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The dynamical origins of the intense magnetic activity exhibited by most M-dwarf stars must be tied to their internal convective dynamos, and yet there remain many unanswered questions concerning its details. Despite the central role magnetic flux ropes are thought to play in the formation of solar and stellar starspots, global MHD simulations of stellar interiors have historically struggled to self-consistently capture their dynamics. By their nature, these structures tend to be short-lived or wholly absent in all but the most turbulent high-resolution simulations -- environments which make their hand-identification and study prohibitively time-consuming. We present here a novel machine-learning pipeline for the autonomous identification and analysis of self-consistent, rising flux ropes found in our global 3D MHD simulations of M-dwarf convective interiors. We report on the details of the models developed, as well as early results from the project and its prospects moving forward.
Machine Learning, MHD, Flux Emergence, Cool Stars on the main sequence, M-Dwarf, Convection, Dynamo
Machine Learning, MHD, Flux Emergence, Cool Stars on the main sequence, M-Dwarf, Convection, Dynamo
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