D. Shaun Bloomfield 1,2, K. Domijan 3, P. A. Higgins 2, P. T

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Solar Flare Forecasting from Magnetic Feature Properties Generated by the SMART Algorithm D. Shaun Bloomfield 1,2, K. Domijan 3, P.A. Higgins 2, P.T. Gallagher 2 1 Northumbria University, UK 2 Trinity College Dublin, Ireland 3 NUI Maynooth, Ireland This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 640216 ESWW13 – 17th Nov 2016

Data Source Solar Monitor Active Region Tracking (SMART) algorithm SOHO/MDI magnetic feature (MF) finder similar in concept to SDO/HMI (S)HARP cut-outs Extracts ~25 magnetic properties for each MF association with GOES >C1.0 NOAA numbered regions and ephemeral/unspotted regions

Training and Testing Data Direct comparison to Ahmed et al. (2013) Sol. Phys., 283, 157 Training (10.75 years): Apr 1996 – Dec 2000 ; Jan 2003 – Dec 2008 Testing (4 years): Jan 2001 – Dec 2002 ; Jan 2009 – Dec 2010 Marginal relevance scores found from training set total length of neutral lines (Lnl) max. horizontal gradient of vertical field across neutral line (Mx_Grad) All-MF NOAA-only Training Testing > C1.0 within 24 hr 16,673 10,571 1,137 707 No-flare or < C1.0 313,617 177,380 5,272 2,789

Classification Rule Construction Random draw of 100 flare (red) and 300 non-flare (black) MFs Build linear logistic regression classifier – sigmoid surface

Classification Rule Variation Repeat previous classifier construction 50 times All-MF case has many zero values (no neutral lines in MF detection) NOAA-only case boundaries much more variable

Forecast Application Categorical forecasts reached by thresholding a classification rule SMART MFs checked against GOES >C1.0 flares within 24 hr Contingency tables drawn up to find categorical skill scores All 50 classification rules applied to every test data point i.e., 50 contingency tables at each classifier threshold value Forecast Flare No-flare Observed TP FN FP TN TSS= TP FN+TP − FP FP+TN HSS= TP+TN − E random N− E random

Forecast Performance All-MFs TSS Classifier Threshold Classif. Thresh. (NOAA) HSS 0.05 0.78 0.29 0.10 0.82 0.37 0.15 0.83 0.42 0.20 0.45 0.25 0.48 0.30 0.81 0.51 0.35 0.80 0.53 0.40 0.55 0.76 0.56 0.50 0.75 0.57 0.73 0.59 0.60 0.70 0.65 0.68 0.61 0.62 0.85 0.90 0.58 0.95 0.44 0.52 All-MFs TSS median 2.5th → 97.5th percentiles Classifier Threshold

Forecast Performance All-MFs TSS NOAA-only TSS Classifier Threshold HSS 0.05 0.78 0.09 0.29 0.13 0.10 0.82 0.50 0.37 0.34 0.15 0.83 0.61 0.42 0.46 0.20 0.65 0.45 0.53 0.25 0.64 0.48 0.56 0.30 0.81 0.62 0.51 0.58 0.35 0.80 0.59 0.40 0.57 0.55 0.76 0.54 0.75 0.73 0.60 0.70 0.68 0.39 0.52 0.36 0.32 0.47 0.85 0.28 0.44 0.90 0.24 0.38 0.95 0.18 0.33 All-MFs TSS median 2.5th → 97.5th percentiles NOAA-only TSS Classifier Threshold

Ordinal log. regression Conclusions Data Forecast method TSS Flare level Reference All-MF Log. regression 0.83 >C1.0 This work Neural network 0.64 Ahmed et al. (2013) NOAA-only 0.65 Ordinal log. regression C-class Song et al. (2009) NOAA/SWPC (human) 0.57 Crown (2012) NOAA/SWPC (look-up) 0.45 McIntosh-Poisson 0.46 Bloomfield et al. (2012) Very good / good performance compared to literature No forecast improvement with more complicated models >2 parameter logistic regression linear classifiers on lower-dimensional projections (e.g., PCA, KPCA) non-linear classifiers (e.g., SVM, GP)