Identifying Coronal Mass Ejection Active Region Sources: An Automated Approach
Identifying the source regions of coronal mass ejections (CMEs) is crucial for understanding their origins and improving space weather forecasting. We present an automated algorithm for matching CMEs detected by the Large Angle Spectrometric Coronagraph with their source active regions, specifically...
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| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ad9b27 |
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| author | Julio Hernandez Camero Lucie M. Green Alex Piñel Neparidze |
| author_facet | Julio Hernandez Camero Lucie M. Green Alex Piñel Neparidze |
| author_sort | Julio Hernandez Camero |
| collection | DOAJ |
| description | Identifying the source regions of coronal mass ejections (CMEs) is crucial for understanding their origins and improving space weather forecasting. We present an automated algorithm for matching CMEs detected by the Large Angle Spectrometric Coronagraph with their source active regions, specifically Space Weather HMI Active Region Patches (SHARPs), between 2010 May and 2019 January. Our method uses posteruptive signatures, including flares and coronal dimmings, to associate CMEs with potential source regions. Out of 4190 CMEs, we successfully match 1132, achieving a recall rate of ~57% for frontside events. We find that the algorithm performs better for complex SHARP regions containing multiple NOAA regions and for faster CMEs, consistent with expectations that more energetic events produce stronger eruption signatures. We find that CME–flare association rates increase with flare intensity, aligning with previous studies. While our approach has limitations, such as focusing exclusively on SHARP regions and relying on a limited set of posteruptive signatures, it significantly reduces the time required for CME source identification while providing transparent, reproducible results. We encourage the solar physics community to build upon this work, developing improved automated tools for CME source identification. The resulting catalog of CME–source region associations is made publicly available, offering a valuable resource for statistical studies and machine learning applications in solar physics and space weather forecasting. |
| format | Article |
| id | doaj-art-e2c61b4dc0ba4120b798359bfdebab65 |
| institution | Kabale University |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-e2c61b4dc0ba4120b798359bfdebab652025-01-17T08:43:12ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-0197916310.3847/1538-4357/ad9b27Identifying Coronal Mass Ejection Active Region Sources: An Automated ApproachJulio Hernandez Camero0https://orcid.org/0000-0002-4472-4559Lucie M. Green1https://orcid.org/0000-0002-0053-4876Alex Piñel Neparidze2https://orcid.org/0009-0009-0001-1360Mullard Space Science Laboratory, University College London , UKMullard Space Science Laboratory, University College London , UKUniversity College London , UKIdentifying the source regions of coronal mass ejections (CMEs) is crucial for understanding their origins and improving space weather forecasting. We present an automated algorithm for matching CMEs detected by the Large Angle Spectrometric Coronagraph with their source active regions, specifically Space Weather HMI Active Region Patches (SHARPs), between 2010 May and 2019 January. Our method uses posteruptive signatures, including flares and coronal dimmings, to associate CMEs with potential source regions. Out of 4190 CMEs, we successfully match 1132, achieving a recall rate of ~57% for frontside events. We find that the algorithm performs better for complex SHARP regions containing multiple NOAA regions and for faster CMEs, consistent with expectations that more energetic events produce stronger eruption signatures. We find that CME–flare association rates increase with flare intensity, aligning with previous studies. While our approach has limitations, such as focusing exclusively on SHARP regions and relying on a limited set of posteruptive signatures, it significantly reduces the time required for CME source identification while providing transparent, reproducible results. We encourage the solar physics community to build upon this work, developing improved automated tools for CME source identification. The resulting catalog of CME–source region associations is made publicly available, offering a valuable resource for statistical studies and machine learning applications in solar physics and space weather forecasting.https://doi.org/10.3847/1538-4357/ad9b27Solar coronal mass ejectionsSolar physicsCatalogsSolar flaresSolar active region magnetic fieldsSolar active regions |
| spellingShingle | Julio Hernandez Camero Lucie M. Green Alex Piñel Neparidze Identifying Coronal Mass Ejection Active Region Sources: An Automated Approach The Astrophysical Journal Solar coronal mass ejections Solar physics Catalogs Solar flares Solar active region magnetic fields Solar active regions |
| title | Identifying Coronal Mass Ejection Active Region Sources: An Automated Approach |
| title_full | Identifying Coronal Mass Ejection Active Region Sources: An Automated Approach |
| title_fullStr | Identifying Coronal Mass Ejection Active Region Sources: An Automated Approach |
| title_full_unstemmed | Identifying Coronal Mass Ejection Active Region Sources: An Automated Approach |
| title_short | Identifying Coronal Mass Ejection Active Region Sources: An Automated Approach |
| title_sort | identifying coronal mass ejection active region sources an automated approach |
| topic | Solar coronal mass ejections Solar physics Catalogs Solar flares Solar active region magnetic fields Solar active regions |
| url | https://doi.org/10.3847/1538-4357/ad9b27 |
| work_keys_str_mv | AT juliohernandezcamero identifyingcoronalmassejectionactiveregionsourcesanautomatedapproach AT luciemgreen identifyingcoronalmassejectionactiveregionsourcesanautomatedapproach AT alexpinelneparidze identifyingcoronalmassejectionactiveregionsourcesanautomatedapproach |