Package: act_dr6_lenslike
ACT DR6 Lensing LikelihoodThis repository contains likelihood software for the ACT DR6 CMB lensing analysis. If you use this software and/or the associated data, please cite both of the following papers:Madhavacheril, Qu, Sherwin, MacCrann, Li et al ACT Collaboration (2023), arxiv:2304.05203Qu, Sherwin, Madhavacheril, Han, Crowley et al ACT Collaboration (2023), arxiv:2304.05202In addition, if you use the ACT+Planck lensing combination variant from the likelihood, please also cite:Carron, Mirmelstein, Lewis (2022), arxiv:2206.07773, JCAP09(2022)039ChainsA pre-release version of the chains from Madhavacheril et al are availablehere. Please make sure to read the README file.Step 1: InstallOption 1: Install from PyPIYou can install the likelihood directly with:pip install act_dr6_lenslikeOption 2: Install from GithubIf you wish to be able to make changes to the likelihood for development, first clone this repository. Then install with symbolic links:pip install -e . --userTests can be run usingpython setup.py testStep 2: download and unpack dataThis can be performed automatically with the suppliedget-act-data.shscript. Otherwise follow the steps below.Download the likelihood data tarball for ACT DR6 lensing fromNASA's LAMBDA archive.Extract the tarball into theact_dr6_lenslike/data/directory in the cloned repository such the directoryv1.2is directly inside it. Only then should you proceed with the next steps.Step 3: use in Python codesGeneric Python likelihoodimport act_dr6_lenslike as alike

variant = 'act_baseline'
lens_only = False # use True if not combining with any primary CMB data
like_corrections = True # should be False if lens_only is True

# Do this once
data_dict = alike.load_data(variant,lens_only=lens_only,like_corrections=like_corrections)
# This dict will now have entries like `data_binned_clkk` (binned data vector), `cov`
# (covariance matrix) and `binmat_act` (binning matrix to be applied to a theory
# curve starting at ell=0).

# Get cl_kk, cl_tt, cl_ee, cl_te, cl_bb predictions from your Boltzmann code.
# These are the CMB lensing convergence spectra (not potential or deflection)
# as well as the TT, EE, TE, BB CMB spectra (needed for likelihood corrections)
# in uK^2 units. All of these are C_ell (not D_ell), no ell or 2pi factors.
# Then call
lnlike=alike.generic_lnlike(data_dict,ell_kk,cl_kk,ell_cmb,cl_tt,cl_ee,cl_te,cl_bb)Cobaya likelihoodYour Cobaya YAML or dictionary should have an entry of this formlikelihood:
    act_dr6_lenslike.ACTDR6LensLike:
        lens_only: False
        stop_at_error: True
        lmax: 4000
        variant: act_baselineNo other parameters need to be set. (e.g. do not manually setlike_correctionsorno_like_correctionshere).
An example is provided inACTDR6LensLike-example.yaml. If, however, you are combining with
the ACT DR4 CMB 2-point power spectrum likelihood, you should also setno_actlike_cmb_corrections: True(in addition tolens_only: Trueas described below). You do not need to do this if you are combining
with Planck CMB 2-point power spectrum likelihoods.Important parametersvariantshould beact_baselinefor the ACT-only lensing power spectrum with the baseline multipole rangeact_extendedfor the ACT-only lensing power spectrum with the extended multipole range (L<1250)actplanck_baselinefor the ACT+Planck lensing power spectrum with the baseline multipole rangeactplanck_extendedfor the ACT+Planck lensing power spectrum with the extended multipole range (L<1250)lens_onlyshould beFalse when combining with any primary CMB measurementTrue when not combining with any primary CMB measurementRecommended theory accuracyFor CAMB calls, we recommend the following (or higher accuracy):lmax: 4000lens_margin:1250lens_potential_accuracy: 4AccuracyBoost:1lSampleBoost:1lAccuracyBoost:1halofit_version:mead2016