Open Access
| Issue |
J. Space Weather Space Clim.
Volume 9, 2019
|
|
|---|---|---|
| Article Number | A22 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/swsc/2019019 | |
| Published online | 27 June 2019 | |
- Barnes G, Longcope DW, Leka KD. 2005. Implementing a magnetic charge topology model for solar active regions. ApJ 629(1): 561–571. DOI: 10.1086/431175. [Google Scholar]
- Barnes G, Leka KD, Schrijver CJ, et al. 2016. A comparison of flare forecasting methods. I. Results from the All-Clear workshop. ApJ 829(2): 89. DOI: 10.3847/0004-637X/829/2/89. [NASA ADS] [CrossRef] [Google Scholar]
- Benz AO. 2017. Flare Observations. Liv Rev Sol Phys 14(2): 59. DOI: 10.1007/s41116-016-0004-3. [Google Scholar]
- Bloomfield DS, Higgins PA, McAteer RTJ, Gallagher PT. 2012. Toward reliable benchmarking of solar flare forecasting methods. ApJL 747(2): L41. DOI: 10.1088/2041-8205/747/2/L41. [Google Scholar]
- Camporeale E. 2019. The challenge of machine learning in space weather nowcasting and forecasting. arXiv:1903.05192. [Google Scholar]
- Contarino L, Zuccarello F, Romano P, Spadaro D, Ermolli I. 2009. Flare forecasting based on sunspot-groups characteristics. Acta Geophys 57(1): 52–63. DOI: 10.2478/s11600-008-0067-1. [CrossRef] [Google Scholar]
- Falconer DA, Moore RL, Gary GA. 2008. Magnetogram measures of total nonpotentiality for prediction of solar coronal mass ejections from active regions of any degree of magnetic complexity. ApJ 689(2): 1433–1442. DOI: 10.1086/591045. [Google Scholar]
- Florios K, Kontogiannis I, Park S-H, et al. 2018. Forecasting solar flares using magnetogram-based predictors and machine learning. Sol Phys 293(2): 42. DOI: 10.1007/s11207-018-1250-4. [Google Scholar]
- Georgoulis MK, Rust DM. 2007. Quantitative forecasting of major solar flares. ApJ 661(1): L109–L112. DOI: 10.1086/518718. [NASA ADS] [CrossRef] [Google Scholar]
- Inceoglu F, Jeppesen JH, Kongstad P, et al. 2018. Using machine learning methods to forecast if solar flares will be associated with CMEs and SEPs. ApJ 861(2): 128. DOI: 10.3847/1538-4357/aac81e. [CrossRef] [Google Scholar]
- Jaynes ET. 2003. Probability Theory – The Logic of Science. Cambridge University Press. DOI: 10.1017/CBO9780511790423 [Google Scholar]
- Kim T, Park E, Lee H, et al. 2019. Solar farside magnetograms from deep learning analysis of STEREO/EUVI data. Nat Astron 3: 397–400. DOI: 10.1038/s41550-019-0711-5. [Google Scholar]
- Kontogiannis I, Georgoulis MK, Park S-H, Guerra JA. 2018. Testing and improving a set of morphological predictors of flaring activity. Sol Phys 293(6): 18. DOI: 10.1007/s11207-018-1317-2. [CrossRef] [Google Scholar]
- Korsós MB, Erdélyi R. 2016. On the state of a solar active region before flares and CMEs. ApJ 823(2): 153. DOI: 10.3847/0004-637X/823/2/153. [CrossRef] [Google Scholar]
- Korsós MB, Ludmány A, Erdélyi R, Baranyi T. 2015. On flare predictability based on sunspot group evolution. ApJL 802(2): L21. DOI: 10.1088/2041-8205/802/2/L21. [CrossRef] [Google Scholar]
- Korsós MB, Yang S, Erdélyi R. 2019. Investigation of pre-flare dynamics using the weighted horizontal magnetic gradient method: From small to major flare classes. J Space Weather Space Clim 9: A6. DOI: 10.1051/swsc/2019002. [CrossRef] [Google Scholar]
- Leka KD, Barnes G. 2003. Photospheric Magnetic Field Properties of Flaring versus Flare-quiet Active Regions. I. Data, General Approach, and Sample Results. ApJ 595(2): 1277–1295. DOI: 10.1086/377511. [CrossRef] [Google Scholar]
- Romano P, Elmhamdi A, Falco M, et al. 2018. Homologous white light solar flares driven by photospheric shear motions. ApJL 852(1): L10. DOI: 10.3847/2041-8213/aaa1df. [CrossRef] [Google Scholar]
- Romano P, Elmhamdi A, Kordi A. 2019. Two strong white-light solar flares in AR NOAA 12673 as potential clues for stellar superflares. Sol Phys 294(1): 4. DOI: 10.1007/s11207-018-1388-0. [Google Scholar]
- Romano P, Zuccarello F. 2007. Photospheric magnetic evolution of super active regions. A&A 474(2): 633–637. DOI: 10.1051/0004-6361:20078110. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Romano P, Zuccarello F, Guglielmino SL, et al. 2015. Recurrent flares in active region NOAA 11283. A&A 582: A55. DOI: 10.1051/0004-6361/201525887. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Schrijver CJ. 2007. A characteristic magnetic field pattern associated with all major solar flares and its use in flare forecasting. ApJL 655(2): L117–L120. DOI: 10.1086/511857. [NASA ADS] [CrossRef] [Google Scholar]
- Ternullo M, Contarino L, Romano P, Zuccarello F. 2006. A statistical analysis of sunspot groups hosting M and X flares. Astron Nachr 327(1): 36–43. DOI: 10.1002/asna.200510485. [CrossRef] [Google Scholar]
- Wheatland MS. 2004. A bayesian approach to solar flare prediction. ApJ 609(2): 1134–1139. DOI: 10.1086/421261. [NASA ADS] [CrossRef] [Google Scholar]
- Yuan Y, Shih FY, Jing J, Wang H-M. 2010. Automated flare forecasting using a statistical learning technique. Res Astron Astrophys 10(8): 785–796. DOI: 10.1088/1674-4527/10/8/008. [Google Scholar]
- Zirin H. 1998. The Astrophysics of the Sun, by H. Zirin. Cambridge University Press, Cambridge, UK, 448 p. [Google Scholar]
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