Open Access
Issue |
J. Space Weather Space Clim.
Volume 10, 2020
Topical Issue - Space Weather research in the Digital Age and across the full data lifecycle
|
|
---|---|---|
Article Number | 13 | |
Number of page(s) | 16 | |
DOI | https://doi.org/10.1051/swsc/2020014 | |
Published online | 17 April 2020 |
- Adams H, Emerson T, Kirby M, Neville R, Peterson C, Shipman P, Chepushtanova S, Hanson E, Motta F, Ziegelmeier L. 2017. Persistence images: A stable vector representation of persistent homology. J Mach Learn Res 18(1): 218–252. https://dl.acm.org/doi/abs/10.5555/3122009.3122017. [Google Scholar]
- Ahmed OW, Qahwaji R, Colak T, Dudok De Wit T, Ipson S. 2010. A new technique for the calculation and 3D visualisation of magnetic complexities on solar satellite images. Vis Comput 26(5): 385–395. https//doi.org/10.1007/s00371-010-0418-1. [CrossRef] [Google Scholar]
- Aulanier G, Pariat E, Démoulin P. 2005. Current sheet formation in Quasi-separatrix layers and hyperbolic flux tubes. A&A 444: 961–976. https://doi.org/10.1051/0004-6361:20053600. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Barnes G, Leka KD. 2006. Photospheric magnetic field properties of flaring vs. non-flare quiet active regions. III. Magnetic Charge Topology Models. Astrophys J 646: 1303–1318. https://doi.org/10.1086/510282. [NASA ADS] [CrossRef] [Google Scholar]
- Barnes G, Longcope DW, Leka KD. 2005. Implementing a magnetic charge topology model for solar active regions. Astrophys J 629: 561–571. https://doi.org/10.1086/431175. [NASA ADS] [CrossRef] [Google Scholar]
- Barnes G, Leka KD, Schrijver CJ, Colak T, Qahwaji R, et al. 2016. A comparison of flare forecasting methods. I. Results from the “All-Clear” Workshop. Astrophys J 829(2): 1–32. https://doi.org/10.3847/0004-637x/829/2/89. [Google Scholar]
- Benvenuto F, Piana M, Campi C, Massone AM. 2018. A hybrid supervised/unsupervised machine learning approach to solar flare prediction. Astrophys J 853(1): 90. https://doi.org/10.3847/1538-4357/aaa23c. [CrossRef] [Google Scholar]
- Bhavsar SP, Lauer DA. 1996. Analysis of the CFA “Great Wall” using the minimal spanning tree, Springer, Netherlands, Dordrecht, pp. 517–519. https://doi.org/10.1007/978-94-009-0145-2_66. [Google Scholar]
- Bobra MG, Couvidat S. 2015. Solar flare prediction using SDO/HMI vector magnetic field data with a machine-learning algorithm. Astrophys J 798(2): 135. https://doi.org/10.1088/0004-637x/798/2/135. [Google Scholar]
- Bobra MG, Sun X, Hoeksema JT, Turmon M, Liu Y, Hayashi K, Barnes G, Leka KD. 2014. The helioseismic and magnetic imager (HMI) vector magnetic field pipeline: SHARPs – Space-Weather HMI Active Region Patches. Sol Phys 289(9): 3549–3578. https://doi.org/10.1007/s11207-014-0529-3. [Google Scholar]
- Boucheron LE, Al-Ghraibah A, McAteer RTJ. 2015. Prediction of solar flare size and time-to-flare using support vector machine regression. Astrophys J 812: 51. https://doi.org/10.1088/0004-637X/812/1/51. [CrossRef] [Google Scholar]
- Bubenik P. 2015. Statistical topological data analysis using persistence landscapes. J Mach Learn Res 16(1): 77–102. https://dl.acm.org/doi/10.5555/2789272.2789275. [Google Scholar]
- Campi C, Benvenuto F, Massone AM, Bloomfield DS, Georgoulis MK, Piana M. 2019. Feature ranking of active region source properties in solar flare forecasting and the uncompromised stochasticity of flare occurrence. Astrophys J 883(2): 150. https://doi.org/10.3847/1538-4357/ab3c26. [CrossRef] [Google Scholar]
- Camporeale E. 2019. The challenge of machine learning in space weather: Nowcasting and forecasting. Space Weather 17(8): 1166–1207. https://doi.org/10.1029/2018SW002061. [CrossRef] [Google Scholar]
- Carrière M., Cuturi M., Oudot S. 2017. Sliced Wasserstein Kernel for persistence diagrams. In: Proceedings of the 34th International Conference on Machine Learning – Volume 70, ICML’17. PMLR, International Convention Centre, Sydney, Australia, pp. 664–673. https://JMLR.org, https://dl.acm.org/doi/10.5555/3305381.3305450. [Google Scholar]
- Carrière M, Chazal F, Ike Y, Lacombe T, Royer M, Umeda Y. 2019. PersLay: A simple and versatile neural network layer for persistence diagrams. to appear in The Proceedings of the International Conference on Artificial Intelligence and Statistics, 2020. [Google Scholar]
- Chazal F, Fasy BT, Lecci F, Rinaldo A, Wasserman L. 2014. Stochastic convergence of persistence landscapes and silhouettes. In: Proceedings of the Thirtieth Annual Symposium on Computational Geometry, SOCG’14, 474:474–474:483, ACM, New York, NY, USA. https://doi.org/10.1145/2582112.2582128 [Google Scholar]
- Colak T, Qahwaji R. 2009. Automated solar activity prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares. Space Weather 7: S06001. https://doi.org/10.1029/2008SW000401. [NASA ADS] [CrossRef] [Google Scholar]
- Crown MD. 2012. Validation of the NOAA Space Weather Prediction Center’s solar flare forecasting look-up table and forecaster-issued probabilities. Space Weather 10: S06006. https://doi.org/10.1029/2011SW000760. [CrossRef] [Google Scholar]
- de Silva V, Ghrist R. 2007. Coverage in sensor networks via persistent homology. Algebr Geom Topol 7(1): 339–358. https://doi.org/10.2140/agt.2007.7.339. [CrossRef] [Google Scholar]
- Demoulin P, Henoux JC, Priest ER, Mandrini CH. 1996. Quasi-separatrix layers in solar fares.I. Method. A&A 308: 643–655. [Google Scholar]
- DeRosa ML, Schrijver CJ, Barnes G, Leka KD, Lites BW, et al. 2009. A critical assessment of nonlinear force-free field modeling of the solar corona for active region 10953. Astrophys J 696(2): 1780–1791. https://doi.org/10.1088/0004-637x/696/2/1780. [NASA ADS] [CrossRef] [Google Scholar]
- Devogele M, Rivet JP, Tanga P, Bendjoya P, Surdej J, Bartczak P, Hanuš J. 2015. A method to search for large-scale concavities in asteroid shape models. Mon Notic Roy Astron Soc 453: 2232–2240. https://doi.org/10.1093/mnras/stv1740. [CrossRef] [Google Scholar]
- Duchi J, Hazan E, Singer Y. 2011. Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12(null): 2121–2159. https://dl.acm.org/doi/10.5555/1953048.2021068. [Google Scholar]
- Edelsbrunner H, Letscher D, Zomorodian A. 2000. Topological persistence and simplification. Discrete Comput Geom 28: 511–533. https://doi.org/10.1007/s00454-002-2885-2. [Google Scholar]
- Florios K, Kontogiannis I, Park S-H, Guerra JA, Benvenuto F, Bloomfield DS, Georgoulis MK. 2018. Forecasting solar flares using magnetogram-based predictors and machine learning. Sol Phys 293(2): 28. https://doi.org/10.1007/s11207-018-1250-4. [Google Scholar]
- Forrest AR. 1971. II. Current Developments in the Design and Production of Three-Dimensional Curved Objects – Computational Geometry. Proc Roy Soc Lond A: Math Phys Eng Sci 321(1545): 187–195. https://doi.org/10.1098/rspa.1971.0025. [Google Scholar]
- Gallagher P, Moon Y-J, Wang H. 2002. Active-region monitoring and flare forecasting – I. Data processing and first results. Sol Phys 209: 171–183. https://doi.org/10.1023/A:1020950221179. [NASA ADS] [CrossRef] [Google Scholar]
- Ghrist R. 2008. Barcodes: The persistent topology of data. Bull Am Math Soc 45(1): 61–75. https://doi.org/10.1090/S0273-0979-07-01191-3. [CrossRef] [Google Scholar]
- Glorot X., Bengio Y. 2010. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Vol. 9 of Proceedings of Machine Learning Research, Teh Y.W., Titterington M. (Eds.), PMLR, Chia Laguna Resort, Sardinia, Italy, pp. 249–256. https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.207.2059. [Google Scholar]
- Gu Q., Li, Z., Han, J. 2011. Generalized Fisher score for feature selection. In: Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI’11, AUAI Press, Arlington, Virginia, USA, pp. 266–273. https://dl.acm.org/doi/abs/10.5555/3020548.3020580. [Google Scholar]
- Guerra JA, Park S-H, Gallagher PT, Kontogiannis I, Georgoulis MK, Bloomfield DS. 2018. Active region photospheric magnetic properties derived from line-of-sight and radial fields. Sol Phys 293(1): 9. https://doi.org/10.1007/s11207-017-1231-z. [CrossRef] [Google Scholar]
- Hale GE, Ellerman F, Nicholson SB, Joy AH. 1919. The magnetic polarity of sun-spots. Astrophys J 49: 153. https://doi.org/10.1086/142452. [CrossRef] [Google Scholar]
- Haykin S. 1998. Neural networks: A comprehensive foundation, 2nd edn., Prentice Hall PTR, USA. https://dl.acm.org/doi/book/10.5555/521706. [Google Scholar]
- Huang X, Wang H, Xu L, Liu J, Li R, Dai X. 2018. Deep learning based solar flare forecasting model. I. Results for line-of-sight magnetograms. Astrophys J 856(1): 7. https://doi.org/10.3847/1538-4357/aaae00. [Google Scholar]
- Jonas E, Bobra M, Shankar V, Todd Hoeksema J, Recht B. 2018. Flare prediction using photospheric and coronal image data. Sol Phys 293(3): 48. https://doi.org/10.1007/s11207-018-1258-9. [Google Scholar]
- Kaczynski T., Mischaikow K., Mrozek M. 2004. Computational homology, Vol. 157 of Applied Mathematical Sciences, Springer-Verlag, New York. https://doi.org/10.1007/B97315. [CrossRef] [Google Scholar]
- Kingma D.P., Ba J. 2014. Adam: A method for stochastic optimization. arXiv e-prints. arXiv:1412.6980. [Google Scholar]
- Knyazeva IS, Makarenko NG, Livshits MA. 2011. Detection of new emerging magnetic flux from the topology of SOHO/MDI magnetograms. Astron Rep 55(5): 463. https://doi.org/10.1134/S1063772911050040. [CrossRef] [Google Scholar]
- Knyazeva IS, Urtiev FA, Makarenko NG. 2017. On the prognostic efficiency of topological descriptors for magnetograms of active regions. Geomagn Aeron 57(8): 1086–1091. https://doi.org/10.1134/S0016793217080126. [CrossRef] [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): 96. https://doi.org/10.1007/s11207-018-1317-2. [CrossRef] [Google Scholar]
- Kusano G.,Fukumizu, K., Hiraoka, Y. 2016. Persistence weighted Gaussian Kernel for topological data analysis. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning – Volume 48, ICML’16, 2004–2013. PMLR, New York, New York, USA. https://dl.acm.org/doi/10.5555/3045390.3045602. [Google Scholar]
- Le T.,Yamada, M. 2018. Persistence Fisher Kernel: A Riemannian manifold Kernel for persistence diagrams. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, 10028–10039, Curran Associates Inc., Red Hook, NY, USA. https://dl.acm.org/doi/10.5555/3327546.3327666. [Google Scholar]
- Leka KD, Park S-H, Kusano K, Andries J, Barnes G, et al. 2019a. A comparison of flare forecasting methods. II. Benchmarks, metrics, and performance results for operational solar flare forecasting systems. Astrophys J Suppl Ser 243(2): 36. https://doi.org/10.3847/1538-4365/ab2e12. [CrossRef] [Google Scholar]
- Leka KD, Park S-H, Kusano K, Andries J, Barnes G, et al. 2019b. A comparison of flare forecasting methods. III. Systematic behaviors of operational solar flare forecasting systems. Astrophys J 881(2): 101. https://doi.org/10.3847/1538-4357/ab2e11. [CrossRef] [Google Scholar]
- Longcope DW. 2005. Topological methods for the analysis of solar magnetic fields. Liv Rev Sol Phys 2(1): 7. https://doi.org/10.12942/lrsp-2005-7. [Google Scholar]
- Makarenko N, Malkova D, Machin M, Knyazeva I, Makarenko I. 2014. Methods of computational topology for the analysis of dynamics of active regions of the sun. J Math Sci 203(6): 806–815. https://doi.org/10.1007/s10958-014-2170-y. [CrossRef] [Google Scholar]
- McAteer RTJ, Gallagher PT, Conlon PA. 2010. Turbulence, complexity, and solar flares. Adv Space Res 45: 1067–1074. https://doi.org/10.1016/j.asr.2009.08.026. [NASA ADS] [CrossRef] [Google Scholar]
- McIntosh PS. 1990. The classification of sunspot groups. Sol Phys 125: 251–267. https://doi.org/10.1007/BF00158405. [NASA ADS] [CrossRef] [Google Scholar]
- Metcalf TR, DeRosa ML, Schrijver CJ, Barnes G, van Ballegooijen AA, Wiegelmann T, Wheatland MS, Valori G, McTiernan JM. 2008. Nonlinear force-free modeling of coronal magnetic fields. II. Modeling a filament arcade and simulated chromospheric and photospheric vector fields. Sol Phys 247(2): 269–299. https://doi.org/10.1007/s11207-007-9110-7. [NASA ADS] [CrossRef] [Google Scholar]
- Nishizuka N, Sugiura K, Kubo Y, Den M, Watari S, Ishii M. 2017. Solar flare prediction model with three machine-learning algorithms using ultraviolet brightening and vector magnetograms. Astrophys J 835: 156. https://doi.org/10.3847/1538-4357/835/2/156. [CrossRef] [Google Scholar]
- Nishizuka N, Sugiura K, Kubo Y, Den M, Ishii M. 2018. Deep flare net (DeFN) model for solar flare prediction. Astrophys J 858: 113. https://doi.org/10.3847/1538-4357/aab9a7. [NASA ADS] [CrossRef] [Google Scholar]
- Park E, Moon Y-J, Shin S, Yi K, Lim D, Lee H, Shin G. 2018. Application of the deep convolutional neural network to the forecast of solar flare occurrence using full-disk solar magnetograms. Astrophys J 869(2): 91. https://doi.org/10.3847/1538-4357/aaed40. [CrossRef] [Google Scholar]
- Paszke A, Gross S, Massa F, Lerer A, Bradbury J, et al. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems, Vol. 32, Curran Associates Inc. pp. 8026–8037. http://papers.nips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library [Google Scholar]
- Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, et al. 2011. Scikit-learn: Machine learning in python. J Mach Learn Res 12(null): 2825–2830. https://dl.acm.org/doi/10.5555/1953048.2078195. [Google Scholar]
- Pesnell WD, Thompson BJ, Chamberlin PC. 2011. The solar dynamics observatory (SDO). Sol Phys 275(1–2): 3–15. https://doi.org/10.1007/s11207-011-9841-3. [Google Scholar]
- Preparata FP, Shamos MI. 1985. Computational geometry: An introduction, Springer-Verlag, New York. https://doi.org/10.1007/978-1-4612-1098-6. [CrossRef] [Google Scholar]
- Priest ER, Démoulin P. 1995. Three-dimensional magnetic reconnection without null points. 1. Basic theory of magnetic flipping. J Geophys Res 100: 23443–23464. https://doi.org/10.1029/95JA02740. [Google Scholar]
- Reininghaus J., Huber S., Bauer U., Kwitt R. 2015. A stable multi-scale kernel for topological machine learning. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4741–4748. https://doi.org/10.1109/CVPR.2015.7299106. [Google Scholar]
- Robins V, Meiss J, Bradley E. 1998. Computing connectedness: An exercise in computational topology. Nonlinearity 11(4): 913–922. [CrossRef] [Google Scholar]
- Robins V, Meiss J, Bradley E. 2000. Computing connectedness: Disconnectedness and discreteness. Phys D 139(3–4): 276–300. [NASA ADS] [CrossRef] [Google Scholar]
- Scherrer PH, Schou J, Bush RI, Kosovichev AG, Bogart RS, et al. 2011. The helioseismic and magnetic imager (HMI) investigation for the solar dynamics observatory (SDO). Sol Phys 275(1–2): 207–227. https://doi.org/10.1007/s11207-011-9834-2. [Google Scholar]
- Schrijver CJ. 2007. A characteristic magnetic field pattern associated with all major solar flares and its use in flare forecasting. Astrophys J Lett 655(2): L117. https://doi.org/10.1086/511857. [CrossRef] [Google Scholar]
- Schrijver CJ. 2016. The nonpotentiality of coronae of solar active regions, the dynamics of the surface magnetic field, and the potential for large flares. Astrophys J 820(2): 1–17. https://doi.org/10.3847/0004-637x/820/2/103. [CrossRef] [Google Scholar]
- Schrijver CJ, DeRosa ML, Metcalf TR, Liu Y, McTiernan J, Régnier S, Valori G, Wheatland MS, Wiegelmann T. 2006. Nonlinear force-free modeling of coronal magnetic fields part I: A quantitative comparison of methods. Sol Phys 235(1–2): 161–190. https://doi.org/10.1007/s11207-006-0068-7. [NASA ADS] [CrossRef] [Google Scholar]
- Schrijver CJ, DeRosa ML, Metcalf T, Barnes G, Lites B, et al. 2008. Nonlinear force-free field modeling of a solar active region around the time of a major flare and coronal mass ejection. Astrophys J 675: 1637–1644. https://doi.org/10.1086/527413. [NASA ADS] [CrossRef] [Google Scholar]
- Singh G, Memoli F, Ishkhanov T, Sapiro G, Carlsson G, Ringach D. 2008. Topological analysis of population activity in visual cortex. J Vis 8(8: (11)): 1–18. https://doi.org/10.1167/8.8.11. [CrossRef] [Google Scholar]
- Tarr L, Longcope D. 2012. Calculating energy storage due to topological changes in emerging active region NOAA AR 11112. Astrophys J 749(1): 64. https://doi.org/10.1088/0004-637x/749/1/64. [CrossRef] [Google Scholar]
- Tarr L, Longcope D, Millhouse M. 2013. Calculating separate magnetic free energy estimates for active regions producing multiple flares: NOAA AR11158. Astrophys J 770(1): 4. https://doi.org/10.1088/0004-637X/770/1/4. [CrossRef] [Google Scholar]
- Topaz CM, Ziegelmeier L, Halverson T. 2015. Topological data analysis of biological aggregation models. PLoS One 10(5): 1–26. https://doi.org/10.1371/journal.pone.0126383. [CrossRef] [Google Scholar]
- Wang YM, Sheeley NR Jr. 1992. On potential field models of the solar corona. Astrophys J 392: 310. https://doi.org/10.1086/171430. [Google Scholar]
- Wheatland MS. 2004. A Bayesian approach to solar flare prediction. Astrophys J 609: 1134–1139. https://doi.org/10.1086/421261. [Google Scholar]
- Wiegelmann T, Sakurai T. 2012. Solar force-free magnetic fields. Liv Rev Sol Phys 9: 5. https://doi.org/10.12942/lrsp-2012-5. [Google Scholar]
- Woodcock F. 1976. The evaluation of yes/no forecasts for scientific and administrative purposes. Mon Weather Rev 104(10): 1209–1214. https://doi.org/10.1175/1520-0493(1976)104<1209:TEOYFF>2.0.CO;2. [CrossRef] [Google Scholar]
- Xu X, Cisewski-Kehe J, Green SB, Nagai D. 2019. Finding cosmic voids and filament loops using topological data analysis. Astron Comput 27: 34–52. https://doi.org/10.1016/j.ascom.2019.02.003. [CrossRef] [Google Scholar]
- Yang X, Lin G, Zhang H, Mao X. 2013. Magnetic nonpotentiality in photospheric active regions as a predictor of solar flares. Astrophys J 774(2): L27. https://doi.org/10.1088/2041-8205/774/2/l27. [CrossRef] [Google Scholar]
- Yu D, Huang X, Wang H, Cui Y, Hu Q, Zhou R. 2010. Short-term solar flare level prediction using a Bayesian network approach. Astrophys J 710(1): 869. https://doi.org/10.1088/0004-637x/710/1/869. [Google Scholar]
- Yuan Y, Shih F, Jing J, Wang H. 2010. Solar flare forecasting using sunspot-groups classification and photospheric magnetic parameters. Proc Int Astron Union 6: 446–450. https://doi.org/10.1017/S1743921311015742. [CrossRef] [Google Scholar]
- Zheng Y, Li X, Wang X. 2019. Solar flare prediction with the hybrid deep convolutional neural network. Astrophys J 885(1): 73. https://doi.org/10.3847/1538-4357/ab46bd. [CrossRef] [Google Scholar]
- Zomorodian A. 2012. Topological data analysis, vol. 70 of Advances in Applied and Computational Topology, American Mathematical Society, Providence. https://doi.org/10.1090/psapm/070. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.