Bibcode
Socas-Navarro, H.
Referencia bibliográfica
Neural Networks, 16, 355
Advertised on:
4
2003
Revista
Número de citas
27
Número de citas referidas
23
Descripción
The quantification of the solar magnetic field is a crucial step in
modern solar physics to understand the dynamics, activity and
variability of our star. Presently, a reliable inference of these fields
is only possible by means of a computer-intensive process that has so
far limited scientists to the analysis of observations from small
regions of the solar disk, and/or very crude spatial and temporal
resolution. This work presents a different approach to the problem, in
which a multilayer perceptron, trained with known synthetic profiles, is
able to recognize the profiles and return the magnetic field used to
synthesize them. The network is then confronted with real observations
of a sunspot which had been previously inverted using traditional
inversion techniques. A quantitative comparison between these two
procedures shows the reliability of the network when applied to points
having magnetic filling factors larger than approximately 70%. The
dramatic decrease in the re!
quired computing time presents an opportunity for the routine analysis
of large-scale, high-resolution solar observations.