Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds

1 code implementation • 4 Apr 2023 • Duo Xu, Jonathan C. Tan, Chia-Jung Hsu, Ye Zhu

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.

Denoising

Strong Gravitational Lensing Parameter Estimation with Vision Transformer

1 code implementation • 9 Oct 2022 • Kuan-Wei Huang, Geoff Chih-Fan Chen, Po-Wen Chang, Sheng-Chieh Lin, Chia-Jung Hsu, Vishal Thengane, Joshua Yao-Yu Lin

Quantifying the parameters and corresponding uncertainties of hundreds of strongly lensed quasar systems holds the key to resolving one of the most important scientific questions: the Hubble constant ($H_{0}$) tension.