Statistical Models for Solar Flare Interval Distribution in Individual Active Regions

TL;DR

This paper analyzes solar flare intervals in active regions using statistical models, proposing a new method for model comparison, and finds that lognormal and inverse Gaussian models better describe flare timing than exponential models.

Contribution

Introduces a new procedure using maximum likelihood and AIC for comparing probability models of solar flare intervals, revealing non-random flare occurrence patterns.

Findings

Lognormal and inverse Gaussian models outperform exponential models.

Solar flare intervals are regulated by underlying mechanisms.

Proposes a probabilistic forecasting approach based on interval distribution analysis.

Abstract

This article discusses statistical models for solar flare interval distribution in individual active regions. We analyzed solar flare data in 55 active regions that are listed in the GOES soft X-ray flare catalog. We discuss some problems with a conventional procedure to derive probability density functions from any data set and propose a new procedure, which uses the maximum likelihood method and Akaike Information Criterion (AIC) to objectively compare some competing probability density functions. We found that lognormal and inverse Gaussian models are more likely models than the exponential model for solar flare interval distribution in individual active regions. The results suggest that solar flares do not occur randomly in time; rather, solar flare intervals appear to be regulated by solar flare mechanisms. We briefly mention a probabilistic solar flare forecasting method as an application of a solar flare interval distribution analysis.