Multiscale statistical analysis of coronal solar activity

TL;DR

This paper employs multiscale statistical analysis using proper orthogonal decomposition on solar corona images to differentiate active regions from quiet ones and to understand heat transport mechanisms.

Contribution

It introduces a novel multiscale statistical approach to analyze coronal solar activity and distinguish between different activity levels based on temperature map dynamics.

Findings

Active regions show distinct multiscale statistical properties.

The analysis reveals insights into heat transport in the corona.

Method can differentiate between pre- and post-flare states.

Abstract

Multi-filter images from the solar corona are used to obtain temperature maps which are analyzed using techniques based on proper orthogonal decomposition (POD) in order to extract dynamical and structural information at various scales. Exploring active regions before and after a solar flare and comparing them with quiet regions we show that the multiscale behavior presents distinct statistical properties for each case that can be used to characterize the level of activity in a region. Information about the nature of heat transport is also be extracted from the analysis.