Computer Science – Learning
Scientific paper
Dec 2009
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009agufmsm51a1335k&link_type=abstract
American Geophysical Union, Fall Meeting 2009, abstract #SM51A-1335
Computer Science
Learning
[1942] Informatics / Machine Learning, [2720] Magnetospheric Physics / Energetic Particles: Trapped, [2774] Magnetospheric Physics / Radiation Belts
Scientific paper
More and more radiation belt models are being combined with data using data assimilation methods. One important step towards this goal is to convert radiation belt data into phase space densities and adiabatic coordinates. This step of converting fluxes into phase space densities requires accurate calculations of particle drift shells or magnetic drift invariants L*. In a dynamic and realistic field, calculating L* as one of the adiabatic coordinates needs sophisticated magnetic field models that, in turn, require computationally intensive numerical integration. Typically a single L* drift shell integration can take on the order of 10^5 calls to a magnetic field model. In addition, the drift shell has to be recalculated every few minutes because either the spacecraft moved along its orbit or the magnetic field environment changed. This has turned into a computational bottleneck for many radiation belt models. We have developed a new method for calculation L* orders of magnitudes faster than direct numerical integration methods. Our method is based on a neural network that can replace the computationally intensive L* calculation with the Tsyganenko & Sitnov 2005 model. This new surrogate model has applications to real-time radiation belt forecasting, analysis of data sets spanning decades of observations, and other space weather applications.
Friedel Reiner H.
Koller Josef
Reeves Geoff D.
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