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We implement support for a cosmological parameter estimation algorithm asproposed by Racine et al. (2016) in Commander, and quantify its computationalefficiency and cost. For a semi-realistic simulation similar to Planck LFI 70GHz, we find that the computational cost of producing one single sample isabout 20 CPU-hours and that the typical Markov chain correlation length is$\sim$100 samples. The net effective cost per independent sample is $\sim$2 000CPU-hours, in comparison with all low-level processing costs of 812 CPU-hoursfor Planck LFI and WMAP in Cosmoglobe Data Release 1. Thus, althoughtechnically possible to run already in its current state, future work shouldaim to reduce the effective cost per independent sample by one order ofmagnitude to avoid excessive runtimes, for instance through multi-gridpreconditioners and/or derivative-based Markov chain sampling schemes. Thiswork demonstrates the computational feasibility of true Bayesian cosmologicalparameter estimation with end-to-end error propagation for high-precision CMBexperiments without likelihood approximations, but it also highlights the needfor additional optimizations before it is ready for full production-levelanalysis.

Cosmoglobe: Towards end-to-end CMB cosmological parameter estimation without likelihood approximations