On the Prognostic Efficiency of Topological Descriptors for Magnetograms of Active Regions
Tóm tắt
Solar flare prediction remains an important practical task of space weather. An increase in the amount and quality of observational data and the development of machine-learning methods has led to an improvement in prediction techniques. Additional information has been retrieved from the vector magnetograms; these have been recently supplemented by traditional line-of-sight (LOS) magnetograms. In this work, the problem of the comparative prognostic efficiency of features obtained on the basis of vector data and LOS magnetograms is discussed. Invariants obtained from a topological analysis of LOS magnetograms are used as complexity characteristics of magnetic patterns. Alternatively, the so-called SHARP parameters were used; they were calculated by the data analysis group of the Stanford University Laboratory on the basis of HMI/SDO vector magnetograms and are available online at the website (http://jsoc.stanford.edu/) with the solar dynamics observatory (SDO) database for the entire history of SDO observations. It has been found that the efficiency of large-flare prediction based on topological descriptors of LOS magnetograms in epignosis mode is at least s no worse than the results of prognostic schemes based on vector features. The advantages of the use of topological invariants based on LOS data are discussed.
Tài liệu tham khảo
Barnes, G., Leka, K.D., Schrijver, C.J., et al., A comparison of flare forecasting methods. I. Results from the “all-clear” workshop, Astrophys. J., 2016, vol. 829, no. 2, id 89.
Baryshnikov, Y. and Ghrist, R., Euler integration over definable functions, Proc. Natl. Acad. Sci., 2010, vol. 107, no. 21, pp. 9525–9530.
Bloomfield, D.Sh., Higgins, P.A., McAteer, R.T.J., and Gallagher, P.T., Toward reliable benchmarking of solar flare forecasting methods, Astrophys. J. Lett., 2012, vol. 747, no. 2, L41.
Bobra, M.G. and Couvidat, S., Solar flare prediction using SDO/HMI vector magnetic field data with a machinelearning algorithm, Astrophys. J., 2015, vol. 798, id 135.
Bobra, M.G. and Ilonidis, S., Predicting coronal mass ejections using machine learning methods, Astrophys. J., 2016, vol. 821, no. 2, id 127.
Bobra, M.G., Sun, X., Hoeksma, J.T., et al., The Helioseismic and Magnetic Imager (HMI) vector magnetic field pipeline: SHARPs—space-weather HMI active region patches, Sol. Phys., 2014, vol. 289, no. 9, pp. 3549–3678.
Boucheron, L.E., Al-Ghraibah, A., and McAteer, R.T.J., Prediction of solar flare size and time-to-flare using support vector machine regression, Astrophys. J., 2015, vol. 812, no. 1, id 51.
Carlsson, G., Topology and data, Bull. Am. Math. Soc., 2009, vol. 46, no. 2, pp. 255–308.
Edelsbrunner, H. and Harer, J., Computational Topology: An Introduction, Providence, RI: Am. Math. Soc., 2010.
Ghrist, R., Barcodes: The persistent topology of data, Bull. Am. Math. Soc., 2008, vol. 45, no. 1, pp. 61–75.
Karimova, L.M., Kruglin, O.A., Makarenko, N.G., and Romanova, N.V., Power law distribution in statistics of failures in operation of spacecraft onboard equipment, Cosmic Res., 2011, vol. 49, no. 5, pp. 458–463.
Knyazeva, I.S., Makarenko, N.G., and Urt’ev, F.A., Comparison of the dynamics of active regions by methods of computational topology, Geomagn. Aeron. (Engl. Transl.), 2015, vol. 55, no. 8, pp. 1134–1140.
Leka, K.D. and Barnes, G., Photospheric magnetic field properties of flaring versus flare-quiet active regions, Astrophys. J., 2007, vol. 656, no. 2, pp. 1173–1186.
Makarenko, N.G., et al., Methods of computational topology for the analysis of dynamics of active regions of the Sun, Fundam. Prikl. Mat., 2013, vol. 18, no. 2, pp. 79–93.
Qahwaji, R. and Colak, T., Automatic short-term solar flare prediction using machine learning and sunspot associations, Sol. Phys., 2007, vol. 241, pp. 195–211.
Robins, V. and Turner, K., Principal component analysis of persistent homology rank functions with case studies of spatial point patterns, sphere packing and colloids, Phys. D (Amsterdam, Neth.), 2016, vol. 334, pp. 99–117.
Serra, J., Image Analysis and Mathematical Morphology, Orlando: Academic, 1982, vol. 1.
Sharma, A.S., Baker, D.N., Bhattacharyya, A., et al., Complexity and extreme events in geosciences: An overview, in Extreme Events and Natural Hazards: The Complexity Perspective, Washington, D.C.: AGU, 2012, pp. 1–16.
Smith, J.B., Jr., Predicting activity levels for specific locations within solar active regions, Solar Activity Observations and Predictions, McIntosh, P.S. and Dryer, D., Eds., Cambridge: Mass. Inst. Technol., 1972, pp. 429–442.
Takens, F., Chaos, in Structures in Dynamics, Finite Dimensional Deterministic Studies, van Groesen, E. and de Jager, E.M., Eds., Amsterdam: Elsevier, 1991, pp. 97–110.