CMR Exploration. II. Filament Identification with Machine Learning

Abstract

Abstract 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.