Power-spectrum space decomposition of frequency tomographic data for intensity mapping experiments. (arXiv:2308.14777v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Feng_C/0/1/0/all/0/1">Chang Feng</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Abdalla_F/0/1/0/all/0/1">Filipe B. Abdalla</a>
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.
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.
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