A Python compressed low-$\ell$ Planck likelihood for temperature and polarization

TL;DR

Planck-low-py is a Python-based, compressed low-$ell$ likelihood tool for Planck 2018 data, simplifying analysis of large-scale temperature and polarization spectra while maintaining consistent cosmological constraints.

Contribution

It introduces a new Python implementation that compresses low-$ell$ Planck data into fewer bins, facilitating easier joint-probe analysis and forecasting.

Findings

Cosmological constraints are consistent with full likelihoods.

Reduces data complexity for large-scale analysis.

Enables easier integration into multi-probe studies.

Abstract

We present Planck-low-py, a binned low-$\ell$ 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 $\ell<30$ 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 $\Lambda$CDM model parameters using Planck-low-py are consistent with those derived with the full Commander and SimAll likelihoods from the Planck legacy release.