Compressed Python likelihood for large scale temperature and polarization from Planck

Heather Prince, Jo Dunkley

Research output: Contribution to journalArticlepeer-review

Abstract

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 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 ΛCDM 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.

Original languageEnglish (US)
Article number023518
JournalPhysical Review D
Volume105
Issue number2
DOIs
StatePublished - Jan 15 2022

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy (miscellaneous)

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