Poster Open Access

# Machine Learning analysis of self-consistent magnetic flux ropes realized in M-dwarf dynamo simulations

Connor P Bice; Juri Toomre

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<dc:creator>Connor P Bice</dc:creator>
<dc:creator>Juri Toomre</dc:creator>
<dc:date>2021-03-02</dc:date>
<dc:description>The dynamical origins of the intense magnetic activity exhibited by most M-dwarf stars remains an unanswered question in stellar astrophysics. Despite the central role magnetic flux ropes are thought to play in the formation of sun and star spots, 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 approach for the identification and dynamical analysis of the hundreds of self-consistent, rising flux ropes in our simulations of M-dwarf interiors. We report on the details of the models developed, as well as the prospects for their continued use with not only our own simulations, but those of the entire stellar convection community.</dc:description>
<dc:description>Supported by NASA grants NNX17AG22G and 80NSSC20K1543</dc:description>
<dc:identifier>https://zenodo.org/record/4574633</dc:identifier>
<dc:identifier>10.5281/zenodo.4574633</dc:identifier>
<dc:identifier>oai:zenodo.org:4574633</dc:identifier>
<dc:relation>doi:10.5281/zenodo.4574632</dc:relation>
<dc:relation>url:https://zenodo.org/communities/coolstars20half</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:title>Machine Learning analysis of self-consistent magnetic flux ropes realized in M-dwarf dynamo simulations</dc:title>
<dc:type>info:eu-repo/semantics/conferencePoster</dc:type>
<dc:type>poster</dc:type>
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