Self-Organized Criticality in Stellar Flares

TL;DR

This study analyzes stellar flare size distributions from EUVE and Kepler data, finding power law slopes consistent with self-organized criticality models, and highlights the importance of bias correction for accurate characterization.

Contribution

It provides the first comprehensive analysis of stellar flare size distributions across multiple datasets, confirming their consistency with SOC models after bias correction.

Findings

Power law slopes of stellar flare energies are around 2.09 with uncertainties.

Slopes do not significantly vary over time or with stellar spectral type.

Bias correction methods align observations with SOC theoretical predictions.

Abstract

Power law size distributions are the hallmarks of nonlinear energy dissipation processes governed by self-organized criticality. Here we analyze 75 data sets of stellar flare size distributions, mostly obtained from the {\sl Extreme Ultra-Violet Explorer (EUVE)} and the {\sl Kepler} mission. We aim to answer the following questions for size distributions of stellar flares: (i) What are the values and uncertainties of power law slopes? (ii) Do power law slopes vary with time ? (iii) Do power law slopes depend on the stellar spectral type? (iv) Are they compatible with solar flares? (v) Are they consistent with self-organized criticality (SOC) models? We find that the observed size distributions of stellar flare fluences (or energies) exhibit power law slopes of $\alpha_E=2.09\pm0.24$ for optical data sets observed with Kepler. The observed power law slopes do not show much time variability and do not depend on the stellar spectral type (M, K, G, F, A, Giants). In solar flares we find that background subtraction lowers the uncorrected value of $\alpha_E=2.20\pm0.22$ to $\alpha_E=1.57\pm0.19$. Furthermore, most of the stellar flares are temporally not resolved in low-cadence (30 min) Kepler data, which causes an additional bias. Taking these two biases into account, the stellar flare data sets are consistent with the theoretical prediction $N(x) \propto x^{-\alpha_x}$ of self-organized criticality models, i.e., $\alpha_E=1.5$. Thus, accurate power law fits require automated detection of the inertial range and background subtraction, which can be modeled with the generalized Pareto distribution, finite-system size effects, and extreme event outliers.