Search

We investigated the interaction of a coronal mass ejection (CME) and a coronal hole (CH) in its vicinity using remote-sensing and 1 AU in situ data. We used extreme-ultraviolet images and magnetograms to identify coronal structures and coronagraph images to analyze the early CME propagation. The Wind spacecraft and the Advanced Composition Explorer (ACE) provide plasma and magnetic field data of near-Earth interplanetary space. We applied various diagnostic tools to the images and to the time-series data. We find that the CME erupts under a streamer and causes the evacuation of material at its far end, which is observable as dimming and subsequent CH formation. The CME is likely deflected in its early propagation and travels southwest of the Sun-Earth line. In situ data lack signatures of a large magnetic cloud, but show a small flux rope at the trailing edge of the interplanetary CME (ICME), followed by an Alfvénic wave. This wave is identified as exhaust from a Petschek-type reconnection region following the successful application of a Walén test. We infer that the two spacecraft at 1 AU most likely traverse the ICME leg that is in the process of reconnection along the heliospheric current sheet that separates the ICME and the high-speed stream outflowing from the CH.

Context. A precise detection of the coronal hole boundary is of primary interest for a better understanding of the physics of coronal holes, their role in the solar cycle evolution, and space weather forecasting. Aims. We develop a reliable, fully automatic method for the detection of coronal holes that provides consistent full-disk segmentation maps over the full solar cycle and can perform in real-time. Methods. We use a convolutional neural network to identify the boundaries of coronal holes from the seven extreme ultraviolet (EUV) channels of the Atmospheric Imaging Assembly (AIA) and from the line-of-sight magnetograms provided by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). For our primary model (Coronal Hole RecOgnition Neural Network Over multi-Spectral-data; CHRONNOS) we use a progressively growing network approach that allows for efficient training, provides detailed segmentation maps, and takes into account relations across the full solar disk. Results. We provide a thorough evaluation for performance, reliability, and consistency by comparing the model results to an independent manually curated test set. Our model shows good agreement to the manual labels with an intersection-over-union (IoU) of 0.63. From the total of 261 coronal holes with an area > 1.5 × 1010 km2 identified during the time-period from November 2010 to December 2016, 98.1% were correctly detected by our model. The evaluation over almost the full solar cycle no. 24 shows that our model provides reliable coronal hole detections independent of the level of solar activity. From a direct comparison over short timescales of days to weeks, we find that our model exceeds human performance in terms of consistency and reliability. In addition, we train our model to identify coronal holes from each channel separately and show that the neural network provides the best performance with the combined channel information, but that coronal hole segmentation maps can also be obtained from line-of-sight magnetograms alone. Conclusions. The proposed neural network provides a reliable data set for the study of solar-cycle dependencies and coronal-hole parameters. Given the fast and robust coronal hole segmentation, the algorithm is also highly suitable for real-time space weather applications.