6 papers
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile +21
Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely availab…
Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems
Mengke Zhao, Guang-Xing Li, Duo Xu +1
Complex physical systems, from supersonic turbulence to the macroscopic structure of the universe, are governed by continuous multiscale dynamics. While modern machine learning arc…
Disk Wind Feedback from High-mass Protostars. V. Application of Multi-Modal Machine Learning to Characterize Outflow Properties
Duo Xu, Ioana A. Stelea, Joshua S. Speagle +2
Characterizing protostellar outflows is fundamental to understanding star formation feedback, yet traditional methods are often hindered by projection effects and complex morpholog…
A Sampling-Based Domain Generalization Study with Diffusion Generative Models
Ye Zhu, Yu Wu, Duo Xu +3
In this work, we investigate the domain generalization capabilities of diffusion models in the context of synthesizing images that are distinct from the training data. Instead of f…
Dynamic Diffusion Schrödinger Bridge in Astrophysical Observational Inversions
Ye Zhu, Duo Xu, Zhiwei Deng +2
We study Diffusion Schrödinger Bridge (DSB) models in the context of dynamical astrophysical systems, specifically tackling observational inverse prediction tasks within Giant Mol…
Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models
Duo Xu, Jenna Karcheski, Chi-Yan Law +3
Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds (GMCs), remains a significant challenge. We present a machine learning app…