86 citations · 541 across the 15 of their papers we have counts for
16 papers · 1 filter
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…
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…
Rates and delay times of type Ia supernovae in the Dark Energy Survey
P. Wiseman, M. Sullivan, M. Smith +87
We use a sample of 809 photometrically classified type Ia supernovae (SNe Ia) discovered by the Dark Energy Survey (DES) along with 40415 field galaxies to calculate the rate of SN…
OzDES Reverberation Mapping Program: The first Mg II lags from five years of monitoring
Zhefu Yu, Paul Martini, A. Penton +63
Reverberation mapping is a robust method to measure the masses of supermassive black holes (SMBHs) outside of the local Universe. Measurements of the radius -- luminosity () r…
OzDES Reverberation Mapping Program: Lag recovery reliability for 6-year CIV analysis
Andrew Penton, Umang Malik, Tamara Davis +66
We present the statistical methods that have been developed to analyse the OzDES reverberation mapping sample. To perform this statistical analysis we have created a suite of custo…
A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
S. Mucesh, W. G. Hartley, A. Palmese +72
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorit…