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Testing Self-Organized Criticality Across the Main Sequence using Stellar Flares from TESS

arXiv 2109.07011 ↗First posted 14 Sep 2021astro-ph.SR astro-ph.EP cs.LGcited by 0

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Abstract (arXiv)

Self-organized criticality describes a class of dynamical systems that maintain themselves in an attractor state with no intrinsic length or time scale. Fundamentally, this theoretical construct requires a mechanism for instability that may trigger additional instabilities locally via dissipative processes. This concept has been invoked to explain nonlinear dynamical phenomena such as featureless energy spectra that have been observed empirically for earthquakes, avalanches, and solar flares. If this interpretation proves correct, it implies that the solar coronal magnetic field maintains itself in a critical state via a delicate balance between the dynamo-driven injection of magnetic energy and the release of that energy via flaring events. All-sky high-cadence surveys like the Transiting Exoplanet Survey Satellite (TESS) provide the necessary data to compare the energy distribution of flaring events in stars of different spectral types to that observed in the Sun. We identified ~ 106 flaring events on ~ 105 stars observed by TESS at 2-minute cadence. By fitting the flare frequency distribution for different mass bins, we find that all main sequence stars exhibit distributions of flaring events similar to that observed in the Sun, independent of their mass or age. This may suggest that stars universally maintain a critical state in their coronal topologies via magnetic reconnection events. If this interpretation proves correct, we may be able to infer properties of magnetic fields, interior structure, and dynamo mechanisms for stars that are otherwise unresolved point sources.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 1 of its 70 references that are in the map. First posted on arXiv 14 Sep 2021.

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References · 1

  1. 2020DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learningcited by 4

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