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

TL;DR

This paper introduces a Bayesian power-spectrum space decomposition method for frequency tomographic data, enabling effective foreground removal and signal extraction in intensity mapping experiments.

Contribution

It presents a novel technique that operates on power spectra rather than maps, improving component separation in intensity mapping data.

Findings

Successfully removes bright foreground contamination.

Accurately extracts intensity mapping signals.

Validated with mock data for interferometric and single-dish experiments.

Abstract

We present a Bayesian framework to establish a power-spectrum space decomposition of frequency tomographic (PSDFT) data for future intensity mapping (IM) experiments. Different from most traditional component-separation methods which work in the map domain, this new technique treats multifrequency power spectra as raw data and can reconstruct component power spectra by taking advantage of distinct components' correlation patterns in the frequency domain. We have validated this new technique for both interferometric and single-dish-like IM experiments, respectively, using synthesized mock data that contain bright foreground contaminants, IM signals, and instrumental effects at different frequencies. The PSDFT approach can effectively remove the bright foreground contamination and extract the targeted IM signals using a Bayesian approach in a power-spectrum subspace. This new approach can be directly applied to a broad range of IM analyses and will be well suited to future high-quality IM datasets, providing a powerful tool for future IM surveys.