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Compressed Python likelihood for large scale temperature and polarization from Planck

Author(s): Prince, Heather; Dunkley, Jo

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Abstract: We present Planck-low-py, a binned low-` 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 ` < 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.
Publication Date: 13-Jan-2022
Electronic Publication Date: 13-Jan-2022
Citation: Prince, Heather, Dunkley, Jo. (Compressed Python likelihood for large scale temperature and polarization from <i>Planck</i>. Physical Review D, 105 (2), 10.1103/physrevd.105.023518
DOI: doi:10.1103/physrevd.105.023518
ISSN: 2470-0010
EISSN: 2470-0029
Language: en
Type of Material: Journal Article
Journal/Proceeding Title: Physical Review D
Version: Author's manuscript

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