ACT DR6 Lensing Likelihood

This 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:2304.05203
• Qu, Sherwin, Madhavacheril, Han, Crowley et al ACT Collaboration (2023), arxiv:2304.05202

In 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)039

Chains

A pre-release version of the chains from Madhavacheril et al are available here. Please make sure to read the README file.

Step 1: Install

Option 1: Install from PyPI

You can install the likelihood directly with:

pip install act_dr6_lenslike

Option 2: Install from Github

If 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 . --user

Tests can be run using

python setup.py test

Step 2: download and unpack data

This can be performed automatically with the supplied get-act-data.sh script. Otherwise follow the steps below.

Download the likelihood data tarball for ACT DR6 lensing from NASA's LAMBDA archive.

Extract the tarball into the act_dr6_lenslike/data/ directory in the cloned repository such the directory v1.2 is directly inside it. Only then should you proceed with the next steps.

Step 3: use in Python codes

Generic Python likelihood

import 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 likelihood

Your Cobaya YAML or dictionary should have an entry of this form

likelihood:
    act_dr6_lenslike.ACTDR6LensLike:
        lens_only: False
        stop_at_error: True
        lmax: 4000
        variant: act_baseline

No other parameters need to be set. (e.g. do not manually set like_corrections or no_like_corrections here). An example is provided in ACTDR6LensLike-example.yaml. If, however, you are combining with the ACT DR4 CMB 2-point power spectrum likelihood, you should also set no_actlike_cmb_corrections: True (in addition to lens_only: True as described below). You do not need to do this if you are combining with Planck CMB 2-point power spectrum likelihoods.

Important parameters

• variant should be
  • act_baseline for the ACT-only lensing power spectrum with the baseline multipole range
  • act_extended for the ACT-only lensing power spectrum with the extended multipole range (L<1250)
  • actplanck_baseline for the ACT+Planck lensing power spectrum with the baseline multipole range
  • actplanck_extended for the ACT+Planck lensing power spectrum with the extended multipole range (L<1250)
• lens_only should be
  • False when combining with any primary CMB measurement
  • True when not combining with any primary CMB measurement

Recommended theory accuracy

For CAMB calls, we recommend the following (or higher accuracy):

• lmax: 4000
• lens_margin:1250
• lens_potential_accuracy: 4
• AccuracyBoost:1
• lSampleBoost:1
• lAccuracyBoost:1
• halofit_version:mead2016