Statistical Models for Solar Flare Interval Distribution in Individual Active Regions

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.

15 pages, 2 figures, 3 tables, accepted for publication in Solar Physics

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• Bayesian Blocks, A New Method to Analyze Structure in Photon Counting Data
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• The Origin of the Solar Flare Waiting-Time Distribution
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Cited by in corpus (6)

• Verification of operational solar flare forecast: Case of Regional Warning Center Japan
• Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods
• Monte-Carlo Simulation of Solar Active-Region Energy
• A Correlation in the Waiting-time Distributions of Solar Flares
• Non-Stationary Fast-Driven Self-Organized Criticality in Solar Flares
• Statistical relationship between the succeeding solar flares detected by the RHESSI satellite