Hyperinterpolation variants on the sphere achieve stable Sobolev approximation from scattered data by interpreting discretization error as the action of a spectral multiplier on the cubature discrepancy measure, without requiring exact cubature formulas.
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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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Hyperinterpolation beyond exact cubature: a spectral multiplier approach
Hyperinterpolation variants on the sphere achieve stable Sobolev approximation from scattered data by interpreting discretization error as the action of a spectral multiplier on the cubature discrepancy measure, without requiring exact cubature formulas.
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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.