A Python compressed low- Planck likelihood for temperature and polarization
arXiv:2104.05715
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 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 CDM model parameters using Planck-low-py are consistent with those derived with the full Commander and SimAll likelihoods from the Planck legacy release.
arXiv comment7 pages, 6 figures, code available at https://github.com/heatherprince/planck-low-py
References in corpus (11)
- Efficient Computation of CMB anisotropies in closed FRW models
- Cosmological parameters from CMB and other data: a Monte-Carlo approach
- Efficient sampling of fast and slow cosmological parameters
- First Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Parameter Estimation Methodology
- CMB power spectrum parameter degeneracies in the era of precision cosmology
- Polarization of the Microwave Background in Reionized Models
- How to measure CMB polarization power spectra without losing information
- Generalized massive optimal data compression
- Hybrid Estimation of CMB Polarization Power Spectra
- Taking Bigger Metropolis Steps by Dragging Fast Variables
- Data compression in cosmology: A compressed likelihood for Planck data