BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.
Diffuse source separation in CMB observations
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abstract
We review issues and methods for diffuse component separation in the context of Cosmic Microwave Background observations. The revised version contains a paragraph on FastICA and its application to CMB component separation, which was missing in the first version.
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Comparison of Galactic magnetic field models to polarized synchrotron observations shows good agreement on angles but poor match on intensity, indicating local foreground structures must be incorporated.
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BROOM: a python package for model-independent analysis of microwave astronomical data
BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.
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A comparison between Galactic magnetic field models and polarized synchrotron emission with C-BASS at 4.76 GHz and S-PASS at 2.3 GHz
Comparison of Galactic magnetic field models to polarized synchrotron observations shows good agreement on angles but poor match on intensity, indicating local foreground structures must be incorporated.