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Interferometric imaging of the type IIIb and U radio bursts observed with LOFAR on 22 August 2017
Context. The Sun is the source of different types of radio bursts that are associated with solar flares, for example. Among the most frequently observed phenomena are type III solar bursts. Their radio images at low frequencies (below 100 MHz) are relatively poorly studied due to the limitations of legacy radio telescopes. Aims. We study the general characteristics of types IIIb and U with stria structure solar radio bursts in the frequency range of 20-80 MHz, in particular the source size and evolution in different altitudes, as well as the velocity and energy of electron beams responsible for their generation. Methods. In this work types IIIb and U with stria structure radio bursts are analyzed using data from the LOFAR telescope including dynamic spectra and imaging observations, as well as data taken in the X-ray range (GOES and RHESSI satellites) and in the extreme ultraviolet (SDO satellite). Results. In this study we determined the source size limited by the actual shape of the contour at particular frequencies of type IIIb and U solar bursts in a relatively wide frequency band from 20 to 80 MHz. Two of the bursts seem to appear at roughly the same place in the studied active region and their source sizes are similar. It is different in the case of another burst, which seems to be related to another part of the magnetic field structure in this active region. The velocities of the electron beams responsible for the generation of the three bursts studied here were also found to be different.
The flare likelihood and region eruption forecasting (FLARECAST) project: flare forecasting in the big data & machine learning era
The European Union funded the FLARECAST project, that ran from January 2015 until February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different machine learning techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple machine-learning algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels, at least for major flares. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic.