Automatic Detection and Classification of Boundary Crossings in Spacecraft in situ Data
- 1Know Center, Knowledge Discovery, Austria (hruedisser@know-center.at)
- 2University of Graz, Graz, Austria
- 3Department of Physics, Washington University in St. Louis, MO 63130, USA
- 4Space Research Institute, Austrian Academy of Sciences, Graz, Austria
- 5Institute of Atmospheric Physics of Czech Academy of Sciences, Prague, Czechia
- 6RL Community, AI AUSTRIA, Vienna, Austria
Planetary magnetospheres create multiple sharp boundaries, such as the bow shock, where the solar wind plasma is decelerated and compressed, or the magnetopause, a transition between solar wind field and planetary field.We attempt to use deep learning to identify magnetospheric boundaries, i.e. planetary and interplanetary shocks crossings and magnetopause crossings in spacecraft in situ data. The boundaries are identified by a discontinuity in a magnetic field, plasma density, and in the spectrum of high-frequency waves. These measurements are available on many planetary missions. Data from Earth's missions Cluster and THEMIS are used for training. We ultimately strive for successful classification of boundaries (shock, magnetopause, inbound, outbound) and the correct handling of multiple crossings.
How to cite: Rüdisser, H. T., Windisch, A., Amerstorfer, U. V., Píša, D., and Soucek, J.: Automatic Detection and Classification of Boundary Crossings in Spacecraft in situ Data, Europlanet Science Congress 2022, Granada, Spain, 18–23 Sep 2022, EPSC2022-47, https://doi.org/10.5194/epsc2022-47, 2022.
