Artificial intelligence unfolding for space radiation monitor data

S Aminalragia-Giamini,C Papadimitriou,H Evans,P Jiggens,I Sandberg,D Rodgers,I A Daglis,A Tsigkanos

doi:10.1051/swsc/2018041

Abstract

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).

Highlights

A significant volume of data associated with observations of critical Space Weather phenomena, such as Solar Proton Events (SPE) (Paassilta et al, 2018; Robinson et al, 2018) and Radiation Belts (RB) enhancements (Baker et al, 2004; Tang et al, 2016), are attributed to in situ measurements of radiation monitors on board spacecrafts
For the GenCORUM unfolding, we have used spectral indexes b 2
In this work we have presented an Artificial Intelligence unfolding method for the calculation of proton and electron fluxes from radiation monitor count-rates

Summary

Introduction

A significant volume of data associated with observations of critical Space Weather phenomena, such as Solar Proton Events (SPE) (Paassilta et al, 2018; Robinson et al, 2018) and Radiation Belts (RB) enhancements (Baker et al, 2004; Tang et al, 2016), are attributed to in situ measurements of radiation monitors on board spacecrafts. Radiation environment monitors are instruments tasked with measuring and recording the incident charged particle radiation and provide information and alerts to the host spacecraft and monitoring systems They are typically designed to be sensitive to particle radiation from protons with energies of few up to hundreds of MeVs and to electrons with energies of few hundred KeVs up to few MeVs. They are typically designed to be sensitive to particle radiation from protons with energies of few up to hundreds of MeVs and to electrons with energies of few hundred KeVs up to few MeVs Such energetic particle populations usually originate from solar eruptive phenomena (Liu et al, 2014; Papaioannou et al, 2016) and the complex dynamics of Earth’s Radiation Belts (Kataoka & Miyoshi 2006; Turner et al, 2014).

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