Denoising Diffusion Probabilistic Models to Predict the Number Density of Molecular Clouds in Astronomy

Denoising Diffusion Probabilistic Models (DDPMs) have become the mainstream generative approach in the Machine Learning and Computer Vision area, achieving state-of-the-art performance in synthesizing high-quality images, videos, and audio. In this work, we bring the DDPMs out of the data generation tasks, but to a new scientific application field in astronomy for inferring the volume or number density of giant molecular clouds (GMCs) from projected mass surface density maps. Specifically, we adopt magnetohydrodynamic (MHD) simulations with different global magnetic field strengths and large-scale dynamics, i.e., noncolliding and colliding GMCs. We train a DDPM on both mass surface density maps and their corresponding mass-weighted number density maps from different viewing angles for all the simulations. We compare our 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 (CASItD). Experiments show that DDPMs achieve an order of magnitude improvement in the accuracy of predicting number density compared to that by other methods, demonstrating the promising potential of applying DDPMs in astrophysics.