{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-python-compressed-low-ell-planck-likelihood","title":"A Python compressed low-$\\ell$ Planck likelihood for temperature and polarization","arxiv_id":"2104.05715","date":"2021-04-12","proceeding":null,"authors":["Heather Prince","Jo Dunkley"],"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.","url_abs":"https://arxiv.org/abs/2104.05715v1","url_pdf":"https://arxiv.org/pdf/2104.05715v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-python-compressed-low-ell-planck-likelihood","repo_url":"https://github.com/heatherprince/planck-low-py","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"a-python-compressed-low-ell-planck-likelihood","repo_url":"https://github.com/alexreevesy/planck_compressed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}