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CMR Exploration. II. Filament Identification with Machine Learning

We adopt magnetohydrodynamic simulations that model the formation of filamentary molecular clouds via the collision-induced magnetic reconnection (CMR) mechanism under varying physical conditions. We conduct radiative transfer using radmc-3d to generate synthetic dust emission of CMR filaments. We u...

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Bibliographic Details
Published in:The Astrophysical journal 2023-10, Vol.955 (2), p.113
Main Authors: Xu, Duo, Kong, Shuo, Kaul, Avichal, Arce, Héctor G., Ossenkopf-Okada, Volker
Format: Article
Language:English
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Summary:We adopt magnetohydrodynamic simulations that model the formation of filamentary molecular clouds via the collision-induced magnetic reconnection (CMR) mechanism under varying physical conditions. We conduct radiative transfer using radmc-3d to generate synthetic dust emission of CMR filaments. We use the previously developed machine-learning technique casi-2d along with the diffusion model to identify the location of CMR filaments in dust emission. Both models show a high level of accuracy in identifying CMR filaments in the test data set, with detection rates of over 80% and 70%, respectively, at a false detection rate of 5%. We then apply the models to real Herschel dust observations of different molecular clouds, successfully identifying several high-confidence CMR filament candidates. Notably, the models are able to detect high-confidence CMR filament candidates in Orion A from dust emission, which have previously been identified using molecular line emission.
ISSN:0004-637X
1538-4357
DOI:10.3847/1538-4357/acefce