Nanoflares, the basic unit of impulsive energy release may produce much of the solar background emission. Extrapolation of the energy frequency distribution of observed microflares, which follows a power law to lower energies can give an estimation of the importance of nanoflares for heating the solar corona. If the power law index is greater than 2, then the nanoflare contribution is dominant. We model time series of extreme ultraviolet emission radiance, as random flares with a power law exponent of the flare event distribution. The model is based on three key parameters, the flare rate, the flare duration and the power law exponent of the flare intensity frequency distribution. We use this model to simulate emission line radiance detected in 171Å, observed by STEREO/EUVI and SDO/AIA. The Observed light curves are matched with simulated light curves using an Artificial Neural Network and parameter values are determined across regions of active region, quiet sun, and coronal hole. The damping rate of nanoflares is compared with radiative losses cooling time. The effect of background emission, data cadence, and network sensitivity on the key parameters of model is studied.Most of the observed light curves have a power law exponent, α, greater than the critical value 2. At these sites nanoflare heating could be significant.

Evidence for small amounts of very hot plasma has been found in active regions and might be the indication of an impulsive heating, released at spatial scales smaller than the cross section of a single loop. We investigate the heating and substructure of coronal loops in the core of one such active region by analyzing the light curves in the smallest resolution elements of solar observations in two EUV channels (94Å and 335Å) from the Atmospheric Imaging Assembly on-board the Solar Dynamics Observatory. We model the evolution of a bundle of strands heated by a storm of nanoflares by means of a hydrodynamic 0D loop model (EBTEL). The light curves obtained from the random combination of those of single strands are compared to the observed light curves either in a single pixel or in a row of pixels, simultaneously in the two channels and using two independent methods: an artificial intelligent system (Probabilistic Neural Network, PNN) and a simple cross-correlation technique. We explore the space of the parameters to constrain the distribution of the heat pulses, their duration and their spatial size, and, as a feedback on the data, their signatures on the light curves. From both methods the best agreement is obtained for a relatively large population of events (1000) with a short duration (less than 1 min) and a relatively shallow distribution (power law with index 1.5) in a limited energy range (1.5 decades). The feedback on the data indicates that bumps in the light curves, especially in the 94Å channel, are signatures of a heating excess that occurred a few minutes before.

A previous work of ours found the best agreement between EUV light curves observed in an active region core (with evidence of super-hot plasma) and those predicted from a model with a random combination of many pulseheated strands with a power-law energy distribution. We extend that work by including spatially resolved strand modeling and by studying the evolution of emission along the loops in the EUV 94 Å and 335 Å channels of the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory. Using the best parameters of the previous work as the input of the present one, we find that the amplitude of the random fluctuations driven by the random heat pulses increases from the bottom to the top of the loop in the 94 Å channel and from the top to the bottom in the 335 Å channel. This prediction is confirmed by the observation of a set of aligned neighboring pixels along a bright arc of an active region core. Maps of pixel fluctuations may therefore provide easy diagnostics of nanoflaring regions.

The solar atmosphere is known to be replete with magneto-hydrodynamic wave modes, and there has been significant investment in understanding how these waves propagate through the Sun’s atmosphere and deposit their energy into the plasma. The waves’ journey is made interesting by the vertical variation in plasma quantities that define the solar atmosphere. In addition to this large-scale inhomogeneity, a wealth of fine-scale structure through the chromosphere and corona has been brought to light by high-resolution observations over the last couple of decades. This fine-scale structure represents inhomogeneity that is thought to be perpendicular to the local magnetic fields. The implications of this form of inhomogeneity on wave propagation is still being uncovered, but is known to fundamentally change the nature of MHD wave modes. It also enables interesting physics to arise including resonances, turbulence and instabilities. Here, we review some of the key insights into how the inhomogeneity influences Alfvénic wave propagation through the Sun’s atmosphere, discussing both inhomogeneities parallel and perpendicular to the magnetic field.