B., Hoffman, Y., et al. 1994, ApJ, 432, L75 [NASA ADS] [CrossRef] [Google Scholar]
 Das, S., Louis, T., Nolta, M. R., et al. 2014, JCAP, 1404, 014 [Google Scholar]
 Das, S., Sherwin, B. D., Aguirre, P., et al. 2011, Phys. Rev. Lett., 107, 021301 [NASA ADS] [CrossRef] [Google Scholar]
 Dvorkin, C., & Smith, K. M. 2009, Phys. Rev. D, 79, 043003 [NASA ADS] [CrossRef] [Google Scholar]
 Efstathiou, G. 2014, MNRAS, 440, 1138 [NASA ADS] [CrossRef] [Google Scholar]
 Eisenstein, D. J., & Hu, W. 1998, ApJ, 496, 605 [NASA ADS] [CrossRef] [Google Scholar]
 Fan, X.H., Strauss, M. A., Becker, R. H., et al. 2006, AJ, 132, 117 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Finkelstein, S. L., Ryan, Jr., R. E., Papovich, C., et al. 2015, ApJ, 810, 71 [NASA ADS] [CrossRef] [Google Scholar]
 Gispert, R., Lagache, G., & Puget, J. 2000, A&A, 360, 1 [NASA ADS] [Google Scholar]
 Górski, K. M., Hivon, E., Banday, A. J., et al. 2005, ApJ, 622, 759 [NASA ADS] [CrossRef] [Google Scholar]
 Hall, N., Knox, L., Reichardt, C., et al. 2010, ApJ, 718, 632 [NASA ADS] [CrossRef] [Google Scholar]
 Hanson, D., Challinor, A., Efstathiou, G., & Bielewicz, P. 2011, Phys. Rev. D, 83, 043005 [NASA ADS] [CrossRef] [Google Scholar]
 Hanson, D., Hoover, S., Crites, A., et al. 2013, Phys. Rev. Lett., 111, 141301 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
 Hinshaw, G., Larson, D., Komatsu, E., et al. 2013, ApJS, 208, 19 [NASA ADS] [CrossRef] [Google Scholar]
 Hirata, C. M., Ho, S., Padmanabhan, N., Seljak, U., & Bahcall, N. A. 2008, Phys. Rev. D, 78, 043520 [NASA ADS] [CrossRef] [Google Scholar]
 Hu, W. 2001, Phys. Rev. D, 64, 083005 [NASA ADS] [CrossRef] [Google Scholar]
 Hu, W. 2002, Phys. Rev. D, 65, 023003 [NASA ADS] [CrossRef] [Google Scholar]
 Hu, W., & Okamoto, T. 2002, ApJ, 574, 566 [Google Scholar]
 Humphreys, E., Reid, M. J., Moran, J. M., Greenhill, L. J., & Argon, A. L. 2013, ApJ, 775, 13 [Google Scholar]
 Kesden, M. H., Cooray, A., & Kamionkowski, M. 2003, Phys. Rev. D, 67, 123507 [NASA ADS] [CrossRef] [Google Scholar]
 Lewis, A., & Bridle, S. 2002, Phys. Rev. D, 66, 103511 [NASA ADS] [CrossRef] [Google Scholar]
 Lewis, A., & Challinor, A. 2006, Phys. Rept., 429, 1 [Google Scholar]
 Lewis, A., Challinor, A., & Lasenby, A. 2000, ApJ, 538, 473 [NASA ADS] [CrossRef] [Google Scholar]
 Lewis, A., Challinor, A., & Hanson, D. 2011, J. Cosmol. Astropart., 3, 18 [Google Scholar]
 Limber, D. N. 1954, ApJ, 119, 655 [NASA ADS] [CrossRef] [Google Scholar]
 Namikawa, T., Yamauchi, D., & Taruya, A. 2012, J. Cosmol. Astropart., 1201, 007 [Google Scholar]
 Namikawa, T., Hanson, D., & Takahashi, R. 2013, MNRAS, 431, 609 [NASA ADS] [CrossRef] [Google Scholar]
 Okamoto, T., & Hu, W. 2003, Phys. Rev. D, 67, 083002 [NASA ADS] [CrossRef] [Google Scholar]
 Osborne, S. J., Hanson, D., & Doré, O. 2014, J. Cosmol. Astropart., 1403, 024 [Google Scholar]
 Pan, Z., Knox, L., & White, M. 2014, MNRAS, 445, 2941 [NASA ADS] [CrossRef] [Google Scholar]
 Pettini, M., & Cooke, R. 2012, MNRAS, 425, 2477 [CrossRef] [Google Scholar]
 Planck Collaboration XVII. 2014, A&A, 571, A17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XVIII. 2014, A&A, 571, A18 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XXIX. 2014, A&A, 571, A29 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration I. 2016, A&A, 594, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration II. 2016, A&A, 594, A2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration III. 2016, A&A, 594, A3 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration IV. 2016, A&A, 594, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration V. 2016, A&A, 594, A5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration VI. 2016, A&A, 594, A6 [Google Scholar]
 Planck Collaboration VII. 2016, A&A, 594, A7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration VIII. 2016, A&A, 594, A8 [Google Scholar]
 Planck Collaboration IX. 2016, A&A, 594, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration X. 2016, A&A, 594, A10 [Google Scholar]
 Planck Collaboration XI. 2016, A&A, 594, A11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XII. 2016, A&A, 594, A12 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XIII. 2016, A&A, 594, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XIV. 2016, A&A, 594, A14 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XV. 2016, A&A, 594, A15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XVI. 2016, A&A, 594, A16 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XVII. 2016, A&A, 594, A17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XVIII. 2016, A&A, 594, A18 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XIX. 2016, A&A, 594, A19 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XX. 2016, A&A, 594, A20 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XXI. 2016, A&A, 594, A21 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XXII. 2016, A&A, 594, A22 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XXIII. 2016, A&A, 594, A23 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XXIV. 2016, A&A, 594, A24 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XXV. 2016, A&A, 594, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XXVI. 2016, A&A, 594, A26 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XXVII. 2016, A&A, 594, A27 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Planck Collaboration XXVIII. 2016, A&A, 594, A28 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 POLARBEAR Collaboration. 2014a, Phys. Rev. Lett., 112, 131302 [NASA ADS] [CrossRef] [Google Scholar]
 POLARBEAR Collaboration. 2014b, Phys. Rev. Lett., 113, 021301 [NASA ADS] [CrossRef] [Google Scholar]
 Regan, D., Shellard, E., & Fergusson, J. 2010, Phys. Rev. D, 82, 023520 [NASA ADS] [CrossRef] [Google Scholar]
 Riess, A. G., Macri, L., Casertano, S., et al. 2011, ApJ, 730, 119 [NASA ADS] [CrossRef] [Google Scholar]
 Ross, A. J., Samushia, L., Howlett, C., et al. 2015, MNRAS, 449, 835 [NASA ADS] [CrossRef] [Google Scholar]
 Schmittfull, M. M., Challinor, A., Hanson, D., & Lewis, A. 2013, Phys. Rev. D, 88, 063012 [NASA ADS] [CrossRef] [Google Scholar]
 Smith, K. M., Zahn, O., Dore, O., & Nolta, M. R. 2007, Phys. Rev. D, 76, 043510 [NASA ADS] [CrossRef] [Google Scholar]
 Smith, K. M., Cooray, A., Das, S., et al. 2009, AIP Conf. Proc., 1141, 121 [NASA ADS] [CrossRef] [Google Scholar]
 Song, Y.S., Cooray, A., Knox, L., & Zaldarriaga, M. 2003, ApJ, 590, 664 [NASA ADS] [CrossRef] [Google Scholar]
 van Engelen, A., Keisler, R., Zahn, O., et al. 2012, ApJ, 756, 142 [NASA ADS] [CrossRef] [Google Scholar]
 van Engelen, A., Bhattacharya, S., Sehgal, N., et al. 2014, ApJ, 786, 13 [NASA ADS] [CrossRef] [Google Scholar]
 van Engelen, A., Sherwin, B. D., Sehgal, N., et al. 2015, ApJ, 808, 7 [NASA ADS] [CrossRef] [Google Scholar]
Appendix A: Pipeline
In this appendix we provide a more detailed technical overview of the pipeline that we use to extract lensing information from the Planck maps. Starting from input CMB sky maps the pipeline has the following three distinct analysis stages.

(1)
Filtering. In this step, we perform a linear operation on themaps, which performs three tasks.

a.
Mask out regions of the sky that may be contaminated bypoint sources or residual foreground emission (primarilyfrom the Galaxy).

b.
Deconvolve the instrumental beam and pixel transfer functions.

c.
Downweight noisy modes to allow later steps of the pipeline to produce optimal (in the minimumvariance sense) estimates of the lensing potential and its power spectrum.

a.
Quadratic Estimators. The filtered CMB multipoles are then fed into quadratic estimators designed to extract the statistical anisotropy that is induced by lensing. These estimators are formed by summing over pairs of CMB fluctuations with a weight function optimized to detect the offdiagonal contributions to their covariance matrix from (fixed) lenses. This step produces maps of the estimated lensing potential , and is described in detail in Appendix A.2.
Power Spectrum Estimation. To estimate the power spectrum of the lensing potential, we exploit the nonGaussianity induced by lensing. At first order in the power spectrum , lensing generates a contribution to the connected 4point function (or trispectrum) of the observed CMB, which we probe using auto and crossspectra of the 2point quadratic estimators obtained in step (2) of the pipeline. There are two complications here that are worth noting: the auto and crossspectra of the lensing estimates have noise biases even in the absence of lensing, which must be carefully estimated and subtracted; and nonGaussian contamination from unresolved point sources can mimic lensing and bias the reconstructed power spectrum. Our approaches to these issues are described in Appendix A.3.
We now proceed to describe the individual steps of the lensing pipeline in more detail.
Appendix A.1: Filtering
At this stage in the pipeline, we have noisy maps of the CMB for both data and simulations. To construct minimumvariance estimates of the lensing potential and its power spectrum, we need to filter these maps to downweight noisedominated modes, as well as to deconvolve the transfer functions due to the finite beam size and pixelization.
To derive the optimal (minimumvariance) filter to apply, we use a simplified model of the data given by (A.1)where d_{i} is a single long vector containing the pixelized T, Q, and U sky maps. The “pointing matrix” takes an input sky signal (consisting of T, E, and B multipoles collectively indexed by k), convolves it with a beam and pixel transfer function, and then performs a harmonic transform to map space. The harmonic modes of the sky itself are denoted as s_{k}, and n_{i} is the map noise realization. We have used indices i and k for quantities in mapspace and harmonicspace respectively. Explicitly, the indices k and i are mapped aswhere p denotes a pixel. In this notation, we model the pointing matrix as (A.4)where H_{ℓ} is the HEALPix (Górski et al. 2005) N_{side} = 2048 pixel transfer function, B_{ℓ} is the effective beam transfer function for the map under consideration, and represents the realvalued spherical harmonic for field X evaluated at the center of pixel p in map M.
If both the sky signal and noise are Gaussian random fields (with covariance matrices and ⟨ n_{i}n_{i′} ⟩ ≡ N_{ii′}, respectively) then the inversevariance filtered estimates of the beamdeconvolved sky signal are given in matrix notation by (Bunn et al. 1994) (A.5)We use this equation to obtain the inversevariance filtered T, E, and B multipoles that are fed into our quadratic estimators, with one additional rescaling discussed below. The large bracketed matrix inverse is performed using conjugate descent with a multigrid preconditioner, following Smith et al. (2007).
For the purposes of filtering, we use a simplified model for the covariance matrices of s_{k} and n_{i} in which they are diagonal in harmonic space and pixel space, respectively, with (A.6)Here, are the fiducial theoretical CMB power spectra and gives the noise variance for pixel p in map M. With this diagonal assumption, we have ignored the crosscorrelation for the purposes of filtering. This is slightly suboptimal, but has the advantage of allowing the temperature and polarization maps to be filtered independently. It also has the benefit of making analytical calculations for the estimator normalization simpler (see Sect. A.2). Also for simplicity, we take to be constant over all unmasked pixels and given by (A.7)Here is the map noise level in μK arcmin, N_{pix} is the number of pixels in an N_{side} = 2048HEALPix map, and M_{p} is a mask map (which is zero for masked pixels, and unity otherwise). We always take the noise level to be the same for paired Q/U maps. The mask term in Eq. (A.7)takes the noise level to infinity for masked pixels. In the filtering operation, which only involves the inverse of the noise matrix, this sets masked pixels to zero.
After evaluating Eq. (A.5), we have a set of inversevariance filtered sky multipoles for X ∈ { T,E,B }. The simplified CMB+whitenoise model above results in a slightly suboptimal filtering procedure since it ignores the following:

(1)
variations in the noise level across the sky due to the uneven hitdistribution (the Ecliptic poles receive more coverage than theequator for the Planck scan strategy);

(2)
scaledependence of the noise power; and

(3)
foreground power.
In Planck Collaboration XVII (2014) we showed that the suboptimality due to (1) for temperaturebased lensing reconstruction was small (accounting for it properly was estimated to improve the uncertainty on the overall lensing amplitude by 4%). The loss will be slightly smaller here for temperature, owing to a more even hit distribution in the fullmission data than in the nominalmission data used in Planck Collaboration XVII (2014). The loss will be larger for polarization, where noise is a more important part of the total error budget, although we have not evaluated the size of the potential degradation. To compensate for (2) and (3) we make a postcorrection by rescaling the modes by a quality factor as a function of scale to obtain final filtered multipoles that are closer to optimal. We denote these as , and they are given by (A.8)These inversevariance filtered multipoles are the input to the lensing estimators described in Sect. A.2. The choice of is discussed below.
For analytical calculations, it is useful to have an approximation to the filtering procedure that is diagonal in harmonic space (i.e. neglecting the small amount of modemixing induced by the masking). In this approximation, the filtered multipoles are given by (A.9)where is a noise realization and (A.10)Here, are the fiducial CMB autospectra used in Eq. (A.6), and the quantity (A.11)is the pixel and beamdeconvolved noise power spectrum for the field X, where is the appropriate map whitenoise level in μK arcmin. We choose such that (A.12)where f_{sky} = ∑ _{p}M_{p}/N_{pix} is the unmasked sky fraction and the approximation sign indicates a smoothing over multipoles.
Appendix A.2: Quadratic estimators
Ensemble averaging over a fixed realization of the CMB lensing potential, the CMB covariance matrix acquires offdiagonal elements given by (A.13)with the fields X_{ℓm},Z_{ℓm} ∈ { T_{ℓm}, E_{ℓm}, B_{ℓm} }. The covariance response functions for the possible field combinations can be found in Okamoto & Hu (2003). They are linear in the (lensed) CMB power spectra, and are only nonzero if ℓ_{1} + ℓ_{2} + L is even for evenparity combinations, e.g. TT and TE, and ℓ_{1} + ℓ_{2} + L is odd for the oddparity combinations TB and EB. Furthermore, the are real for the evenparity combinations and imaginary for the oddparity. We use the offdiagonal covariance to estimate the lensing potential; after the inversevariancefiltered sky maps are generated, pairs of fields , , , are fed into quadratic estimators to estimate the lensing potential. A general quadratic estimator is given as a function of input fields and as (A.14)where is a set of weight functions that define the estimator x, along with the input inversevariance filtered multipoles , for a particular map or pair of maps. The choice of , , or for and depends on the specific estimator x. In most of what follows we shall drop the unless it is necessary to avoid ambiguity.
Optimal lensing estimators use a matched filter for the lensinginduced covariance of Eq. (A.13). We denote these as W^{XZ} for the fields X and Z. The temperatureonly optimal estimator, for example, uses , where the “fid” subscript indicates that the lensing weight function is evaluated for the fiducial cosmological model given at the end of Sect. 2. For the oddparity combinations, the matched filter is minus the covariance response function, e.g. . Neglecting the lensing of any primordial Bmode signal (certainly a valid approximation for scales ℓ_{B}> 100 with Planck polarization sensitivity), generally there are eight possible lensing estimators based on the possible combinations of T, E, and B. (This reduces to five estimators for our baseline analysis that uses a foregroundcleaned map of the CMB in temperature and polarization, and for which there is no distinction between TE and ET, for example.)
The quadratic estimators defined by Eq. (A.14)will receive contributions from nonlensing sources such as masking, beam asymmetry, and inhomogeneity of the instrumental noise. We determine and correct for this bias by averaging the reconstruction over Monte Carlo simulations that include these effects, thereby estimating a “meanfield” that we then subtract.
The response of a quadratic estimator to the covariance given in Eq. (A.13)(averaged over CMB realizations with a single mode φ_{LM} of the lensing potential held fixed) is, in the diagonal approximation of Eq. (A.9), (A.15)where (A.16)As a baseline, all of the results in this paper are obtained using the fiducial model at the end of Sect. 2 to calculate the that appear in the estimator response. However, for cosmological parameter sampling with the lensing likelihood the normalization is recalculated using the appropriate CMB power spectra as the parameter space is explored. This is discussed further in Appendix C.
Putting the above together, we form estimates of the lensing potential as (A.17)We can also sum the individual estimators into a combined minimumvariance estimator (MV) as (A.18)where the sum is taken over the eight lensing estimators (TT, EE, TE, TB, EB, ET, BT, and BE).
Finally, we note an important implementation detail. Naively, evaluating an estimator of the form in Eq. (A.14)requires operations, which would be very computationally expensive at Planck resolution. Fortunately, all of the weight functions used in this paper can be rewritten as a sum of separable terms with the following form (A.19)This separability leads to estimators that can be evaluated with operations in position space as (A.20)The separability of the weight functions also allows the response functions to be calculated quickly analytically; using the positionspace approach given in Dvorkin & Smith (2009) they can be evaluated in operations^{14}.
Appendix A.3: Power spectrum estimation
Ensemble averaging over realizations of both the CMB lensing potential and the primary temperature and polarization fluctuations, the CMB becomes nonGaussian. At first order in the lensing potential power spectrum this nonGaussianity is manifest as a connected 4point function (Hu 2001) (A.21)As might be expected given the presence of the covariance response functions in this expression, estimators to extract from the trispectrum can be written using the quadratic estimators above as building blocks. We write the crossspectrum of two quadratic estimators explicitly as (A.22)where f_{sky} = ∑ _{p}M_{p}/N_{pix} is the unmasked sky fraction, with M_{p} the mask map used in Eq. (A.7). We form estimates of the lensing potential power spectrum (based on estimators x and y) as (A.23)There are several correction terms here, which are discussed in more detail below. We also plot them for the MV reconstruction based on the Planck 2015 SMICA maps in Fig. A.1.
Fig. A.1
Lens reconstruction bias terms for the MV lensing estimator applied to the Planck 2015 SMICA maps. Dashed lines indicate regions where the bias term is negative. The fiducial ΛCDM theory power spectrum is plotted as the black solid line. 
The term represents the disconnected contribution to the 4point function, which would be nonzero even in the absence of lensing. We estimate this term by replacing some of the data fields with those from two sets of independent simulations (labelled MC1 and MC2), and then average over realizations as (A.24)This method for determining the disconnected bias can be derived from the Edgeworth expansion of the lensed CMB (Regan et al. 2010; neglecting the Tφ and Eφ correlations).If the simulations have a covariance differing from reality by a linear (in general, anisotropic) correction, this form of the N0 estimator using a mixture of data and simulations is insensitive to the correction at linear order (Namikawa et al. 2013). The largest difference we see between simulations and data is about 5% in power before correction, so the quadratic error should be at most about (0.05)^{2} = 0.0025, corresponding to an error of less than 0.4σ for aggressive bins at L ≤ 400 rising to around 1.3σ at L = 2000. Since we also match the data calibration, and add isotropic foreground and noise power to the simulations to match the observed amplitude and shape of the power spectrum of the data, the isotropic part of the error is actually close to zero. A residual error only arises at second order due to our inability to capture any statistical anisotropy in the missing foreground and noise power, and hence should be much smaller than the above estimates. This makes the N0 subtraction rather robust, despite it being substantially larger than the signal on small scales.Furthermore, the mixed form reduces correlations of the lensing power spectrum estimates between different multipoles (Hanson et al. 2011) and with the measured CMB power spectra (Schmittfull et al. 2013).
The term corrects for the nonGaussian secondary contractions (the other permutations in Eq. A.21) due to lensing, as discussed in Kesden et al. (2003). We evaluate it using the flatsky approximation in 2D Fourier space as (A.25)where . Here the weight functions are flatsky lensing weight functions; they can be found in Hu & Okamoto (2002).
We characterize any differences between the average lensing power obtained on simulations with the input power. This is determined by (A.26)The MC correction is included to account for possible issues in the estimation of the normalization, mixing of power between power spectrum bins, and errors in the calculation of the N1 bias. Although in principle several of these effects should be included as multiplicative rather than additive corrections, we find that the MC correction is generally small enough (less than 10% of the fiducial lensing power spectrum) that it does not matter at our experimental sensitivity whether we apply the correction additively or multiplicatively.
Finally, we make a correction for the nonGaussianity due to the shotnoise of unresolved point sources in temperature. This is given by (A.27)where is an estimate of the shotnoise amplitude and S^{2} denotes the weight function (nonzero only for TT) defined by (A.28)The ℛ^{xS2} is given by Eq. (A.16)but with replaced by . Following Osborne et al. (2014), the estimate for the shotnoise trispectrum amplitude is obtained in an analogous way to Eq. (A.23), but using the quadratic estimator formed with the pointsource weight function above rather than the lensing weight function. The overall source amplitude is then estimated as (A.29)where the sums are taken over 100 ≤ L ≤ 2048. Note that ℛ^{S2S2} is given by Eq. (A.16)but with both and replaced by .
We use Eq. (A.23)to estimate the lensing potential power spectrum, which we then bin into bandpowers for plotting as well as constructing a lensing likelihood. We use Monte Carlo simulations of the power spectrum estimation to construct error bars for these bandpowers. There is one technical point here, which is that the realizationdependent correction term can be cumbersome to compute. Instead, for the purpose of determining error bars, we use a semianalytical approximation to this term given by (A.30)where the empirical power spectra of the filtered fields are (A.31)
Appendix B: Biashardened estimators
The quadratic lensing estimators of Appendix A.2 can be biased by nonlensing sources of statistical anisotropy such as beam asymmetry, noise inhomogeneity, and masking. In our fiducial analyses, we estimate these meanfield biases using Monte Carlo simulations. However, this approach can be sensitive to the fidelity of the simulations, particularly for the reconstruction on large angular scales where the meanfield biases can be orders of magnitude above the lensing signals of interest. As a crosscheck, we also take a more datadependent approach to correcting these biases. As with lensing, most sources of statistical anisotropy in a map can be associated with a covariance response function , and a field z_{LM} that describes the spatial dependence of the anisotropy. It is possible to construct quadratic estimators for z_{LM}, and then calculate and subtract the resulting lensing bias. This procedure amounts to constructing special “biashardened” estimators (Namikawa et al. 2013). Given a source of bias z, we construct a biashardened estimator with weight function (B.1)where and are generalizations of Eq. (A.16), and is the matched filter for . This method is potentially less susceptible to errors in the meanfield estimate (due to inaccuracies in the simulations used to obtain it), although it has several caveats that are discussed in more detail in Namikawa et al. (2013).
Appendix C: Power spectrum likelihood
Here, we provide more detail on the components of our lensing likelihood (given in Eq. (3), and copied below for convenience) (C.1)The bandpower binning function is given by (C.2)where V_{L} is an approximation to the variance of the power spectrum estimate. For a given crossspectrum between estimators x and y we use (C.3)which produces minimumvariance estimates of the lensing amplitude for each bin in the limit that the sample variance of the lenses is negligible (a reasonable approximation for Planck, which is noise dominated on any individual mode).
The dependence of the likelihood on cosmological parameters enters through the “theory” spectrum . This is the expected value of the estimated spectrum at each point in parameter space. It depends on the cosmological parameters θ in several ways:

directly, through the theory spectrum;

indirectly (but linearly) on , via the theorydependence of N^{(1)} (Eq. (A.25)); and

indirectly, and nonlinearly, on the CMB power spectra , , and through the estimator normalization and N^{(1)} (from the theorydependent covariance response of Eq. (A.13)).
We neglect any other theory dependence, for example in the MC correction that is hard to quantify. In detail, for a given set of cosmological parameters θ, we should calculate (C.4)where the component of the response functions ℛ in the numerator, as well as the N^{(1)} term, are calculated using the CMB power spectra for parameters θ.
Fully recalculating everything for each point in parameter space is prohibitively slow, but for small deviations from the fiducial model we can use a linearized approximation. The dependence on is already linear, and expanding the CMB power spectrum dependence to linear order about the fiducial model we can write (C.5)where j sums over the various CMB power spectra. The matrix (from Eq. (A.25)) gives the exact linear dependence of N^{(1)} on the lensing potential for fixed CMB power spectra, and can be precomputed along with the other derivative matrices for the fiducial model. The binned likelihood of Eq. (C.1)then depends on a binned “theory” power spectrum given in the linear approximation by (C.6)where now a sums over both the and CMB power spectra terms, and can be precomputed in the fiducial model.
The Planck lensing likelihood consists of bandpower estimates and the covariance Σ_{ij}, a set of binning functions , a set of linearcorrection kernels , and the band values of the linearcorrection kernel applied to the fiducial spectrum . Note that since the lensing estimator is independent of the CMB calibration, for a theory model and map calibration parameter y_{cal}, the theory CMB power spectra that are used in Eq. (C.6)are those matched to the data, i.e. .
When evaluating “lensingonly” likelihoods, where the CMB power spectra are only very weakly indirectly constrained, we fix the used in the linear correction to a bestfit to the full data. When calculating joint lensing and CMB results, the correction depends on the CMB power spectra at each point in model space.
Appendix D: CIB model
The cosmic infrared background (CIB) is a diffuse sky signal which begins to dominate over the CMB at frequencies ν ≳ 300 GHz. It is generated by dust that is heated by UV light from young stars and then reradiates thermally in the infrared. The CIB contains approximately half of the total extragalactic stellar flux, and has excellent redshift overlap with the CMB lensing potential (Song et al. 2003). In Sect. 3.2 we use the CIB as a tracer of the lensing potential to estimate the lensinginduced Bmode signal. This requires a model for the crosscorrelation between the CIB and the lensing potential. We calculate this in the Limber approximation as (Limber 1954) (D.1)where P(k;η) is the (nonlinear) matter power spectrum at comoving wavenumber k and conformal time η. The lensing kernel in flat models is given by (D.2)where a(χ) is the scale factor at conformal time η_{0}−χ and χ_{∗} denotes the last scattering surface at z ≈ 1100. To model the CIB, we use the simple SSED model of Hall et al. (2010), which has a kernel given by (D.3)where b_{c} is an overall normalization and z_{c} = σ_{z} = 2 describe the redshift distribution of the CIB intensity. The redshift z is evaluated at conformal time η_{0}−χ. The spectral energy distribution (SED) of a typical CIB source is described by f_{ν}. It is modelled as a modified blackbody with temperature T = 34 K, and spectral index β = 2 up to a threshold ν′ ≈ 4955 GHz, where it transitions to a powerlaw decay with index α = 2: (D.4)The precise value of ν′ is chosen to match smoothly the slope of f_{ν} on both sides of the transition frequency. In this model, the modified blackbody component of the SED is generally what we observe; even at an observation frequency of 1200 GHz (i.e. 250 μm), the powerlaw transition does not occur until z ≈ 4. Our theory curve is plotted in Fig. D.1, as well as the measured crosscorrelation of the Planck 2015 545 GHz map with the MV lensing potential estimate (over 40% of the sky). In addition we have plotted the bandpowers from the rigorous analysis presented in the Planck 2013 data release (Planck Collaboration XVIII 2014). We have set the normalization b_{c} for the theory curve such that the overall amplitude is consistent with both measurements.
Fig. D.1
Crosscorrelation between the MV lensing potential estimate and the Planck 545 GHz channel (red points). The crosscorrelation uses a Galaxy mask leaving 40% of the sky. The earlier result from Planck Collaboration XVIII (2014) using a TT lensing estimator is shown with green points. The simple CIB model discussed in Appendix D is plotted as the black dashed line. 
Appendix E: Parameter dependence of in ΛCDM models
In this appendix we discuss the dependence of the lensing potential power spectrum at multipoles L ≳ 100 on the parameters of ΛCDM models, providing results that are used in the discussion of the “lensingonly” constraints in Sect. 3.5. Since these constraints adopt a prior on n_{s} that is rather tighter than the resulting posterior widths for Ω_{m}h^{2} and h, we ignore dependencies on n_{s} throughout this appendix. We caution the reader that for joint constraints, or fits to the CMB power spectra alone, fractional variations in n_{s} can be comparable to those in Ω_{m}h^{2} and h, so the dependence of on n_{s} cannot be ignored. The dependence of on the matter density was recently discussed in Pan et al. (2014); we follow their discussion closely.
For multipoles L ≳ 20, the angular power spectrum of the lensing potential can be Limberapproximated as (E.1)in spatiallyflat models. Here, P_{Ψ}(k;η) is the equaltime dimensional power spectrum of the Weyl potential Ψ. Well after baryon decoupling, we have^{15}(E.2)where is the dimensionless power spectrum of the primordial curvature perturbation ℛ, T(k) is the transfer function, and g(a) is the growth function for the Weyl potential at scale factor a normalized to unity at high redshift. In ΛCDM, (E.3)where Ω_{m} is the matter fraction at the present and E(a) ≡ H(a) /H_{0}. For Ω_{m} = 0.3, the current value of the growth function is 0.78 and dlng/dlnΩ_{m} = 0.23. Close to Ω_{m} = 0.3, we can approximate the cosmology dependence of the growth function at redshift zero as g(a = 1) ≈ 0.78(Ω_{m}/ 0.3)^{0.23}. For L> 100, the lensing potential mostly probes early enough times that g(a) ≈ 1 is a good approximation (Pan et al. 2014). However, if we wish to express the amplitude of P_{Ψ}(k) at early times in terms of σ_{8} (at the present), we require g(a = 1).
The transfer function is normalized to unity for k ≪ k_{eq}, where k_{eq} ~ 10^{2} Mpc^{1} the inverse of the comoving Hubble parameter at matterradiation equality. For k ≫ k_{eq} we have T(k) ~ (k/k_{eq})^{2}ln(k/k_{eq}). Keeping the radiation energy density fixed today, k_{eq} ∝ ω_{m}, where ω_{m} ≡ Ω_{m}h^{2}. As well as k_{eq}, the other scales that enter the transfer function are the sound horizon at the drag epoch (when baryons dynamically decouple) r_{drag}, and the diffusion scale k_{D}. The broadband shape is controlled by k_{eq}, while r_{drag} determines the scale below which baryon suppression effects are important (see e.g. Eisenstein & Hu 1998). The nonzero baryon fraction ω_{b}/ω_{m} tends to suppress the transfer function on small scales since (essentially) unclustered baryons impede the growth of the CDM density perturbation up to the time after decoupling when baryon infall into the CDM potential wells is complete. The further effect of baryons is to imprint baryon acoustic oscillations in the transfer function for kr_{drag}>π and k ≲ k_{D}. Baryon acoustic oscillations have little impact on since they are washed out by the lineofsight integration. We therefore ignore them here and, where an explicit form for the transfer function is required, for illustrative purposes we use the “nowiggle” fit of Eisenstein & Hu (1998). For the range of parameters of interest, we can assume that the physical baryon density ω_{b} is fixed, so the transfer function can be thought of as a function of k/k_{eq}, kr_{drag} and ω_{m}.
Ignoring departures of the growth function from unity and approximating as scaleinvariant with amplitude A_{s}, we can write (E.4)where x = χ/χ_{∗} and ℓ_{eq} ≡ k_{eq}χ_{∗} ≈ 140. The A_{s}ℓ_{eq} prefactor has a simple physical interpretation: CMB photons are typically deflected by ℓ_{eq} lenses of size , and each meansquared deflection is proportional to A_{s}, giving a meansquared deflection proportional to A_{s}ℓ_{eq}. Given the kdependencies of the transfer function through k/k_{eq} and kr_{drag}, and its direct parameterdependence on ω_{m}, we see that in ΛCDM, the lensing power spectrum at L ≳ 100 is (mostly) determined by only A_{s}, L/ℓ_{eq}, L/ℓ_{drag}, and ω_{m}. Here, the multipole ℓ_{drag} ≡ χ_{∗}/r_{drag} characterizes the BAO scale at lastscattering. The integral in Eq. (E.4)determines the shape of the lensing power spectrum. Defining (E.5)the dependence on the cosmological parameters (e.g. ℓ_{eq}) in the vicinity of the fiducial model is, at any L, approximately a product of power laws with exponents each evaluated for the fiducial cosmology. Note also that and determine the local powerlaw slope of , with (E.9)On the angular scales where Eq. (E.4)is valid, only modes with k>k_{eq} contribute. For these scales, (k/k_{eq})T^{2}(k) increases with k_{eq} giving (and monotonic increase of with L). Around L = 200, the scales that dominate the S/N of a lensing amplitude measurement from Planck, . This, and A_{s}, dominate the parameter dependence of . The effect of baryon suppression is reduced as ω_{m} increases at fixed ω_{b}, so and also rises monotonically with L. Around L = 200, we find , which gives a small, but nonnegligible, further parameter dependence to . Finally, the dependence on ℓ_{drag} is very small, since (with peak value around L ≈ 100, roughly equal to ℓ_{drag}). Putting these pieces together, we find a dependence of on cosmological parameters scaling approximately as (E.10)for L ≳ 100, where the dependence on ℓ_{drag} is very weak.
We can express the parameter dependence of in terms of the usual ΛCDM parameters. The comoving distance to lastscattering, χ_{∗}, is (E.11)where a_{∗} is the scale factor at lastscattering (whose dependence on cosmology can be ignored here). At fixed radiation energy density today, the dependence on Ω_{m} and h is approximately (E.12)around Ω_{m} = 0.3 and h = 0.7^{16}. This differs from the (Ω_{m}h^{2})^{− 1 / 2} of an Einsteinde Sitter universe due to the latetime effects of Λ. Using k_{eq} ∝ Ω_{m}h^{2}, we have (E.13)At fixed baryon density, the sound horizon at the drag epoch scales like in the vicinity of the fiducial model, so that (E.14)Using these dependencies in Eq. (E.10), we find (E.15)where (E.16)is approximately 1.5 at L = 200. The remaining dependence on Ω_{m} is very weak, with at L = 200. Equation (E.15) gives the main dependence of the lensing power spectrum on the cosmological parameters of ΛCDM models at multipoles L ≳ 100.
Appendix E.1: Normalization by σ_{8}
The dependence of the lensing power spectrum on A_{s} can be eliminated in favour of σ_{8}. We use the Poisson equation to relate the dimensional matter power spectrum at a = 1 to that of the Weyl potential, giving (E.17)It follows that the variance of the density contrast in spheres of radius R is (E.18)where we have assumed that is scaleinvariant in passing to the second line. The window function (E.19)is the Fourier transform of a normalized spherical tophat function of radius R; following convention, we take R = 8h^{1} Mpc.
The integral in Eq. (E.18)is a function of k_{eq}R, k_{eq}r_{drag}, and ω_{m} at fixed baryon density (with the latter two from the transfer function). Evaluating the logarithmic derivative with respect to these parameters at the fiducial values, using the “nowiggle” transfer function from Eisenstein & Hu (1998), we find that the integral scales as , with only a very weak further dependence on k_{eq}r_{drag}, in the vicinity of the fiducial model. Finally, the parameter dependence of is (E.20)This parameter dependence agrees well with direct finitedifferencing of σ_{8} calculations with CAMB.
All Tables
Amplitude fits for a suite of alternative reconstructions of the lensing power spectrum and comparison to the baseline MV lensing power spectrum estimate.
Null tests for a set of lens reconstructions based on maps that should contain no CMB signal, or estimators that should be orthogonal to the lensing signal.
All Figures
Fig. 1
Lens reconstruction noise levels for the TT, TE, EE, EB, and TB estimators applied to the SMICA fullmission CMB map. The noise level for their minimumvariance combination (MV) is also shown. The fiducial ΛCDM theory power spectrum used in our Monte Carlo simulations is plotted as the black solid line. 

In the text 
Fig. 2
Lensing potential estimated from the SMICA fullmission CMB maps using the MV estimator. The power spectrum of this map forms the basis of our lensing likelihood. The estimate has been Wiener filtered following Eq. (5), and bandlimited to 8 ≤ L ≤ 2048. 

In the text 
Fig. 3
Simulation of a Wienerfiltered MV lensing reconstruction (upper) and the input φ realization (lower), filtered in the same way as the MV lensing estimate. The reconstruction and input are clearly correlated, although the reconstruction has considerable additional power due to noise. 

In the text 
Fig. 4
Measurement of the lensing Bmode power spectrum, using crosscorrelation with estimates of the lensing potential as discussed in Sect. 3.2. The theoretical lensing Bmode power spectrum, for the parameters of the fiducial cosmological model of Sect. 2, is plotted as dashed black. Green squares and blue triangles are results using the TT and MV φ reconstructions, respectively, to construct the lensinduced Bmode template, while red circles use the CIB (from the Planck545 GHz channel) to construct a proxy for φ. Lensing Bmode power is detected with the expected scale dependence and amplitude at a significance level of approximately 10σ. 

In the text 
Fig. 5
LensingISW bispectrum on large angular scales. The crossspectrum between the MV lensing potential estimate and the temperature anisotropy is plotted for bins of width ΔL = 15, covering the multipole range L = 8–98. The dashed line shows the predicted crossspectrum in the fiducial model. The lensingISW bispectrum is detected at just over 3σ significance. 

In the text 
Fig. 6
Planck 2015 fullmission MV lensing potential power spectrum measurement, as well as earlier measurements using the Planck 2013 nominalmission temperature data (Planck Collaboration XVII 2014), the South Pole Telescope (SPT, van Engelen et al. 2012), and the Atacama Cosmology Telescope (ACT, Das et al. 2014). The fiducial ΛCDM theory power spectrum based on the parameters given in Sect. 2 is plotted as the black solid line. 

In the text 
Fig. 7
Parameter constraints from CMB lensing alone in the ΛCDM model (samples, colourcoded by the value of the Hubble constant) using the minimal priors described in the text; grey bands give the corresponding 1σ and 2σ constraints using the approximate fit of Eq. (13). Solid coloured contours show 68% and 95% constraints when additional information is included: BAO data from SDSS and 6DF (Anderson et al. 2014; Ross et al. 2015; Beutler et al. 2011; blue), and the same but fixing the CMB acousticscale parameter θ_{MC} to a CMB power spectrum fit (red; θ_{MC} = 1.0408). Solid black contours show the constraint from the Planck CMB power spectra (Planck temperature+lowℓ polarization). 

In the text 
Fig. 8
Lensing potential power spectra in the base ΛCDM model drawn from the posterior of the lensingonly likelihood (left) and the Planck TT+lowP likelihood (right), coloured by the value of the dark matter density Ω_{c}h^{2}. For fixed CMB power spectra (i.e. the righthand plot) the lensing power spectrum shape is nearly fixed, but the amplitude increases as the matter density increases across the acousticscale degeneracy (the accompanying reduction in A_{s} is subdominant). With lensing reconstruction data, as Ω_{c}h^{2} varies the lensing spectrum changes shape, with the amplitude around the bestmeasured L ≈ 150, remaining pinned by the accuracy of the measurement there. A combination of the lensing and Planck TT+lowP data measures the matter density significantly better, with the higher values in the righthand plot being excluded. Note that the colour scales on the plots are different. 

In the text 
Fig. 9
Constraints on the rms lensing deflection angle ⟨ d^{2} ⟩ ^{1 / 2} from Planck TT+lowP CMB power spectra in the base ΛCDM model (samples, colour coded by the value of the Hubble constant), showing that the inferred lens deflection amplitude is strongly correlated with the parameter combination . The solid black contour is the constraint from CMB lensing only, which prefers lower values of the lensing amplitude, giving the joint constraint shown by the red contours. Since the lensing reconstruction mainly constrains scales smaller than the peak of the deflection angle power spectrum, there is an additional degeneracy that makes ⟨ d^{2} ⟩ ^{1 / 2} slightly anticorrelated with the measurement of lensing power on smaller scales, which is approximately proportional to (the square of) 

In the text 
Fig. 10
Constraints on the reionization (midpoint) redshift z_{re} and σ_{8} from a combination of Planck CMB power spectra and the lensing reconstruction, excluding lowℓ polarization information. The grey band denotes the approximate z_{re}< 6.5 region excluded by observations of the spectra of highredshift quasars (Fan et al. 2006). The lensing data significantly shrink the allowed region of parameter space, preferring lower values of the reionization redshift, in good agreement with the measurement from Planck lowℓ polarization. 

In the text 
Fig. 11
Constraints on the curvature and rms lensing deflection angle from Planck TT+lowP CMB power spectra (samples, colour coded by the value of the Hubble constant), showing that the closeduniverse models favoured at the 2σ level have high lensing power that is inconsistent with the lensing reconstruction result. The combined lensing result (black contours) is much tighter, and provides additional information on top of the constraint when adding BAO data (red contours); both joint constraints, and the combined filled contours, are consistent with a flat universe. 

In the text 
Fig. 12
Summary of lensing power spectrum consistency tests. Upper panel: comparison of the lensing power spectra for the baseline MV reconstruction and several alternative estimators with different data/analysis choices discussed in the text. Lower panels: detailed plots of the difference between each alternative reconstruction and the MV power spectrum, in units of the MV error bars (σ). The ± 1σ band is indicated in grey. Error bars on individual data points give the standard deviation of the expected difference from the MV reconstruction for each case, estimated using Monte Carlo simulations. 

In the text 
Fig. 13
Power spectrum null tests discussed in Sect. 4.6. Curl null tests are denoted by ψ. The individual null test power spectra have been scaled by the factors indicated in each panel for visual purposes. The fiducial (gradientmode) lensing power spectrum is shown as the dashed line in each panel. 

In the text 
Fig. 14
Grid of lensing potential power spectra for the 15 possible auto and crossspectra of the five quadratic lensing estimators obtained from the SMICA foregroundcleaned maps. The measurements are arranged in order of their total S/N for a measurement of the lensing amplitude. The MV power spectrum estimate, which combines the individual crossspectra and is the basis for our fiducial lensing likelihood, is plotted in the topleft panel. 

In the text 
Fig. 15
Upper: power spectrum of the lowL meanfield (measured from Monte Carlo simulations) for the TT lensing estimator as well as several biashardened estimators. The meanfield power spectrum of the TE lensing estimator is plotted for comparison, and the fiducial lensing potential power spectrum is shown in dashed black. To avoid visual clutter we have not shown the sourcehardened estimator TT−S; its spectrum is midway between the noisehardened (TT−N) and maskhardened (TT−M) cases. Lower: power spectra of the differences between the TT lensing estimate and the biashardened reconstructions or the TE reconstruction (with the same colour scheme as in the top panel). The expected power spectra of the differences, estimated from simulations, are plotted as black dotted lines. 

In the text 
Fig. A.1
Lens reconstruction bias terms for the MV lensing estimator applied to the Planck 2015 SMICA maps. Dashed lines indicate regions where the bias term is negative. The fiducial ΛCDM theory power spectrum is plotted as the black solid line. 

In the text 
Fig. D.1
Crosscorrelation between the MV lensing potential estimate and the Planck 545 GHz channel (red points). The crosscorrelation uses a Galaxy mask leaving 40% of the sky. The earlier result from Planck Collaboration XVIII (2014) using a TT lensing estimator is shown with green points. The simple CIB model discussed in Appendix D is plotted as the black dashed line. 

In the text 
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