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Astrophysics Source Code Library

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Searching for codes credited to 'Acero, F.'

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[ascl:2409.003] SUSHI: Semi-blind Unmixing with Sparsity for Hyperspectral Images

SUSHI (Semi-blind Unmixing with Sparsity for hyperspectral images) performs non-stationary unmixing of hyperspectral images. The typical use case is to map the physical parameters such as temperature and redshift from a model with multiple components using data from hyperspectral images. Applying a spatial regularization provides more robust results on voxels with low signal to noise ratio. The code has been used on X-ray astronomy but the method can be applied to any integral field unit (IFU) data cubes.