J. Space Weather Space Clim.
Volume 8, 2018
System Science: Application to Space Weather Analysis, Modelling, and Forecasting
|Number of page(s)||15|
|Published online||14 November 2018|
Artificial intelligence unfolding for space radiation monitor data
Space Applications & Research Consultancy (SPARC), Athens, Greece
2 European Research and Technology Centre (ESTEC), European Space Agency (ESA), Noordwijk, The Netherlands
3 Department of Physics, National and Kapodistrian University of Athens, Athens, Greece
* Corresponding author: email@example.com
Accepted: 3 October 2018
The reliable and accurate calculation of incident particle radiation fluxes from space radiation monitor measurements, i.e. count-rates, is of great interest and importance. Radiation monitors are relatively simple and easy to implement instruments found on board multiple spacecrafts and can thus provide information about the radiation environment in various regions of space ranging from Low Earth orbit to missions in Lagrangian points and even interplanetary missions. However, the unfolding of fluxes from monitor count-rates, being an ill-posed inverse problem, is not trivial and prone to serious errors due to the inherent difficulties present in such problems. In this work we present a novel unfolding method which uses tools from the fields of Artificial Intelligence and Machine Learning to achieve good unfolding of monitor measurements. The unfolding method combines a Case Based Reasoning approach with a Genetic Algorithm, which are both widely used. We benchmark the method on data from European Space Agency’s (ESA) Standard Radiation Environment Monitor (SREM) on board the INTEGRAL mission by calculating proton fluxes during Solar Energetic Particle Events and electron fluxes from measurements within the outer Radiation Belt. Extensive evaluation studies are made by comparing the unfolded proton fluxes with data from the SEPEM Reference Dataset v2.0 and the unfolded electron fluxes with data from the Van Allen Probes mission instruments Magnetic Electron Ion Spectrometer (MagEIS) and Relativistic Electron Proton Telescope (REPT).
Key words: radiation monitors / unfolding / artificial intelligence / protons / electrons
© S. Aminalragia-Giamini et al., Published by EDP Sciences 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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