Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models

TL;DR

This paper introduces a machine learning approach using Denoising Diffusion Probabilistic Models to estimate magnetic field strength in molecular clouds from synthetic observables, outperforming traditional methods.

Contribution

The study develops and evaluates a multi-channel DDPM model that significantly improves magnetic field estimation accuracy over existing methods, especially on unseen data.

Findings

3-channel DDPM outperforms other variants and power-law fitting.

Classical DCF overestimates magnetic fields by about an order of magnitude.

Modified DCF improves over classical but is less accurate than DDPM.

Abstract

Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds (GMCs), remains a significant challenge. We present a machine learning approach using Denoising Diffusion Probabilistic Models (DDPMs) to estimate magnetic field strength from synthetic observables such as column density, dust continuum polarization vector orientation angles, and line-of-sight (LOS) nonthermal velocity dispersion. We trained three versions of the DDPM model: the 1-channel DDPM (using only column density), the 2-channel DDPM (incorporating both column density and polarization angles), and the 3-channel DDPM (which combines column density, polarization angles, and LOS nonthermal velocity dispersion). We assessed the models on both synthetic test samples and new simulation data that were outside the training set's distribution. The 3-channel DDPM consistently outperformed both the other DDPM variants and the power-law fitting approach based on column density alone, demonstrating its robustness in handling previously unseen data. Additionally, we compared the performance of the Davis-Chandrasekhar-Fermi (DCF) methods, both classical and modified, to the DDPM predictions. The classical DCF method overestimated the magnetic field strength by approximately an order of magnitude. Although the modified DCF method showed improvement over the classical version, it still fell short of the precision achieved by the 3-channel DDPM.