Turning Noise into Data: Characterization of the Van Allen Radiation Belt Using SDO Spikes Data

Speaker: Spyros Kasapis, University of Michigan
October 4, 2022

Presentation

Abstract

The Solar Dynamics Observatory (SDO) is a solar mission in an inclined geosynchronous orbit. Since commissioning, images acquired by Atmospheric Imaging Assembly (AIA) instrument on-board the SDO have frequently displayed “spikes”, pixel regions yielding an extreme number of digital counts. These are theorized to occur from energetic electron collisions with the instrument detector system. These spikes are regularly removed from AIA Level 1.0 images in order to produce clean and reliable data. A study of historical data has found over 100 trillion spikes in the past decade. This project correlates spike detection frequency with radiation environment parameters in order to generate an augmented data product from SDO. We conduct a correlation study between SDO/AIA data and radiation belt activity within the SDO's orbit. By extracting radiation “spike” data from the SDO/AIA images, we produce a comprehensive data product which is correlated not only with geomagnetic parameters such as Kp, Ap and Sym-H but also with the electron and proton fluxes measured by the GOES-14 satellite. As a result, we find that AIA spikes are highly correlated with the GOES-14 electrons detected by the MAGED and EPEAD instruments at the equator (where the two satellites meet) with Spearman's Correlation values of ρ=0.73 and ρ=0.53 respectively, while a weaker correlation of ρ=0.47 is shown with MAGPD protons for the two-year period where both missions returned data uninterruptedly. This correlation proves that the SDO spike data can be proven useful for characterizing the Van Allen radiation belt, especially at areas (outside the equator) where other satellites can't.

Biography

Spyros Kasapis completed his Aerospace Engineering Bachelor’s and Master’s degrees in Worcester Polytechnic Institute where he focused on controls and autonomous aircraft path planning. He obtained his second Master’s degree in Naval Architecture and Marine Engineering and he is currently a 5th year PhD student with a dissertation focused on machine learning and visual recognition. During his time in Michigan and as part of the SOLSTICE team in the Climate and Space Science and Engineering Department, he engaged in research on machine learning methods to predict Solar Energetic Particle events. This summer, as an intern at the NASA Goddard Space Flight Center, he worked on characterizing the Van Allen radiation belt using the SDO satellite data while also being a member of the JPL PSSS 2022 Session 2 cohort where he worked on the Gelatto asteroid sample return mission proposal.