Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models
Document Type
Article
Publication Date
12-1-2023
Abstract
Coronal mass ejections (CMEs) are massive solar eruptions, which have a significant impact on Earth. In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy. Being able to estimate these properties helps better understand CME dynamics. Our study is based on the CME catalog maintained at the Coordinated Data Analysis Workshops Data Center, which contains all CMEs manually identified since 1996 using the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory. We use LASCO C2 data in the period between 1996 January and 2020 December to train, validate, and test DeepCME through 10-fold cross validation. The DeepCME method is a fusion of three deep-learning models, namely ResNet, InceptionNet, and InceptionResNet. Our fusion model extracts features from LASCO C2 images, effectively combining the learning capabilities of the three component models to jointly estimate the mass and kinetic energy of CMEs. Experimental results show that the fusion model yields a mean relative error (MRE) of 0.013 (0.009, respectively) compared to the MRE of 0.019 (0.017, respectively) of the best component model InceptionResNet (InceptionNet, respectively) in estimating the CME mass (kinetic energy, respectively). To our knowledge, this is the first time that deep learning has been used for CME mass and kinetic energy estimations.
Identifier
85179819950 (Scopus)
Publication Title
Astrophysical Journal Letters
External Full Text Location
https://doi.org/10.3847/2041-8213/ad0c4a
e-ISSN
20418213
ISSN
20418205
Issue
2
Volume
958
Grant
AGS-2149748
Fund Ref
National Aeronautics and Space Administration
Recommended Citation
Alobaid, Khalid A.; Abduallah, Yasser; Wang, Jason T.L.; Wang, Haimin; Fan, Shen; Li, Jialiang; Cavus, Huseyin; and Yurchyshyn, Vasyl, "Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models" (2023). Faculty Publications. 1291.
https://digitalcommons.njit.edu/fac_pubs/1291
