An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10492003" target="_blank" >RIV/00216208:11320/24:10492003 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=r5MrnHcMam" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=r5MrnHcMam</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3847/1538-4365/ad2dea" target="_blank" >10.3847/1538-4365/ad2dea</a>
Alternative languages
Result language
angličtina
Original language name
An Algorithm for the Determination of Coronal Mass Ejection Kinematic Parameters Based on Machine Learning
Original language description
Coronal mass ejections (CMEs) constitute the major source of severe space weather events, with the potential to cause enormous damage to humans and spacecraft in space. It is becoming increasingly important to detect and track CMEs, since there are more and more space activities and facilities. We have developed a new algorithm to automatically derive a CME's kinematic parameters based on machine learning. Our method consists of three steps: recognition, tracking, and the determination of parameters. First, we train a convolutional neural network to classify images from Solar and Heliospheric Observatory Large Angle Spectrometric Coronagraph observations into two categories, containing CME(s) or not. Next, we apply the principal component analysis algorithm and Otsu's method to acquire binary-labeled CME regions. Then, we employ the track-match algorithm to track a CME's motion in time-series images and finally determine the CME's kinematic parameters, e.g., velocity, angular width, and central position angle. The results of four typical CME events with different morphological characteristics are presented and compared with a manual CME catalog and several automatic CME catalogs. Our algorithm shows some advantages in the recognition of CME structure and the accuracy of the kinematic parameters. This algorithm can be helpful for real-time CME warnings and predictions. In the future, this algorithm is capable of being applied to CME initialization in magnetohydrodynamic simulations to study the propagation characteristics of real CME events and to provide more efficient predictions of CMEs' geoeffectiveness.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10305 - Fluids and plasma physics (including surface physics)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Astrophysical Journal, Supplement Series
ISSN
0067-0049
e-ISSN
1538-4365
Volume of the periodical
271
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
59
UT code for WoS article
001200083900001
EID of the result in the Scopus database
2-s2.0-85190166194