Plik Iso Z Rar
Dannie Heinzen
We also provide best-fit LCDM CMB power spectra from the baseline Planck TT,TE,EE+lowE+lensing. The spectra must be divided by the best-fit Planck map-based calibration parameter squared, calPlanck**2, to be compared to the coadded CMB spectra. The best-fit calPlanck value can be found in the file "COM_PowerSpect_CMB-base-plikHM_TTTEEE-lowl-lowE-lensing-minimum_R3.01.txt".
The 2018 baseline likelihood release consists of a code package and a single data package. Five extended data packages are also available, enabling exploration of alternatives to the baseline results.The code compiles to a library, allowing for the computation of log likelihoods for a given data set. Each data package contains multiple data sets. A data set permits the computation of a single likelihood among:
Important update May 2021: A bug was reported in the code of the CMB marginalized version of the lensing likelihood. The bug caused a first order correction in the computation of the likelihood to be miscomputed and as a consequence biased cosmological results. The bug was only present in the distributed R3.00 and R3.01 of the likelihood code. It is not present in the other implementation of the likelihood (such as cosmoMC or cobaya version). The bug was not present in the versions of the code used to produce the Legacy lensing paper (Planck-2020-A8[5]) or the released public chains. The issue is corrected with the package R3.10. The correction works with the previously distributed likelihood data files.
All of the likelihood files have at least one nuisance parameter, allowing users to investigate the Planck absolute calibration. We recommend that this parameter is explored over the Gaussian prior 1.00000.0025.
The file contains the 100-GHz, 143-GHz, and 217-GHz binned half-mission TT cross-spectra. Only the 100100, 143143, 143217, and 217217 spectra are actually used. Masks and multipole ranges for each spectrum are different and described in Planck-2020-A5[2].Masks are based on the CMB-cleaned 353-GHz map for the dust component, on the Planck catalogues for the point source part, and on the CO maps.The file also containstemplates for the residual foreground contamination of each spectrum. The templates are neededto allow for computation of the joint CMB and nuisance likelihood. The covariance matrix is computed using an analytical approximation, and corrected for the effect of point sources through Monte Carlo estimates.The covariance matrix is computed for a fiducial cosmology and nuisance model that has been obtained using a first, less optimal estimate of the parameters.The beam matrix computed for the specific masks and data cuts are applied to the 2015 TT ΛCDM best-fit spectra to predict leakage templates. Subpixel effect are predicted for the specific masks and data cuts.
The file contains the 100-GHz, 143-GHz, and 217-GHz binned half-mission T and E cross-spectra. In temperature, only the 100100, 143143, 143217, and 217217 spectra are actually used, whilein TE and EE all of them are used. Masks and multipole ranges for each spectrum are different and described in Planck-2020-A5[2].Masks are based on CMB-cleaned 353-GHz map for the dust component, on the Planck catalogues for the point-source part, and on the CO maps.The file also containstemplates for the residual foreground contamination of each spectrum. The templates are neededto allow the computation of the joint CMB and nuisance likelihood. The covariance matrix is computed using an analytical approximation, and corrected for the effect of point sources through Monte Carlo estimates.The covariance matrix is computed for a fiducial cosmology and nuisance model that has been obtained using a first, less optimal estimate of the parameters.The beam matrix computed for the specific masks and data cuts are applied to the 2015 TT ΛCDM best-fit spectra to predict leakage templates. Subpixel effect are predicted for the specific masks and data cuts.The 300 end-to-end HFI simulations from the FFP10 suite are used to estimate correlated-noise residuals in the EE auto-spectra at 100, 143, and 217 GHz.
The Plik likelihood files described above have been explored using a Bayesian algorithm described in Planck-2020-A5[2]. The joint posterior of the CMB TT, TE, and EE spectra, marginalized over the nuisance parameters has been extracted from this analysis to build a high-ℓ likelihood approximation for the CMB spectrum only. The nuisance parameters have been marginalized in the priors described above.
Production processThe SMICA T maps are filtered and correlated to reconstruct an optimal lensing-reconstruction map. Biases are corrected using Monte Carlo simulationsfor the dominant "mean field" and "N0" contributions, while the "N1" bias is computed using the CMB-based best-fit model. The power spectrum of this map is measured out of a large mask, and binned.The covariance matrix of this binned spectrum is evaluated using numerical simulations.The model-dependent corrections to the normalization and N1 biases are linearized and precomputed.
The covariance of the CMB dependent likelihood is enlarged, and the theory spectrum is shifted to account for the marginalization over the CMB spectrum, using a Gaussian approximation. The "plik_lite" likelihood is used to provide the CMB spectra and covariances.
The 2015 baseline likelihood release consists of a code package and a single data package. Four extended data packages are also available enabling exploration of alternatives to the baseline results.The code compiles to a library allowing for the computation of log likelihoods for a given data set. Each data package contains multiple data sets. A data set permits the computation of a single likelihood among:
Only the baseline data package, COM_Likelihood_Data-baseline_R2.00.tar.gz, will be fully described on this page. It contains six data sets, which are enough to compute all of the baseline Planck results that are discussed in Planck-2015-A12[11]. In particular it allows for the computation of the CMB and lensing likelihood from either the Temperature data only, or the Temperature + Polarization combination.
The other data packages contain data sets that extend the baseline results, enabling the exploration of different regimes, which are discussed in Planck-2015-A11[7] and Planck-2015-A15[12]. Their full description is contained in the documentation included in each of the extended data packages.
DescriptionThe library consists of code written in C and Fortran 90. It can be called from both ofthose languages. Optionally, a python wrapper can be built as well. Scripts tosimplify the linking of the library with other codes are part of the package, aswell as some example codes that can be used to test the correct installation ofthe code and the integrity of the data packages. Optionally, a script is also available, allowingthe user to modify the multipole range of the TT likelihoods and reproduce the hybridizationtest performed in the paper.A description of the tool, the API of the library, as well as different installation procedure are detailed in the readme.md file in the code package.
All of the likelihood files have at least one nuisance parameter, allowing users to investigate the Planck absolute calibration. We recommend that his parameter is explored in the Gaussian prior 1.00000.0025.
Production processThe file contains the 100-GHz, 143-GHz, and 217-GHz binned half-mission TT cross-spectra. Only the 100100, 143143, 143217, and 217217 spectra are actually used. Masks and multipole ranges for each spectrum are different and described in Planck-2015-A11[7].Masks are based on the CMB-cleaned 353-GHz map for the dust component, on the Planck catalogues for the point source part, and on the CO maps.The file also containstemplates for the residual foreground contamination of each spectrum. The templates are neededto allow for computation of the joint CMB and nuisance likelihood. The covariance matrix is computed using an analytical approximation, and corrected for the effect of point sources through Monte Carlo estimates.The covariance matrix is computed for a fiducial cosmology and nuisance model that has been obtained using a first, less optimal estimate of the parameters.
Production processThe file contains the 100-GHz, 143-GHz, and 217-GHz binned half-mission T and E cross-spectra. In temperature, only the 100100, 143143, 143217, and 217217 spectra are actually used, whilein TE and EE all of them are used. Masks and multipole ranges for each spectrum are different and described in Planck-2015-A11[7].Masks are based on CMB-cleaned 353-GHz map for the dust component, on the Planck catalogues for the point source part, and on the CO maps.The file also containstemplates for the residual foreground contamination of each spectrum. The templates are neededto allow the computation of the joint CMB and nuisance likelihood. The covariance matrix is computed using an analytical approximation, and corrected for the effect of point sources through Monte Carlo estimates.The covariance matrix is computed for a fiducial cosmology and nuisance model that has been obtained using a first, less optimal estimate of the parameters.
Production processThe SMICA T maps are filtered and correlated to reconstruct an optimal lensing reconstruction map. Biases are corrected using Monte Carlo simulationsfor the dominant "mean field" and "N0" contribution, while the "N1" bias is computed using the CMB-based best-fit model. The power spectrum of this map is measured out of a large mask, and binned.The covariance matrix of this binned spectrum is evaluated using numerical simulations.The model-dependent corrections to the normalization and N1 biases are linearized and precomputed.
Production processThe SMICA T and P maps are filtered and correlated to reconstruct an optimal lensing reconstruction map. Biases are corrected using Monte Carlo simulationsfor the dominant mean field and N0 contribution, while the N1 bias is computed using the CMB-based best-fit model. The power spectrum of this map is measured outside a large mask, and binned.The covariance matrix of this binned spectrum is evaluated using numerical simulations.The model-dependent corrections to the normalization and N1 biases are linearized and precomputed.
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