Automatic Detection of Interplanetary Coronal Mass Ejections

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 fromsolar wind in situ data. However, classification, early detection and ultimately forecasting still remain challenges when facingthe 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 datasetswith 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.