Using the Maximum X-ray Flux Ratio and X-ray Background to Predict Solar Flare Class

TL;DR

This study discovers a relationship between X-ray flux ratios and background levels that can classify solar flares by intensity, using machine learning on satellite data, achieving high prediction accuracy across flare classes.

Contribution

It introduces a new method combining flux ratio analysis with machine learning to accurately predict solar flare classes from GOES satellite data.

Findings

Achieved 100% accuracy for X-class flare prediction.

Successfully classified 76-81% of M, C, and B flares.

Established a clear flux ratio relationship for flare classification.

Abstract

We present the discovery of a relationship between the maximum ratio of the flare flux (namely, 0.5-4 Ang to the 1-8 Ang flux) and non-flare background (namely, the 1-8 Ang background flux), which clearly separates flares into classes by peak flux level. We established this relationship based on an analysis of the Geostationary Operational Environmental Satellites (GOES) X-ray observations of ~ 50,000 X, M, C, and B flares derived from the NOAA/SWPC flares catalog. Employing a combination of machine learning techniques (K-nearest neighbors and nearest-centroid algorithms) we show a separation of the observed parameters for the different peak flaring energies. This analysis is validated by successfully predicting the flare classes for 100% of the X-class flares, 76% of the M-class flares, 80% of the C-class flares and 81% of the B-class flares for solar cycle 24, based on the training of the parametric extracts for solar flares in cycles 22-23.