planck-2020-lollipop 4.1.1

A cobaya low-ell likelihood polarized for planck

LoLLiPoP: Low-L Likelihood Polarized for Planck

Lollipop is a Planck low-l polarization likelihood based on cross-power-spectra for which the bias is zero when the noise is uncorrelated between maps. It uses the approximation presented in Hamimeche & Lewis (2008), modified as described in Mangilli et al. (2015) to apply to cross-power spectra. This version is based on the Planck PR4 data. Cross-spectra are computed on the CMB maps from Commander component separation applied on each detset-split Planck frequency maps.

It was previously applied and described in

• Planck Collaboration Int. XLVII (2016) for investigating the reionization history,
• Tristram et al. (2021) Planck constraints on the tensor-to-scalar ratio
• Tristram et al. (2022) Improved limits on the tensor-to-scalar ratio using BICEP and Planck data

It is interfaced with the cobaya MCMC sampler.

Requirements

• Python >= 3.5
• numpy
• astropy

Install

The easiest way to install the Lollipop likelihood is via pip

pip install planck-2020-lollipop [--user]

If you plan to dig into the code, it is better to clone this repository to some location

git clone https://github.com/planck-npipe/lollipop.git /where/to/clone

Then you can install the Lollipop likelihoods and its dependencies via

pip install -e /where/to/clone

The -e option allow the developer to make changes within the Lollipop directory without having to reinstall at every changes. If you plan to just use the likelihood and do not develop it, you can remove the -e option.

Installing Lollipop likelihood data

You should use the cobaya-install binary to automatically download the data needed by the lollipop.lowlE or lollipop.lowlB or lollipop.lowlEB likelihoods

cobaya-install /where/to/clone/examples/test_lollipop.yaml -p /where/to/put/packages

Data and code such as CAMB will be downloaded and installed within the /where/to/put/packages directory. For more details, you can have a look to cobaya documentation.

Likelihood versions

• lowlE
• lowlB
• lowlEB