European Solar Physics Online Seminar Archive

The ubiquitous presence of small magnetic elements in the Quiet Sun represents a prominent coupling between the photosphere and the upper layers of the Sun’s atmosphere. Small magnetic element tracking has been widely used to study the transport and diffusion of the magnetic field on the solar photosphere. From the analysis of the displacement spectrum of these tracers, it has been recently agreed that a regime of super-diffusivity dominates the solar surface. In this talk we will focus on the analysis of the bipolar magnetic pairs in the solar photosphere and their diffusion properties, using a 25-h dataset from the HINODE satellite. Interestingly, the displacement spectrum for bipolar couples behaves similarly to the case where all magnetic pairs are considered. We also measure, from the same dataset, the magnetic emergence rate of the bipolar magnetic pairs and we interpret them as the magnetic footpoints of emerging magnetic loops. The measured magnetic emergence rate is used to constrain a simplified model that mimics the advection on the solar surface and evolves the position of a great number of loops, taking into account emergence, reconnection and cancellation events. In particular we compute the energy released by the reconnection between different magnetic loops in the nano-flares energy range. Our model gives a quantitative estimate of the energy released by the reconfiguration of the magnetic loops in a quiet Sun area as a function of height in the solar atmosphere, from hundreds of Km above the photosphere up to the corona, suggesting that an efficiency of ~10% in the energy deposition might sustain the million degree corona. [more]

Solar flares originate from active regions (ARs) hosting complex and strong bipolar magnetic fluxes. Forecasting the probability of an AR to flare and defining reliable precursors of intense flares, i.e., X- or M-class flares, are extremely challenging tasks in the space weather field. In this talk, we focus on two metrics as flare precursors, the unsigned flux R*, tested on MDI/SOHO data and calibrated for higher spatial resolution SDO/HMI maps, and a novel topological parameter D representing the complexity of a solar active region. The parameter D is based on the automatic recognition of magnetic polarity inversion lines (PILs) in identified SDO/HMI ARs and is able to evaluate their magnetic topological complexity. We use both a heuristic approach and a supervised machine-learning method to validate the effectiveness of these metrics to predict the occurrence of X- or M-class flares in a given solar AR during the following 24 hr period. Our feature ranking analysis shows that both parameters play a significant role in prediction performances. Moreover, the analysis demonstrates that the new topological parameter D is the only one, among 173 overall predictors, that is systematically ranked within the top 10 positions. [more]