# Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds

arXiv: https://arxiv.org/abs/2304.01670 · HTML: https://mlsanity.com/paper/2304.01670/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 4 of 4)

- [Realistic galaxy image simulation via score-based generative models](https://mlsanity.com/paper/2111.01713/) (2021)
- [Convolutional Deep Denoising Autoencoders for Radio Astronomical Images](https://mlsanity.com/paper/2110.08618/) (2021)
- [Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network](https://mlsanity.com/paper/2005.03945/) (2020)
- [PGNets: Planet mass prediction using convolutional neural networks for radio continuum observations of protoplanetary disks](https://mlsanity.com/paper/2111.15196/) (2021)

## Built on by (most cited first, 4 of 4)

- [Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2309.05811/) (2023)
- [Quantum Diffusion Model for Quark and Gluon Jet Generation](https://mlsanity.com/paper/2412.21082/) (2024)
- [A Sampling-Based Domain Generalization Study with Diffusion Generative Models](https://mlsanity.com/paper/2310.09213/) (2023)
- [Dynamic Diffusion Schr"odinger Bridge in Astrophysical Observational Inversions](https://mlsanity.com/paper/2506.08065/) (2025)

## Abstract (arXiv)

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.