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

TL;DR

This paper presents a fast, accurate machine learning pipeline, adapted from medical image segmentation, for automatic detection of interplanetary coronal mass ejections in solar wind data, outperforming previous methods in training speed.

Contribution

Introduces a novel, efficient detection pipeline for ICMEs using a medical imaging technique, significantly reducing training time and applicable to various datasets.

Findings

Achieved a True Skill Statistic of 0.64 on Wind data from 1997-2015.

Detected 466 out of 640 ICMEs with 254 false positives.

Maintained reasonable detection performance on smaller datasets from multiple spacecraft.

Abstract

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 propose a pipeline using a method that has recently proven successful in medical image segmentation. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding training time by a factor of approximately 20, thus making it more applicable for other datasets. The method has been tested on in situ data from the Wind spacecraft between 1997 and 2015 with a True Skill Statistic (TSS) of 0.64. Out of the 640 ICMEs, 466 were detected correctly by our algorithm, producing a total of 254 False Positives. Additionally, it produced reasonable results on datasets with fewer features and smaller training sets from Wind, STEREO-A and STEREO-B with True Skill Statistics of 0.56, 0.57 and 0.53, respectively. Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hours and 56 minutes, and the end time with a MAE of 3 hours and 20 minutes. 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.