Self-organized Criticality in Stellar Flares

被引:18
作者
Aschwanden, Markus J. [1 ]
Guedel, Manuel [2 ]
机构
[1] Solar & Stellar Astrophys Lab LMSAL, Palo Alto, CA 94304 USA
[2] Univ Vienna, Astrophys Dept, Turkenschanzstr 17, A-1180 Vienna, Austria
关键词
Stellar activity; Stellar flares; Solar flares; Astrostatistics; X-RAY VARIABILITY; EXTREME-ULTRAVIOLET; FREQUENCY-DISTRIBUTIONS; STATISTICAL-ANALYSIS; ENERGY-DISTRIBUTION; TIME VARIABILITY; STARS; ENERGETICS; EMISSION; SUN;
D O I
10.3847/1538-4357/abdec7
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
Power-law size distributions are the hallmarks of nonlinear energy dissipation processes governed by self-organized criticality (SOC). Here we analyze 75 data sets of stellar flare size distributions, mostly obtained from the Extreme-Ultraviolet Explorer and the 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 SOC models? We find that the observed size distributions of stellar flare fluences (or energies) exhibit power-law slopes of alpha(E) = 2.09 0.24 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 0.22 to alpha(E) = 1.57 0.19. Furthermore, most of the stellar flares are temporally not resolved in low-cadence (30 minutes) 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) proportional to x-alpha(2) of SOC 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.
引用
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页数:16
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