Computer Science – Performance
Scientific paper
Dec 2007
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007agufmsh13a1096j&link_type=abstract
American Geophysical Union, Fall Meeting 2007, abstract #SH13A-1096
Computer Science
Performance
7538 Solar Irradiance, 7594 Instruments And Techniques
Scientific paper
We have developed a statistical method for feature identification in NSO multidimensional imagery which requires a training set of independently determined image segmentations. The large spatial scale of our initial training set determined by the algorithm of Harvey and White (1999, ApJ 515, p. 812) mixes the details of magnetic network which are contained in the observations with quiet Sun and other features. We have found it difficult to reproduce this large scale in models of conditional and prior probabilities and are in fact interested in marking smaller scale structures for comparison with variation of total and spectral solar irradiance. We describe in this paper the performance of our technique with finer scale training sets determined by observations from other instruments and independently for the NSO data.
Jones Philip H.
Malanushenko Olena V.
Pap Judit M.
Turmon M. J.
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