Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind in Situ Data

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resulting from solar wind in situ observations. However, accurate and fast detection still remains a challenge when facing the large amount of data from different instruments. For the automatic detection of ICMEs we recently published a deep learning pipeline which has been trained, validated and tested on Wind, STEREO-A and STEREO-B data. We shortly present results of this work and talk about our current attempt to extend its application to a real time scenario in order to investigate its eligibility for functioning as an early warning system.