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Data1. CMBThe determinations of the CMB temperature and polarization across the sky act as powerful constraints of the cosmological parameters. To achieve accurate cosmology measurements, a robust likelihood function must be developed that considers a variety of factors: the correlation of the temperature and polarization fields, the correlation of angular power spectrum estimators at different multipoles (ℓ) due to partial sky coverage, unresolved astrophysical foregrounds, and instrumental factors. The likelihood function is key as it can serve as the prime CMB analysis tool. CAMEL uses a variety of low-ℓ and high-ℓ likelihood functions. These functions are presented below. 1.1 WMAP1.2 Planck Legacy ArchiveThe Planck Legacy Archive (PLA) gather all the information associated with the released Planck likelihood function. CAMEL is fully compatible with the Planck likelihood format (clik). 1.3 Planck HillipopThe Hillipop likelihood function is one of the three Planck high-ℓ (ℓ>50) likelihoods (see Planck 2015 results. XI.). It has been developed within the Planck collaboration at Laboratoire de l'Accélérateur Linéaire (LAL). It is based on a Gaussian approximation to compare the temperature and polarization cross-power spectra estimated ℓ-by-ℓ with models in the range ℓ = [50,2500]. The data consist of two sets of half-mission (I,Q,U Stokes parameters) maps at 100, 143 and 217 GHz. Frequency dependent apodized masks are applied to these maps in order to limit contamination from emission of diffuse Galactic dust, Galactic CO lines, nearby galaxies and extragalactic point sources. We ultimately retain 72, 62 and 48% of the sky at 100, 143 and 217 GHz, respectively. The same set of masks are used in temperature and polarization. Mask-deconvolved and beam-corrected cross-half-mission angular power spectra (first order aℓm correlation) are computed using Xpol (a polarized version of the Xspect code Tristram+ 2005, MNRAS 358 833). From the six above maps, three sets of six band-averaged angular cross-power spectra are derived for TT, EE and TE. We select the multipole ranges to limit contamination from diffuse Galactic dust emission at low-ℓ and noise at high-ℓ in each power spectrum. We derive a likelihood for each data information (hlpT, hlpE, and hlpX) and combining all (hlpTXE). As the angular power spectra are highly correlated, we developed a semi-analytical estimation of the covariance matrix, which encompassed the anticipated (ℓ-by-ℓ) correlations. The method employs data estimates only, with no reliance upon Monte-Carlo simulations. We worked intensively to achieve an accurate calculation of the 4-aℓm correlations. We tested the approximations used in the calculation with Monte-Carlo simulations and we found that a precision better than a few percents was achieved. In addition to the CMB component, our model considers foreground residuals and calibration differences among maps. We use different models for the foreground’s angular power spectra in temperature and polarization. Our model includes contributions from CIB, Galactic dust, thermal and kinetic SZ effects, Poisson point sources, and cross-correlation between infrared galaxies and the temperature SZ effect. For model construction, we employed the Planck measurements of the millimeter sky (the 9 frequency band data from Planck permits a highly-accurate estimate of the angular power spectra and SED of the different astrophysical emission). The Hillipop temperature likelihood function encloses 6+ cosmological parameters (6 from the base ΛCDM + eventual extensions), 6 parameters associated to calibration (1 absolute calibration and 5 calibrations at the map level relative to 143 GHz first half mission map) and 9 parameters corresponding to the amplitude of the angular power spectra of the astrophysical foregrounds considered in temperature (radio point-sources, IR point-sources, tSZ, kSZ, CIB, Galactic dust and CIBxtSZ). There is a maximum of 21+ free parameters in the Hillipop full likelihood function (19+ for hlpT). HiLLiPOP=HiLLiPOP/DX11dHM_superExt_CO_TT.lik par A_planck nui 1 0.001 0.9 1.1 par c0 nui 0. 0.001 -0.05 0.05 par c1 nui 0. 0.001 -0.05 0.05 fix c2 nui 0. 0.001 -0.05 0.05 par c3 nui 0. 0.001 -0.05 0.05 par c4 nui 0.004 0.001 -0.05 0.05 par c5 nui 0.004 0.001 -0.05 0.05 par Aradio nui 1. 0.1 0.0 10 par Adusty nui 1. 0.1 0.0 10 par Asz nui 1 0.1 0.0 10 par Acib nui 1. 0.1 0.0 10 par AdustTT nui 1. 0.1 0.0 2 par Aksz nui 0.00 1.0 0.0 10 par Aszxcib nui 0.00 1.0 0.0 10 We recommend using the additional constraints as priors extracted from Planck data: gauss1 AdustTT 1 0.2 gauss1 Acib 1 0.2 gauss1 Asz 1 0.2 gauss1 Aradio 1 0.2 gauss1 Adusty 1 0.2 gauss1 A_planck 1 0.0025 gauss1 c0 0 2e-3 gauss1 c1 0 2e-3 gauss1 c3 0 2e-3 gauss1 c4 0.0025 2e-3 gauss1 c5 0.0025 2e-3 Note for polarisation, you should add two parameters for dust contamination and their associated prior: par AdustPP nui 0.00 0.1 0.0 2 par AdustTP nui 0.00 0.1 0.0 2 gauss1 AdustPP 1 0.2 gauss1 AdustTP 1 0.2 1.4 High angular resolution CMB data setsThe high angular resolution CMB data sets (or VHL for very-high-ℓ likelihoods) include measurements from the ground-based Atacama Cosmology Telescope (ACT) and South Pole Telescope (SPT). As explained in Planck 2015 results. XI, the high-ℓ likelihoods includes ACT power spectra at 148 and 218GHz [ Das et al. 2014, JCAP 4 14 ], with a revised binning (described in Calabrese et al. 2013, PRD 87 103012) and final beam estimates [ Hasselfield et al. 2013, ApJS, 209, 17 ], together with SPT measurements in the range 2000<ℓ<13,000 from the 2540deg2 SPT-SZ survey at 95, 150, and 220 GHz [ George et al. 2015, ApJ 799 177 ].
ACT includes two different datasets from two different surveys: To assess the consistency between these data sets and Planck, we use the same templates for cosmic infrared background (CIB) fluctuations, the thermal SZ (tSZ) effect, kSZ effect, and CIBxtSZ components. As the choice for templates are different in Hillipop and plik (PLA likelihood), and as these templates have different formats in each case, we have two version of the parameter files ; those for plik are named '*_plik'. Additional nuisance parameters for ACT and SPT are described below. ACT
ACT_equat=HighEll/ACT_equat.lik par ACT_equat_148_cal nui 0.9991 0.002 0.9 1.1 par ACT_equat_220_cal nui 1.013 0.002 0.9 1.1 par ACT_equat_ADust nui 1.719 0.01 0.05 10 par ACT_equat_Aps_148x148 nui 7.159 0.01 0.1 50 par ACT_equat_Aps_148x220 nui 20 0.01 0.1 50 par ACT_equat_Aps_220x220 nui 60 0.01 10 150 ACT_south=HighEll/ACT_south.lik par ACT_south_148_cal nui 1.007 0.002 0.9 1.1 par ACT_south_220_cal nui 1.032 0.002 0.9 1.1 par ACT_south_ADust nui 1.3 0.01 0.05 10 par ACT_south_Aps_148x148 nui 9 0.01 0.1 50 par ACT_south_Aps_148x220 nui 16.29 0.01 0.1 50 par ACT_south_Aps_220x220 nui 60 0.01 10 150 SPTSPT likelihoods are coded for each survey corresponding to Story et al. 2012 (SPT_low), Reichard et al. 2012 (SPT_high) and George et al. 2014 (SPT_high_2014).
SPT_Low=HighEll/SPT_low.lik par SPT_low_Aps nui 20.32 0.01 1. 60 par SPT_low_cal nui 1.00 0.01 0 2 SPT_High=HighEll/SPT_high_2014.lik or SPT_High=HighEll/SPT_high.lik par SPT_high_95_cal nui 0.9961 0.002 0.9 1.1 par SPT_high_150_cal nui 1.002 0.002 0.9 1.1 par SPT_high_220_cal nui 1.015 0.002 0.9 1.1 par SPT_high_Aps_95x95 nui 7.425 0.01 0.1 50 par SPT_high_Aps_95x150 nui 5.147 0.01 0.1 50 par SPT_high_Aps_95x220 nui 8.8 0.01 0.1 50 par SPT_high_Aps_150x150 nui 6.649 0.01 0.2 50 par SPT_high_Aps_150x220 nui 14.15 0.01 1.5 50 par SPT_high_Aps_220x220 nui 36.07 0.01 3 200 fix SPT_ADust nui 1 We recommend to use the following priors on calibration for SPT_high_2014: gauss1 SPT_high_95_cal 1.01 .01 gauss1 SPT_high_150_cal 1.01 .01 gauss1 SPT_high_220_cal 1.01 .02 Data used for the two SPT likelihood are more or less the same (i.e. the 150 GHz channel). In order to combine these two likelihoods together one may want to avoid too much of double counting. Two things are possible :
SPT_low spectrum the wanted ell range (there are 47 bins from 650 to 3000).
In both cases, the required data are selected and its inverted covariance matrix is recomputed after eliminating the necessary rows and columns. 2. BAOBAO information is added using several 2.1 1D measurementsConstraints on Dv/rs (at one or several redshifts) are managed by a single parameter file compiling all measurements. # input data for BAO # in/fix | name | num of data | z | value | error | out of diag| elements (eventually) in 6dF 1 0.106 457 27 fix SDSS(R) 1 0.35 8.88 0.17 fix BOSS(DR9) 1 0.57 13.67 0.22 fix BOSS-CMASS(DR11) 1 0.57 2056 20 fix BOSS-LOWZ(DR11) 1 0.32 1264 25 fix WiggleZ 3 0.44 0.0916 0.0071 0.60 0.0726 0.0034 0.73 0.0592 0.0032 0.003068 0.0 0.00222005 fix SDSS 2 0.20 0.1905 0.0061 0.35 0.1097 0.0036 0.002723 You can activate or remove data line-by-line or with in/fix parameter. There is no free parameter associate to this likelihood, so you just need to add a line to your parameter file: BAOFile=BAO/BAO1D_dr12.lik 2.2 BAO1D: Generic DVIn order to help in studying forecasts, we introduced a very simple chi2 based on a list of Gaussian Dv measurements (at some zeff and r_drag(fid) values).
You can add measurements as in the file #z_eff rs(fid) Dv_meas error_DV 0.57 149.28 2056 20 and add to your parameter file: BAO_GenericDv=BAO/Generic_example.lik 2.3 2D measurements2D constraints in the (H(z),DA(z)) plane are implemented as correlated Gaussians. We use one file per dataset, in contrast with the 1D case above. We include 3 different 2D files:
Our baseline uses Anderson et al. 2011. The results from Lyα are a bit outdated (and not in perfect agreement with each other). Other similar measurements at other redshifts can be added in your par file. There is no free parameter for this likelihood: BAO2DFile=BAO/Boss_anderson_dr11.lik 2.4 BAO3D: constraints on DM, H(z) and f.σ8 as in DR12DR12 results from BOSS consist in simultaneous constraints on (DM(z), H(z), f.σ8(z)) for 3 redshift bins. This is implemented in the BAO3D class as correlated Gaussians, using the covariance matrix given in [Alam et al., 2016] (arXiv:1607.03155). Add in you param file : BAO_3DFile = BAO/boss_dr12.lik do_mPk = true class z_max_pk 1. Note the two mandatory 3. SNIaThe SNIa part consists of the JLA data and likelihood made available by M. Betoule which are described here. More details are given in Betoule et al. 2014, A&A 568 A22. In addition to the usual cosmological parameters, this likelihood uses 4 nuisance parameters:
which have to be accounted for in the global likelihood parameters as follows: JLA_SNIA_File=JLA/JLA.lik par Mabs nui -19.04 .03 -19.25 -18.85 par alpha nui 0.141 .01 0.10 .18 par beta nui 3.101 0.1 2.5 3.7 par DeltaM nui -.076 0.03 -.13 -0.01 |