We present cosmological parameter constraints as estimated using the Bayesian BeyondPlanck analysis framework. This method supports seamless end-to-end error propagation from raw time-ordered data to final cosmological parameters. As a first demonstration of the method, we analyze time-ordered Planck LFI observations, combined with selected external data (WMAP 33-61 GHz, Planck HFI DR4 353 and 857 GHz, and Haslam 408 MHz) in the form of pixelized maps which are used to break critical astrophysical degeneracies. Overall, all results are generally in good agreement with previously reported values from Planck 2018 and WMAP, with the largest relative difference for any parameter of about 1 σ when considering only temperature multipoles between 30 ≤ ≤ 600. In cases where there are differences, we note that the BeyondPlanck results are generally slightly closer to the high-HFI-dominated Planck 2018 results than previous analyses, suggesting slightly less tension between low and high multipoles. Using low-polarization information from LFI and WMAP, we find a best-fit value of τ = 0.066±0.013, which is higher than the low value of τ = 0.051±0.006 derived from Planck 2018 and slightly lower than the value of 0.069 ± 0.011 derived from joint analysis of official LFI and WMAP products. Most importantly, however, we find that the uncertainty derived in the BeyondPlanck processing is about 30 % larger than when analyzing the official products, after taking into account the different sky coverage. We argue that this is due to marginalizing over a more complete model of instrumental and astrophysical parameters, and this results in both more reliable and more rigorously defined uncertainties. We find that about 2000 Monte Carlo samples are required to achieve robust convergence for a low-resolution CMB covariance matrix with 225 independent modes, and producing these samples takes about eight weeks on a modest computing cluster with 256 cores.

We present cosmological parameter constraints estimated using the Bayesian BeyondPlanck analysis framework. This method supports seamless end-to-end error propagation from raw time-ordered data onto final cosmological parameters. As a first demonstration of the method, we analyzed time-ordered Planck LFI observations, combined with selected external data (WMAP 33-61 GHz, Planck HFI DR4 353 and 857 GHz, and Haslam 408 MHz) in the form of pixelized maps that are used to break critical astrophysical degeneracies. Overall, all the results are generally in good agreement with previously reported values from Planck 2018 and WMAP, with the largest relative difference for any parameter amounting about 1 σ when considering only temperature multipoles between 30 ≤ ℓ ≤ 600. In cases where there are differences, we note that the BeyondPlanck results are generally slightly closer to the high-ℓ HFI-dominated Planck 2018 results than previous analyses, suggesting slightly less tension between low and high multipoles. Using low-ℓ polarization information from LFI and WMAP, we find a best-fit value of τ = 0.066 ± 0.013, which is higher than the low value of τ = 0.052 ± 0.008 derived from Planck 2018 and slightly lower than the value of 0.069 ± 0.011 derived from the joint analysis of official LFI and WMAP products. Most importantly, however, we find that the uncertainty derived in the BeyondPlanck processing is about 30 % greater than when analyzing the official products, after taking into account the different sky coverage. We argue that this uncertainty is due to a marginalization over a more complete model of instrumental and astrophysical parameters, which results in more reliable and more rigorously defined uncertainties. We find that about 2000 Monte Carlo samples are required to achieve a robust convergence for a low-resolution cosmic microwave background (CMB) covariance matrix with 225 independent modes, and producing these samples takes about eight weeks on a modest computing cluster with 256 cores.

The BeyondPlanck and Cosmoglobe collaborations have implemented the first integrated Bayesian end-toend analysis pipeline for CMB experiments. The primary long-term motivation for this work is to develop a common analysis platform that supports efficient global joint analysis of complementary radio, microwave, and sub-millimeter experiments. A strict prerequisite for this to succeed is broad participation from the CMB community, and two foundational aspects of the program are therefore reproducibility and Open Science. In this paper, we discuss our efforts toward this aim. We also discuss measures toward facilitating easy code and data distribution, community-based code documentation, user-friendly compilation procedures, etc. This work represents the first publicly released end-to-end CMB analysis pipeline that includes raw data, source code, parameter files, and documentation. We argue that such a complete pipeline release should be a requirement for all major future and publicly-funded CMB experiments, noting that a full public release significantly increases data longevity by ensuring that the data quality can be improved whenever better processing techniques, complementary datasets, or more computing power become available, and thereby also taxpayers' value for money; providing only raw data and final products is not sufficient to guarantee full reproducibility in the future.

The BeyondPlanck and Cosmoglobe collaborations have implemented the first integrated Bayesian end-to-end analysis pipeline for CMB experiments. The primary long-term motivation for this work is to develop a common analysis platform that supports efficient global joint analysis of complementary radio, microwave, and sub-millimeter experiments. A strict prerequisite for this to succeed is broad participation from the CMB community, and two foundational aspects of the program are therefore reproducibility and Open Science. In this paper, we discuss our efforts toward this aim. We also discuss measures toward facilitating easy code and data distribution, community-based code documentation, user-friendly compilation procedures, etc. This work represents the first publicly released end-to-end CMB analysis pipeline that includes raw data, source code, parameter files, and documentation. We argue that such a complete pipeline release should be a requirement for all major future and publicly-funded CMB experiments, noting that a full public release significantly increases data longevity by ensuring that the data quality can be improved whenever better processing techniques, complementary datasets, or more computing power become available, and thereby also taxpayers' value for money; providing only raw data and final products is not sufficient to guarantee full reproducibility in the future.