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We present a Bayesian framework to establish a power-spectrum spacedecomposition of frequency tomographic (PSDFT) data for future intensitymapping (IM) experiments. Different from most traditional component-separationmethods which work in the map domain, this new technique treats multifrequencypower spectra as raw data and can reconstruct component power spectra by takingadvantage of distinct components' correlation patterns in the frequency domain.We have validated this new technique for both interferometric andsingle-dish-like IM experiments, respectively, using synthesized mock data thatcontain bright foreground contaminants, IM signals, and instrumental effects atdifferent frequencies. The PSDFT approach can effectively remove the brightforeground contamination and extract the targeted IM signals using a Bayesianapproach in a power-spectrum subspace. This new approach can be directlyapplied to a broad range of IM analyses and will be well suited to futurehigh-quality IM datasets, providing a powerful tool for future IM surveys.

Power-spectrum space decomposition of frequency tomographic data for intensity mapping experiments