Data driven foreground clustering approach to component separation in multifrequency CMB experiments: A new Planck CMB map
Rishi Khatri

TL;DR
This paper introduces a novel data-driven foreground clustering method for component separation in multifrequency CMB data, resulting in cleaner, less biased CMB maps especially at low Galactic latitudes, aiding cosmological analyses.
Contribution
The paper proposes a new clustering-based component separation technique that uses spectral data to improve CMB map quality, especially in challenging regions.
Findings
Produced cleaner, unbiased CMB maps at low Galactic latitudes.
Demonstrated improved separation using Planck simulations and data.
Maps are publicly available for further research.
Abstract
We present a new approach to component separation in multifrequency CMB experiments by formulating the problem as that of partitioning the sky into pixel clusters such that within each pixel cluster the foregrounds have similar spectrum, using only the information available in the data. Only spectral information is used for partitioning, allowing spatially far away pixels to belong to the same cluster if their foreground properties are close. We then apply a modified internal linear combination method to each pixel cluster. Since the foregrounds have similar spectrum within each cluster, the number of components required to describe the foregrounds is smaller compared to all data taken together and simple pixel based ILC algorithm works extremely well. We test our algorithm in the full focal plane simulations provided by the Planck collaboration. We apply our algorithm to the Planck…
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