An Automated Algorithm to Distinguish and Characterize Solar Flares and Associated Sequential Chromospheric Brightenings

TL;DR

This paper introduces an automated algorithm for identifying, tracking, and characterizing small-scale brightening events in the solar chromosphere, specifically flare ribbons and sequential chromospheric brightenings, using H-alpha observations.

Contribution

The novel algorithm can distinguish and track solar brightening features, characterizing their evolution and morphology, and is adaptable to various datasets for solar feature analysis.

Findings

Successfully tracks and characterizes flare kernels and SCBs in H-alpha data.

Allows overlay with Doppler and magnetic data for comprehensive analysis.

Provides a flexible tool applicable to different solar observational datasets.

Abstract

We present a new automated algorithm to identify, track, and characterize small-scale brightening associated with solar eruptive phenomena observed in H{\alpha}. The temporal spatially-localized changes in chromospheric intensities can be separated into two categories: flare ribbons and sequential chromospheric brightenings (SCBs). Within each category of brightening we determine the smallest resolvable locus of pixels, a kernel, and track the temporal evolution of the position and intensity of each kernel. This tracking is accomplished by isolating the eruptive features, identifying kernels, and linking detections between frames into trajectories of kernels. We fully characterize the evolving intensity and morphology of the flare ribbons by observing the tracked flare kernels in aggregate. With the location of SCB and flare kernels identified, they can easily be overlaid on top of complementary data sets to extract Doppler velocities and magnetic field intensities underlying the kernels. This algorithm is adaptable to any dataset to identify and track solar features.