Ting-Yun Cheng

collaborators

3 papers

astro-ph.GA2026

Uncertainty-Aware Deep Learning for the Ly $I^\pm$ Forest: CNN-Based Absorber Detection and Characterization

Paryag Sharma, Vikram Khaire, Ting-Yun Cheng +2

The Ly $I^\pm$ forest is a powerful probe of the intergalactic medium and small-scale matter distribution, but deriving absorber properties traditionally requires computationally expen…

astro-ph.GA2025

Deciphering galaxy images using machine vision -- Combining variational autoencoder and principal component analysis for feature extraction

Samuel Howie, Ting-Yun Cheng, Carlton M. Baugh

Here, we present a machine vision approach, combining a VAE framework with PCA, to decipher galaxy images. Using mock gri-band images from the EAGLE simulation, the VAE finds that…

astro-ph.GA2025

Efficient Search for Extremely Metal Poor Galaxies in the Local Universe using Convolutional Neural Networks

Ting-Yun Cheng, Ryan J. Cooke

Nearby extremely metal-poor galaxies (XMPs) allow us to study primitive galaxy formation and evolution in greater detail than is possible at high redshift. This work, for the first…

Uncertainty-Aware Deep Learning for the LyI^± Forest: CNN-Based Absorber Detection and Characterization

Deciphering galaxy images using machine vision -- Combining variational autoencoder and principal component analysis for feature extraction

Efficient Search for Extremely Metal Poor Galaxies in the Local Universe using Convolutional Neural Networks

Uncertainty-Aware Deep Learning for the Ly $I^\pm$ Forest: CNN-Based Absorber Detection and Characterization