118 citations · 431 across the 11 of their papers we have counts for
11 papers
Uncertainty-Aware Deep Learning for the Ly Forest: CNN-Based Absorber Detection and Characterization
Paryag Sharma, Vikram Khaire, Ting-Yun Cheng +2
The Ly forest is a powerful probe of the intergalactic medium and small-scale matter distribution, but deriving absorber properties traditionally requires computationally expens…
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…
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…
OzDES Reverberation Mapping Program: Stacking analysis with H, Mg II and C IV
Umang Malik, Rob Sharp, A. Penton +47
Reverberation mapping is the leading technique used to measure direct black hole masses outside of the local Universe. Additionally, reverberation measurements calibrate secondary…
Lessons Learned from the Two Largest Galaxy Morphological Classification Catalogues built by Convolutional Neural Networks
Ting-Yun Cheng, H. Domínguez Sánchez, J. Vega-Ferrero +54
We compare the two largest galaxy morphology catalogues, which separate early and late type galaxies at intermediate redshift. The two catalogues were built by applying supervised…
Harvesting the Lyα forest with convolutional neural networks
Ting-Yun Cheng, Ryan Cooke, Gwen Rudie
We develop a machine learning based algorithm using a convolutional neural network (CNN) to identify low HI column density Ly absorption systems ($\log{N_{\mathrm{HI}}}/{\rm cm}…
Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks
Ting-Yun Cheng, Christopher J. Conselice, Alfonso Aragón-Salamanca +57
We present in this paper one of the largest galaxy morphological classification catalogues to date, including over 20 million of galaxies, using the Dark Energy Survey (DES) Year 3…
Beyond the Hubble Sequence -- Exploring Galaxy Morphology with Unsupervised Machine Learning
Ting-Yun Cheng, Marc Huertas-Company, Christopher J. Conselice +3
We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hiera…
Galaxy Merger Rates up to z 3 using a Bayesian Deep Learning Model A Major-Merger classifier using IllustrisTNG Simulation data
Leonardo Ferreira, Christopher J. Conselice, Kenneth Duncan +3
Merging is potentially the dominate process in galaxy formation, yet there is still debate about its history over cosmic time. To address this we classify major mergers and measure…
Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder
Ting-Yun Cheng, Nan Li, Christopher J. Conselice +3
In this paper we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder (CAE), and a clustering algorithm consisting of…
Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging
Ting-Yun Cheng, Christopher J. Conselice, Alfonso Aragón-Salamanca +13
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear compar…