Abstract
Abstract Decades of studies have suggested several criteria to detect interplanetary coronal mass ejections (ICME) in time series from in situ spacecraft measurements. Among them, the most common are an enhanced and smoothly rotating magnetic field, a low proton temperature, and a low plasma beta. However, these features are not all observed for each ICME due to their strong variability. Visual detection is time-consuming and biased by the observer interpretation, leading to non-exhaustive, subjective, and thus hardly reproducible catalogs. Using convolutional neural networks on sliding windows and peak detection, we provide a fast, automatic, and multi-scale detection of ICMEs. The method has been tested on the in situ data from WIND between 1997 and 2015, and on the 657 ICMEs that were recorded during this period. The method offers an unambiguous visual proxy of ICMEs that gives an interpretation of the data similar to what an expert observer would give. We found at a maximum 197 of the 232 ICMEs of the 2010–2015 period (recall 84% ± 4.5%), including 90% of the ICMEs present in the lists of Nieves-Chinchilla et al. and Chi et al. The minimal number of False Positives was 25 out of 158 predicted ICMEs (precision 84% ± 2.6%). Although less accurate, the method also works with one or several missing input parameters. The method has the advantage of improving its performance by just increasing the amount of input data. The generality of the method paves the way for automatic detection of many different event signatures in spacecraft in situ measurements.
Highlights
Coronal Mass Ejections (CMEs) are spectacular manifestations of the solar activity which are responsible for the expulsion at large velocties of large quantities of solar plasma and magnetic field
Using Convolutional Neural Networks that estimated a similarity parameter for windows of data of various sizes, from 1 to 100hr, and a post processing method based on peak detection, we developed a pipeline that provides an automatic Interplanetary coronal mass ejections (ICME) detection from the WIND spacecraft in-situ measurements
Depending on the decision threshold we set on our detection, the pipeline offers the possibility to detect additional ICMEs or to generate consistent and reproducible ICME catalogs that could be used for further statistical study
Summary
Coronal Mass Ejections (CMEs) are spectacular manifestations of the solar activity which are responsible for the expulsion at large velocties of large quantities of solar plasma and magnetic field. Lepping et al (2005) proposed an automatic detection method based on empirical thresholds These thresholds are inferred from the expert knowledge of ICME properties and involve various physical and temporal parameters such as the duration, the plasma β, the magnetic field, the bulk velocity or the quality of the fit with a flux rope model. Even though this method was able to recognize a fair quantity of identified events (45 on a total of 76 ICMEs in the period considered), the large number of found false positives (66 for a total of 111 predicted ICMEs) evidenced both the incompleteness of the list as well as the limits of using fixed thresholds for automatic identification.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
