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Non-negative matrix factorization approach to sky subtraction for optical spectroscopy
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Numerous sky background subtraction techniques have been developed since the first implementations of computer-based reduction of spectra. Kurtz & Mink (2000) described a singular value decomposition-based method which allowed them to subtract night sky background from multi-fiber spectroscopic observations without any additional sky observations. We hereby take this approach one step further with usage of non-negative matrix factorization instead of principal component analysis and generalize it to 2D spectra. This allows us to generate approximately 10 times as many valid eigenspectra because of non-negativity. We apply our method to short-slit spectra of low-mass galaxies originating from intermediate-resolution Echelle spectrographs (ESI at Keck, MagE at Magellan, X-Shooter at the VLT) when sources fill the entire slit. We demonstrate its efficiency even when no offset sky observations were collected.
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Cited by 1 Pith paper
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Sky Background Building of Multi-objective Fiber spectra Based on Mutual Information Network
A mutual-information neural network estimates per-fiber sky backgrounds from all spectra in a LAMOST exposure, outperforming the standard Super sky on sky fibers, though object-fiber performance is not validated.
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