CMR exploration II -- filament identification with machine learning

TL;DR

This paper combines MHD simulations, radiative transfer, and machine learning to identify filamentary molecular clouds formed via CMR, achieving high accuracy and discovering new candidates in Herschel observations.

Contribution

It introduces a novel machine learning approach for identifying CMR filaments in dust emission, validated with simulations and applied to real data.

Findings

High detection accuracy of over 80% in simulations

Successful identification of CMR filaments in Herschel data

Detection of new high-confidence filament candidates in Orion A

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.