We describe here the application of a machine learning method for ﬂare forecasting using vectors of properties extracted from images provided by the Helioseismic and Magnetic Imager in the Solar Dynamics Observatory (SDO/HMI). We also discuss how the method can be used to quantitatively assess the impact of such properties on the prediction process

Benvenuto, F.

Campi, C.

Guastavino, S.

Massone, A. M.

Piana, M.

arXiv

Piana M.

Campi C.

Benvenuto F.

Guastavino S.

Massone A. M.

Archivio istituzionale della ricerca - Università di Padova

Flare forecasting and feature ranking using SDO/HMI data

We describe here the application of a machine learning method for flare forecasting using vectors of properties extracted from images provided by the Helioseismic and Magnetic Imager in the Solar Dynamics Observatory (SDO/HMI). We also discuss how the method can be used to quantitatively assess the impact of such properties on the prediction process

Archivio istituzionale della ricerca - Università di Genova

DOI 10.1393/ncc/i2019-19030-1Colloquia: SoHe3 2018IL NUOVO CIMENTO 42 C (2019) 30Flare forecasting and feature ranking using SDO/HMI dataM. Piana(1)(3), C. Campi(2), F. Benvenuto(1), S. Guastavino(1)and A. M. Massone(1)(3)(1) Dipartimento di Matematica, Università di Genova - Genova, Italy(2) Dipartimento di Medicina, Università di Padova - Padova, Italy(3) CNR SPIN Genova - Genova, Italyreceived 28 December 2018Summary. — We describe here the application of a machine learning method forflare forecasting using vectors of properties extracted from images provided by theHelioseismic and Magnetic Imager in the Solar Dynamics Observatory (SDO/HMI).We also discuss how the method can be used to quantitatively assess the impact ofsuch properties on the prediction process.1. – IntroductionSolar flares, the most explosive phenomena in the heliosphere, may extend to over10, 000 km while releasing more than 1032 erg in less than 100 seconds, acceleratingbillions of tons of material to more than 106 km/h, emitting electromagnetic radiation atall wavelengths and, in this way, triggering the whole space weather connection. The fullcomprehension of solar (and stellar) flare physics is still an open issue, to such an extentthat we can talk about a sort of flare paradox: simple computation based on their physicaland geometrical properties and on magnetohydrodynamic (MHD) equations would leadto predict a light-up time for flares longer than 105 years, while the observed flash phasefor these mysterious events is of the order of some minutes.The numerical modelling of solar flare physics may rely on two different perspectives.On the one hand finite and boundary element methods applied against MHD partialdifferential equations allow the simulation of the electromagnetic fields and plasma prop-erties in time and space; on the other hand, artificial intelligence allows pattern identifi-cation in the data mess and both source reconstruction with inverse methods and flareprediction with machine learning. In the present paper we will outline how a hybrid su-pervised/unsupervised machine learning method [1] is able to utilize features extractedfrom experimental observations of active regions.Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0) 12 M. PIANA et al.2. – Setup of the problemThe data we consider in this paper are provided by the Helioseismic and MagneticImager in the payload of the Solar Dynamics Observatory (SDO/HMI). This telescopeprovides full disk vector magnetograms with a temporal cadence of 12 minutes, startingfrom february 2010. Relying on the Solar Monitor Active Region Tracker (SMART)tools and also using pattern recognition methods developed within the ’Horizon 2020’FLARECAST effort (http://flarecast.eu), the input data at disposal for the machinelearning analysis become feature vectors of dimension up to 171 characterizing propertiesof the active regions (ARs) present in the SDO/HMI maps. Therefore the ingredients ofour supervised approach are:• A historical data set of feature vectors extracted from SDO/HMI data stored inthe mission archive.• A set of labels, each one associated to a feature set and encoding the outcomeinformation, i.e. labels testifying the possible flare occurrence and intensity.• A computational machine learning method that is trained by means of the historical(training) set and the corresponding set of labels.When a new magnetogram arrives, the pattern recognition method extracts the featuresfrom it and the trained machine learning method both predicts the outcome correspond-ing to the new feature set and assesses the impact of each feature against the predictioneffectiveness.3. – The hybrid supervised/unsupervised machine learning methodThe method we used for both prediction and feature ranking [1] is based on a hybridsupervised/unsupervised approach in which the supervised phase computes the weightswith which each feature contributes to the prediction and the unsupervised phase providesan automatic, data-dependent threshold as a support for the binary prediction. Moreformally, we denote with X the set of N F−dimensional vectors representing the featurevectors at disposal in the training set. If y is the N−dimensional vector containing theobserved binary labels, the supervised step is realized by means of the LASSO approach[2], which requires the solution of the minimum problemβ̂ = arg minβ(‖y − Xβ‖22 + λ‖β‖1) .(1)The solution of this problem is the vector β̂ of feature weights, with which we canforward-compute theoretical labels by means ofŷ = Xβ̂ .(2)Then, we applied a fuzzy clustering technique [3] on the computed labels in order todetermine the centroids of the label-1 set (the flare occurs) and of the label-0 set (the flaredoes not occur). Therefore, when the new feature vector xnew arrives, xtnewβ̂ is comparedto the two centroids in order to decide the cluster to which it belongs. Further, the entriesof β̂ quantify the impact of each feature on the prediction task; therefore ranking themcorresponds to rank the prediction impacts.FLARE FORECASTING AND FEATURE RANKING USING SDO/HMI DATA 3Table I. – Skill scores obtained by the hybrid LASSO method described in this paper (HLA), ahybrid version of logit (HLO), a support vector machine for classification (SVC) and random for-est (RF). TSS stands for true skill statistics, HSS for Heidke skill score, ACC for accuracy, FARfor false alarm ratio, POD for probability of detection. The numbers correspond to the averagevalues over 100 realizations of the training set with the corresponding standard deviations.TSS HSS ACC FAR PODHLA 0.51 ± 0.04 0.54 ± 0.03 0.83 ± 0.01 0.29 ± 0.03 0.59 ± 0.04HLO 0.50 ± 0.03 0.53 ± 0.03 0.83 ± 0.01 0.30 ± 0.03 0.59 ± 0.04SVC 0.45 ± 0.03 0.50 ± 0.03 0.83 ± 0.01 0.25 ± 0.03 0.51 ± 0.04RF 0.49 ± 0.03 0.52 ± 0.03 0.83 ± 0.01 0.28 ± 0.03 0.56 ± 0.034. – ResultsWe assessed the effectiveness of this approach for flare prediction and feature rankingby considering point-in-time SDO/HMI images in the time range between 09/14/2012and 04/30/2016, with a time cadence of 24 hours. After the application of the patternrecognition step we had at disposal 4442 sets of 171−dimension feature vectors. Then thetraining set was realized by randomly extracting 66% of the overall set of feature vectorsand labeling each vector by annotating whether a flare with class at least C1 occurredin the next 24 hours (the same can be done for flares with class at least M1). The set ofthe remaining feature vectors were used as test set for the experiment. Table I explainsthe reliability of the prediction by comparing five standard skill scores [4] computed bymeans of the hybrid method and of other three machine learning methods [5], [6], [7].Then, Table II shows how these same methods are able to rank the features accordingto their impact on the prediction.5. – ConclusionsWe showed how machine learning can be used for both binary flare forecasting andproperty ranking in the case of vector magnetograms provided by SDO/HMI. In particu-lar, the ability to quantitatively assess the impact with which each property contributesto the prediction procedure has both physical and instrumental applications: high rankfeatures are most likely associated to crucial physical processes and instruments thatTable II. – Ranking of the properties extracted from ARs recognized in SDO/HMI magnetogramsas provided by the same machine learning methods considered in Table I. The numbers in thefirst four columns correspond to rank values computed from the weight vectors (1) and averagedover 100 random realizations of the test set. The last two columns contain the average of theprevious numbers over the four algorithm outcomes and the corresponding standard deviation,respectively.HLA HLO SVC RF average stdflare index past 13.98 28.84 19.91 3.51 16.56 10.63sharp kw/hgradbh/total 3.47 37 18.59 16.57 18.95 13.87wlsg br/value int 3.74 14.43 22.86 43.14 21.04 16.68sharp kw/jz/max 26.05 28 16.94 18.58 22.27 5.29sharp kw/usiz/max 24.2 36.75 34.79 18.37 28.53 8.734 M. PIANA et al.accurately observe properties with high rank are probably more worthwhile designing.The hybrid method described in this paper is currently implemented in the platformdeveloped within the ’Horizon 2020’ FLARECAST effort, together with other 15 bothsupervised and unsupervised algorithms. This service is accessible for open download athttp://flarecast.eu and is constantly updated also thanks to its highly modular design.∗ ∗ ∗The authors have been supported by the H2020 grant Flare Likelihood And RegionEruption foreCASTing (FLARECAST), project number 640216.REFERENCES[1] Benvenuto F., Piana M., Campi C. and Massone A. M., Astrophys. J., 853 (2018) 90.[2] Tibshirani R., J. R. Stat. Soc. B, 58 (1996) 267.[3] Bezdek J. C., Pattern Recognition with Fuzzy Objective Function Algorithms, (Kluwer,Norwell) 1981.[4] Bloomfield D. S., Higgins P. A., McAteer R. T. and Gallagher P. T., Astrophys.J., 747 (2012) L41.[5] Wu T. T., Chen Y. F., Hastie T., Sobel E. and Lange K., Bioinformatics, 25 (2009)714.[6] Cortes C. and Vapnik V., Mach. Learn., 20 (1995) 273.[7] Breiman L., Mach. Learn., 45 (2001) 5.

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Flare forecasting and feature ranking using SDO/HMI data

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