The heating of the solar chromosphere and corona is a long-standing puzzle in solar physics. Hinode observations show the ubiquitous presence of chromospheric anemone jets outside sunspots in active regions. They are typically 3 to 7 arc seconds = 2000 to 5000 kilometers long and 0.2 to 0.4 arc second = 150 to 300 kilometers wide, and their velocity is 10 to 20 kilometers per second. These small jets have an inverted Y-shape, similar to the shape of x-ray anemone jets in the corona. These features imply that magnetic reconnection similar to that in the corona is occurring at a much smaller spatial scale throughout the chromosphere and suggest that the heating of the solar chromosphere and corona may be related to small-scale ubiquitous reconnection.

Shortly after the seminal paper "Self-Organized Criticality: An explanation of 1/f noise" by Bak et al. (1987), the idea has been applied to solar physics, in "Avalanches and the Distribution of Solar Flares" by Lu and Hamilton (1991). In the following years, an inspiring cross-fertilization from complexity theory to solar and astrophysics took place, where the SOC concept was initially applied to solar flares, stellar flares, and magnetospheric substorms, and later extended to the radiation belt, the heliosphere, lunar craters, the asteroid belt, the Saturn ring, pulsar glitches, soft X-ray repeaters, blazars, black-hole objects, cosmic rays, and boson clouds. The application of SOC concepts has been performed by numerical cellular automaton simulations, by analytical calculations of statistical (powerlaw-like) distributions based on physical scaling laws, and by observational tests of theoretically predicted size distributions and waiting time distributions. Attempts have been undertaken to import physical models into the numerical SOC toy models, such as the discretization of magneto-hydrodynamics (MHD) processes. The novel applications stimulated also vigorous debates about the discrimination between SOC models, SOC-like, and non-SOC processes, such as phase transitions, turbulence, random-walk diffusion, percolation, branching processes, network theory, chaos theory, fractality, multi-scale, and other complexity phenomena. We review SOC studies from the last 25 years and highlight new trends, open questions, and future challenges, as discussed during two recent ISSI workshops on this theme

Hinode discovered a beautiful giant jet with both cool and hot components at the solar limb on 2007 February 9. Simultaneous observations by the Hinode SOT, XRT, and TRACE 195Å satellites revealed that hot (∼ 5 × 10 6 K) and cool (∼ 10 4 K) jets were located side by side and that the hot jet preceded the associated cool jet (∼ 1 − 2 min.). A current-sheet-like structure was seen in optical (Ca II H), EUV (195Å), and soft X-ray emissions, suggesting that magnetic reconnection is occurring in the transition region or upper chromosphere. Alfvén waves were also observed with Hinode SOT. These propagated along the jet at velocities of ∼ 200 km s −1 with amplitudes (transverse velocity) of ∼5-15 km s −1 and a period of ∼ 200s. We performed two-dimensional MHD simulation of the jets on the basis of the emerging flux -reconnection model, by extending Yokoyama and Shibata's model. We extended the model with a more realistic initial condition (∼ 10 6 K corona) and compared our model with multi-wavelength observations. The improvement of the coronal temperature and density in the simulation model allowed for the first time the reproduction of the structure and evolution of both the cool and hot jets quantitatively, supporting the magnetic reconnection model. The generation and the propagation of Alfvén waves are also reproduced self-consistently in the simulation model.

We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010-2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.

We developed a solar flare prediction model using a deep neural network (DNN), named Deep Flare Net (DeFN). The model can calculate the probability of flares occurring in the following 24 h in each active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., ≥M class versus

Aims. A broad jet was observed in a weak magnetic field area at the edge of active region NOAA 11106 that also produced other nearby recurring and narrow jets. The peculiar shape and magnetic environment of the broad jet raised the question of whether it was created by the same physical processes of previously studied jets with reconnection occurring high in the corona. Methods. We carried out a multi-wavelength analysis using the EUV images from the Atmospheric Imaging Assembly (AIA) and magnetic fields from the Helioseismic and Magnetic Imager (HMI) both on-board the Solar Dynamics Observatory, which we coupled to a high-resolution, nonlinear force-free field extrapolation. Local correlation tracking was used to identify the photospheric motions that triggered the jet, and time-slices were extracted along and across the jet to unveil its complex nature. A topological analysis of the extrapolated field was performed and was related to the observed features. Results. The jet consisted of many different threads that expanded in around 10 minutes to about 100 Mm in length, with the bright features in later threads moving faster than in the early ones, reaching a maximum speed of about 200 km s −1 . Time-slice analysis revealed a striped pattern of dark and bright strands propagating along the jet, along with apparent damped oscillations across the jet. This is suggestive of a (un)twisting motion in the jet, possibly an Alfvén wave. Bald patches in field lines, low-altitude flux ropes, diverging flow patterns, and a null point were identified at the basis of the jet. Conclusions. Unlike classical λ or Eiffel-tower-shaped jets that appear to be caused by reconnection in current sheets containing null points, reconnection in regions containing bald patches seems to be crucial in triggering the present jet. There is no observational evidence that the flux ropes detected in the topological analysis were actually being ejected themselves, as occurs in the violent phase of blowout jets; instead, the jet itself may have gained the twist of the flux rope(s) through reconnection. This event may represent a class of jets different from the classical quiescent or blowout jets, but to reach that conclusion, more observational and theoretical work is necessary.

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