69 citations · 118 across the 12 of their papers we have counts for
6 papers · 1 filter
Foundation Models for Astrophysics
Xiaosheng Zhao, Yuan-Sen Ting
Foundation models are high-capacity networks pretrained once on broad data and then reused across many tasks. This chapter introduces them through the idea of a transferable repres…
Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization
Anshuman Acharya, Michele Bianco, Daniela Breitman +18
When operational, the SKA will generate unprecedented amounts of data and provide exquisite sensitivity for 21 cm tomography of Cosmic Dawn (CD) and the Epoch of Reionization (EoR)…
Likelihood-free Model Selection in Cosmic Reionization with Three-dimensional Tomographic 21 cm Lightcone Images
T. Binnie, Xiaosheng Zhao, J. R. Pritchard +1
We explore likelihood-free (aka simulation-based) Bayesian model selection to quantify model comparison analyses of reionisation scenarios. We iteratively train the 3D Convolutiona…
Simulation-based Inference of Reionization Parameters from 3D Tomographic 21 cm Light-cone Images -- II: Application of Solid Harmonic Wavelet Scattering Transform
Xiaosheng Zhao, Yi Mao, Shifan Zuo +1
The information regarding how the intergalactic medium is reionized by astrophysical sources is contained in the tomographic three-dimensional 21 cm images from the epoch of reioni…
3D ScatterNet: Inference from 21 cm Light-cones
Xiaosheng Zhao, Shifan Zuo, Yi Mao
The Square Kilometre Array (SKA) will have the sensitivity to take the 3D light-cones of the 21 cm signal from the epoch of reionization. This signal, however, is highly non-Gaussi…
Can Diffusion Model Conditionally Generate Astrophysical Images?
Xiaosheng Zhao, Yuan-Sen Ting, Kangning Diao +1
Generative adversarial networks (GANs) are frequently utilized in astronomy to construct an emulator of numerical simulations. Nevertheless, training GANs can prove to be a precari…