Automated Solar Feature Detection for Space Weather Applications

TL;DR

This paper reviews recent image processing techniques for automated detection of solar features like active regions, coronal holes, and CMEs, aiming to improve space weather monitoring amidst increasing data volumes.

Contribution

It provides a comprehensive overview of recent advances in automated solar feature detection methods for space weather applications.

Findings

Enhanced detection accuracy for faint solar features

Improved processing speed for large data volumes

Better forecasting capabilities for space weather events

Abstract

The solar surface and atmosphere are highly dynamic plasma environments, which evolve over a wide range of temporal and spatial scales. Large-scale eruptions, such as coronal mass ejections, can be accelerated to millions of kilometres per hour in a matter of minutes, making their automated detection and characterisation challenging. Additionally, there are numerous faint solar features, such as coronal holes and coronal dimmings, which are important for space weather monitoring and forecasting, but their low intensity and sometimes transient nature makes them problematic to detect using traditional image processing techniques. These difficulties are compounded by advances in ground- and space- based instrumentation, which have increased the volume of data that solar physicists are confronted with on a minute-by-minute basis; NASA's Solar Dynamics Observatory for example is returning many thousands of images per hour (~1.5 TB/day). This chapter reviews recent advances in the application of images processing techniques to the automated detection of active regions, coronal holes, filaments, CMEs, and coronal dimmings for the purposes of space weather monitoring and prediction.