Modeling Spatially Varying (De)Convolution Kernels for LSST Difference Imaging

Details Modeling Spatially Varying (De)Convolution Kernels for LSST Difference Imaging Modeling Spatially Varying (De)Convolution Kernels for LSST Difference Imaging

Jan 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009aas...21346028b&link_type=abstract

American Astronomical Society, AAS Meeting #213, #460.28; Bulletin of the American Astronomical Society, Vol. 41, p.371

Astronomy and Astrophysics

Astronomy

Scientific paper

A main component of LSST's nightly Image Processing Pipeline is image subtraction. We present advances in the modeling of basis functions for image subtraction convolution kernels, including atomic decomposition and rapid selection of basis functions from a pre-computed dictionary. We also describe methods to model the spatial variation of the kernel, including methods adapted from the Geostatistical community. These techniques are being implemented within the LSST Data Management build system. Suggestions for convolution kernel and difference image quality metrics are presented.

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