Abstract

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 171 \AA, 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, $\alpha$, greater than the critical value 2. At these sites nanoflare heating could be significant.

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