121 citations · 501 across the 18 of their papers we have counts for
4 papers · 1 filter
Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models
Duo Xu, Stella Offner, Robert Gutermuth +3
Accurately quantifying the impact of radiation feedback in star formation is challenging. To address this complex problem, we employ deep learning techniques, denoising diffusion p…
CMR exploration II -- filament identification with machine learning
Duo Xu, Shuo Kong, Avichal Kaul +2
We adopt magnetohydrodynamics (MHD) simulations that model the formation of filamentary molecular clouds via the collision-induced magnetic reconnection (CMR) mechanism under varyi…
Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds
Duo Xu, Jonathan C. Tan, Chia-Jung Hsu +1
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) fr…
CMR exploration I -- filament structure with synthetic observations
Shuo Kong, Volker Ossenkopf-Okada, Héctor G. Arce +2
In this paper, we carry out a pilot parameter exploration for the collision-induced magnetic reconnection (CMR) mechanism that forms filamentary molecular clouds. Following Kong et…