Murphy, Geoff G.Bull, PhilipSantos, Mario G.2026-09-112026-09-112026Murphy, G.G., Bull, P., Santos, M.G., Zhang, Z. and Cunnington, S., 2026. Bayesian component separation and power spectrum estimation for 21 cm intensity mapping data cubes. Monthly Notices of the Royal Astronomical Society, p.stag1503.https://doi.org/10.1093/mnras/stag1503https://hdl.handle.net/10566/25413Foreground removal remains an ongoing challenge in radio cosmology, and increasingly sensitive experiments necessitate more robust analysis techniques. In this work, we model simulated data from a single-dish intensity mapping experiment, and use the Gibbs sampling and Gaussian constrained realization (GCR) techniques to draw samples from the posterior probability distribution of the model parameters. This allows for a separation of the foregrounds and 21 cm signal at the map level, as well as recovery of the 1-dimensional H i power spectrum to within statistical uncertainties. Despite the model consisting of over 2 million free parameters in the example presented here, these methods allow us to sample from the Bayesian posterior at a rate of (Formula presented)  s per iteration. This framework is also resilient to frequency channel flagging (e.g. due to RFI excision), with the GCR steps effectively in-painting the missing data with statistically consistent model realizations. The power spectrum is recovered accurately in the presence of strong foreground contamination and RFI flagging – the estimate falling within (Formula presented) of the true model in our example, similar to the commonly-used transfer function correction method. Statistical realizations of foreground and H i maps are also recovered, with associated uncertainties available from the full joint posterior distribution of all parameters.encosmology: large scale structure of Universemethods: data analysismethods: statisticalIterative methodsMappingArticle