129 citations · 247 across the 4 of their papers we have counts for
9 papers · 1 filter
Pushing automated morphological classifications to their limits with the Dark Energy Survey
J. Vega-Ferrero, H. Domínguez Sánchez, M. Bernardi +60
We present morphological classifications of 27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classif…
Dark Energy Survey Identification of A Low-Mass Active Galactic Nucleus at Redshift 0.823 from Optical Variability
Hengxiao Guo, Colin J. Burke, Xin Liu +56
We report the identification of a low-mass AGN, DES J02180430, in a redshift galaxy in the Dark Energy Survey (DES) Supernova field. We select DES J02180430 as an…
Supernova Siblings: Assessing the Consistency of Properties of Type Ia Supernovae that Share the Same Parent Galaxies
D. Scolnic, M. Smith, A. Massiah +82
While many studies have shown a correlation between properties of the light curves of Type Ia SN (SNe Ia) and properties of their host galaxies, it remains unclear what is driving…
An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks
C. Jacobs, T. Collett, K. Glazebrook +55
We search Dark Energy Survey (DES) Year 3 imaging for galaxy-galaxy strong gravitational lenses using convolutional neural networks, extending previous work with new training sets…
CIV Black Hole Mass Measurements with the Australian Dark Energy Survey (OzDES)
J. K. Hoormann, P. Martini, T. M. Davis +67
Black hole mass measurements outside the local universe are critically important to derive the growth of supermassive black holes over cosmic time, and to study the interplay betwe…
Finding high-redshift strong lenses in DES using convolutional neural networks
C. Jacobs, T. Collett, K. Glazebrook +63
We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250,000 simulated lenses at re…