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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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Published in: | The Astrophysical journal 2023-10, Vol.955 (2), p.113 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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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. |
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ISSN: | 0004-637X 1538-4357 |
DOI: | 10.3847/1538-4357/acefce |