Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds

TL;DR

This paper presents a novel application of Denoising Diffusion Probabilistic Models to accurately infer the volume density of molecular clouds from surface density maps, outperforming traditional methods.

Contribution

It introduces a diffusion model approach for molecular cloud density prediction, demonstrating significant accuracy improvements over existing empirical and neural network methods.

Findings

Diffusion model outperforms traditional methods by an order of magnitude in accuracy.

The approach successfully predicts 3D densities from 2D surface maps.

Application to real astronomical data demonstrates practical utility.

Abstract

We introduce the state-of-the-art deep learning Denoising Diffusion Probabilistic Model (DDPM) as a method to infer the volume or number density of giant molecular clouds (GMCs) from projected mass surface density maps. We adopt magnetohydrodynamic simulations with different global magnetic field strengths and large-scale dynamics, i.e., noncolliding and colliding GMCs. We train a diffusion model on both mass surface density maps and their corresponding mass-weighted number density maps from different viewing angles for all the simulations. We compare the diffusion model performance with a more traditional empirical two-component and three-component power-law fitting method and with a more traditional neural network machine learning approach (CASI-2D). We conclude that the diffusion model achieves an order of magnitude improvement on the accuracy of predicting number density compared to that by other methods. We apply the diffusion method to some example astronomical column density maps of Taurus and the Infrared Dark Clouds (IRDCs) G28.37+0.07 and G35.39-0.33 to produce maps of their mean volume densities.