Energetic characterisation and statistics of solar coronal brightenings

TL;DR

This study analyzes the energy distribution of solar coronal brightenings using high-resolution EUV data, revealing power-law distributions and suggesting these events contribute minimally to overall coronal heating.

Contribution

It provides a detailed statistical analysis of coronal brightening energies across multiple channels, improving estimates of their role in coronal heating compared to previous crude methods.

Findings

Energy distributions follow power-laws.

Coronal brightenings contribute little to total heating.

Power-law correlations exist between event parameters.

Abstract

To explain the high temperature of the corona, much attention has been paid to the distribution of energy in dissipation events. Indeed, if the event energy distribution is steep enough, the smallest, unobservable events could be the largest contributors to the total energy dissipation in the corona. Previous observations have shown a wide distribution of energies but remain inconclusive about the precise slope. Furthermore, these results rely on a very crude estimate of the energy. On the other hand, more detailed spectroscopic studies of structures such as coronal bright points do not provide enough statistical information to derive their total contribution to heating. We aim at getting a better estimate of the distributions of the energy dissipated in coronal heating events using high-resolution, multi-channel Extreme Ultra-Violet (EUV) data. To estimate the energies corresponding to heating events and deduce their distribution, we detect brightenings in five EUV channels of the Atmospheric Imaging Assembly (AIA) on-board the Solar Dynamics Observatory (SDO). We combine the results of these detections and we use maps of temperature and emission measure derived from the same observations to compute the energies. We obtain distributions of areas, durations, intensities, and energies (thermal, radiative, and conductive) of events. These distributions are power-laws, and we find also power-law correlations between event parameters. The energy distributions indicate that the energy from a population of events like the ones we detect represents a small contribution to the total coronal heating, even when extrapolating to smaller scales. The main explanations for this are how heating events can be extracted from observational data, and the incomplete knowledge of the thermal structure and processes in the coronal plasma attainable from available observations.