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

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ID: 324356
2026
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Abstract
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.
Reference Key
openalex_W7201988285 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Geoff Murphy, Philip Bull, Mário G. Santos, Zheng Zhang, Steven Cunnington
Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1503
URL
https://doi.org/10.1093/mnras/stag1503
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