Compressed Python likelihood for large scale temperature and polarization from Planck

Abstract

We present Planck-low-py, a binned low-$\ensuremath{\ell}$ 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 $\ensuremath{\ell}<30$ temperature and polarization angular power spectra information from Planck into two log-normal bins in temperature and seven 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. Using Planck-low-py, we show that cosmological constraints on $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model parameters, and on parameters including running and isocurvature, agree with those derived with the full Commander and SimAll likelihoods from the Planck legacy release.