CMR exploration II -- filament identification with machine learning

Abstract

We adopt magnetohydrodynamics (MHD) 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 showed a high level of accuracy in identifying CMR filaments in the test dataset, 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.