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arXiv · 2104.05715

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

Heather Prince·Jo Dunkley
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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.

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Heather Prince, Jo Dunkley. 2021-04-12. A Python compressed low-$\ell$ Planck likelihood for temperature and polarization. https://arxiv.org/abs/2104.05715

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