Automatic Detection of Interplanetary Coronal Mass Ejections
- 1Know Center, Knowledge Discovery, Graz, Austria
- 2Institute of Physics, University of Graz, Graz, Austria
- 3Department of Physics, Washington University in St. Louis, MO 63130, USA
- 4RL Community, AI AUSTRIA, Vienna, Austria
- 5Space Research Institute, Austrian Academy of Sciences, Graz, Austria
- 6Zentralanstalt für Meteorologie und Geodynamik, Vienna, Austria
Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past,
different machine learning approaches have been used to automatically detect events in existing time series resulting from
solar wind in situ data. However, classification, early detection and ultimately forecasting still remain challenges when facing
the large amount of data from different instruments. We propose a pipeline using a Network similar to the ResUNet++ (Jha et al. (2019)), for the automatic detection of ICMEs. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding GPU usage, training time and robustness to missing features, thus making it more usable for other datasets.
The method has been tested on in situ data from WIND. Additionally, it produced reasonable results on STEREO A and STEREO B datasets
with less input parameters. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions.
How to cite: Ruedisser, H., Windisch, A., Amerstorfer, U. V., Amerstorfer, T., Möstl, C., Reiss, M. A., and Bailey, R. L.: Automatic Detection of Interplanetary Coronal Mass Ejections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9077, https://doi.org/10.5194/egusphere-egu22-9077, 2022.
