We present Planck-low-py, a binned low-ℓ temperature and E-mode polarization likelihood, as an option to facilitate ease of use of the Planck 2018 large-scale data in joint-probe analysis and forecasting. It is written in Python and compresses the ℓ<30 temperature and polarization angular power spectra information from Planck into two log-normal bins in temperature and three in polarization. These angular scales constrain the optical depth to reionization and provide a lever arm to constrain the tilt of the primordial power spectrum. We show that cosmological constraints on ΛCDM model parameters using Planck-low-py are consistent with those derived with the full Commander and SimAll likelihoods from the Planck legacy release.
Cite
@article{arxiv.2104.05715,
title = {A Python compressed low-$\ell$ Planck likelihood for temperature and polarization},
author = {Heather Prince and Jo Dunkley},
journal= {arXiv preprint arXiv:2104.05715},
year = {2021}
}
Comments
7 pages, 6 figures, code available at https://github.com/heatherprince/planck-low-py