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arXiv · 2604.26890

Bayesian component separation and power spectrum estimation for 21 cm intensity mapping data cubes

Geoff G. Murphy·Philip Bull·Mario G. Santos·Zheng Zhang·Steven Cunnington
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Abstract

Foreground 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 realisation (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 HI 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 $<30$ seconds 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 realisations. The power spectrum is recovered accurately in the presence of strong foreground contamination and RFI flagging -- the estimate falling within $2σ$ of the true model in our example, similar to the commonly-used transfer function correction method. Statistical realisations of foreground and HI maps are also recovered, with associated uncertainties available from the full joint posterior distribution of all parameters.

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Geoff G. Murphy, Philip Bull, Mario G. Santos, Zheng Zhang, Steven Cunnington. 2026-08-24. Bayesian component separation and power spectrum estimation for 21 cm intensity mapping data cubes. https://doi.org/10.1093/mnras%2Fstag1503

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