Automatic 3D Reconstruction of Coronal Mass Ejections Based on Dual-viewpoint Observations and Machine Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10510548" target="_blank" >RIV/00216208:11320/25:10510548 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=2aajOIJ4DS" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=2aajOIJ4DS</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3847/1538-4365/adf433" target="_blank" >10.3847/1538-4365/adf433</a>
Alternative languages
Result language
angličtina
Original language name
Automatic 3D Reconstruction of Coronal Mass Ejections Based on Dual-viewpoint Observations and Machine Learning
Original language description
Coronal mass ejections (CMEs) are the major driver of severe space weather events, causing substantial economic losses to space-based and ground-based human assets. It is essential to advance our understanding of CME propagation dynamics for better predictions. We have developed an algorithm that automatically reconstructs CME structure, integrating dual-viewpoint observations with machine learning techniques. It consists of three stages: (1) region acquisition, (2) model construction, and (3) function optimization. First, we use two independent convolutional neural networks to identify and detect CMEs in the coronagraph images from two spacecraft. Next, we construct the projections of the graduated cylindrical shell (GCS) model in the fields of view of the coronagraphs. In the final step, optimal parameters are retrieved by minimizing the function that quantifies the morphological discrepancies between the image of the GCS model and the CME detection. Four CME events are reconstructed and analyzed to demonstrate the accuracy of our algorithm. A statistical analysis of 97 CME events from 2007 to 2018 is conducted to investigate both the two-dimensional (2D) and three-dimensional (3D) parameters. According to our statistics, the actual velocities are underestimated by 8%, and the widths are overestimated by 47% because of the projection effect from 2D observations. Furthermore, the widths and velocities are investigated to have a positive correlation coefficient of 0.67 (2D) and 0.52 (3D). The proposed method can further be used to provide CME initial parameters for magnetohydrodynamics simulations, enabling a deeper understanding of CME kinematics.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10305 - Fluids and plasma physics (including surface physics)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
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
280
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
44
UT code for WoS article
001569022300001
EID of the result in the Scopus database
2-s2.0-105015494566