An automated classification approach to ranking photospheric proxies of magnetic energy build-up

Abstract

Aims. We study the photospheric magnetic field of ∼2000 active regions over solar cycle 23 to search for parameters that may be indicative of energy build-up and its subsequent release as a solar flare in the corona. Methods. We extract three sets of parameters: (1) snapshots in space and time: total flux, magnetic gradients, and neutral lines; (2) evolution in time: flux evolution; and (3) structures at multiple size scales: wavelet analysis. This work combines standard pattern recognition and classification techniques via a relevance vector machine to determine (i.e., classify) whether a region is expected to flare (≥C1.0 according to GOES). We consider classification performance using all 38 extracted features and several feature subsets. Classification performance is quantified using both the true positive rate (the proportion of flares correctly predicted) and the true negative rate (the proportion of non-flares correctly classified). Additionally, we compute the true skill score which provides an equal weighting to true positive rate and true negative rate and the Heidke skill score to allow comparison to other flare forecasting work. Results. We obtain a true skill score of ∼0.5 for any predictive time window in the range 2 to 24 h, with a true positive rate of ∼0.8 and a true negative rate of ∼0.7. These values do not appear to depend on the predictive time window, although the Heidke skill score (<0.5) does. Features relating to snapshots of the distribution of magnetic gradients show the best predictive ability over all predictive time windows. Other gradient-related features and the instantaneous power at various wavelet scales also feature in the top five (of 38) ranked features in predictive power. It has always been clear that while the photospheric magnetic field governs the coronal non-potentiality (and hence likelihood of producing a solar flare), photospheric magnetic field information alone is not sufficient to determine this in a unique manner. Furthermore we are only measuring proxies of the magnetic energy build up. We are still lacking observational details on why energy is released at any particular point in time. We may have discovered the natural limit of the accuracy of flare predictions from these large scale studies.

On the Non-Kolmogorov Nature of Flare-Productive Solar Active Regions

“…A few active regions tend to produce multiple, large flares, while many active regions evolve through their lifetimes without showing any considerable flare activity (Bloomfield et al 2012). This means that certain types of magnetic configuration may be more important in flare production (McAteer et al 2005;Schrijver 2007;Al-Ghraibah et al 2015). Detecting and understanding these differences in flare-productive active regions is essential to realize how and when solar flares occur (Barnes et al 2016).…”

A Comparison of Flare Forecasting Methods. I. Results From the “All-Clear” Workshop

“…As such, it is desirable to be able to predict when a solar flare event will occur and how large it will be prior to observing the flare itself. In the last decade, the number of published approaches to flare forecasting using photospheric magnetic field observations has proliferated, with widely varying evaluations about how well each works (e.g., Abramenko 2005;Jing et al 2006;McAteer et al 2005a;Schrijver 2007;Barnes & Leka 2008;Mason & Hoeksema 2010;Yu et al 2010;Yang et al 2013;Boucheron et al 2015;Al-Ghraibah et al 2015, in addition to references for each method described, below).…”

Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms

“…Features derived from the line-of-sight magnetogram are useful indicators for future flare prediction, such as the magnetic flux, the gradient of the magnetic field (Yu et al 2009;Steward et al 2011), the length of magnetic neutral lines (Steward et al 2011), the effective magnetic field (Georgoulis & Rust 2007;Papaioannou et al 2015), the unsigned magnetic flux near the magnetic neutral lines (Rvalue: Schrijver 2007;Falconer et al 2011), the total magnetic energy dissipation (Song et al 2009), the weighted magnetic neutral line length and the distance between NS polarity sunspot centers (Mason & Hoeksema 2010), the nonpotentiality (e.g., Falconer et al 2014), and the wavelet spectra (Yu et al 2010;Al-Ghraibah et al 2015;Boucheron et al 2015;Muranushi et al 2015). These features are related to the dynamics of flux emergence and are strongly correlated with the energy storage and the triggering mechanisms.…”