Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models

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dc.authoridCavus, Huseyin/0000-0003-4224-7039
dc.authoridYurchyshyn, Vasyl/0000-0001-9982-2175
dc.authoridAbduallah, Yasser/0000-0003-0792-2270
dc.contributor.authorAlobaid, Khalid A.
dc.contributor.authorAbduallah, Yasser
dc.contributor.authorWang, Jason T. L.
dc.contributor.authorWang, Haimin
dc.contributor.authorFan, Shen
dc.contributor.authorLi, Jialiang
dc.contributor.authorCavus, Huseyin
dc.date.accessioned2025-01-27T20:20:54Z
dc.date.available2025-01-27T20:20:54Z
dc.date.issued2023
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractCoronal 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.
dc.description.sponsorshipNSF divided by GEO divided by Division of Atmospheric and Geospace Sciences (AGS)https://doi.org/10.13039/100000159; King Saud University, Saudi Arabia [AGS-2300341]; NSF; Fulbright Visiting Scholar Program of the Turkish Fulbright Commission
dc.description.sponsorshipWe appreciate the anonymous referee for constructive comments and suggestions. We thank members of the Institute for Space Weather Sciences for fruitful discussions. K.A. is supported by King Saud University, Saudi Arabia. J.W. and H.W. acknowledge support from NSF grants AGS-1927578, AGS-2149748, AGS-2228996, and OAC-2320147. H.C. is supported by the Fulbright Visiting Scholar Program of the Turkish Fulbright Commission. V.Y. is supported by the NSF grant AGS-2300341. The CME catalog used in this work was created and maintained at the CDAW Data Center by NASA and the Catholic University of America in cooperation with the Naval Research Laboratory. SOHO is an international cooperation project between ESA and NASA.
dc.identifier.doi10.3847/2041-8213/ad0c4a
dc.identifier.issn2041-8205
dc.identifier.issn2041-8213
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85179819950
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3847/2041-8213/ad0c4a
dc.identifier.urihttps://hdl.handle.net/20.500.12428/21845
dc.identifier.volume958
dc.identifier.wosWOS:001110279000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIop Publishing Ltd
dc.relation.ispartofAstrophysical Journal Letters
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectCme Arrival-Time
dc.subjectReconnection
dc.titleEstimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models
dc.typeArticle

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