4 citations · 7 across the 3 of their papers we have counts for
3 papers
Enhancing Lyα Emitter Identification in HETDEX with a Convolutional Neural Network
Shiro Mukae, Erin Mentuch Cooper, Karl Gebhardt +9
We present a deep learning framework to enhance the identification of Ly emitters (LAEs) in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX), an untargeted spectroscop…
Participatory Science and Machine Learning Applied to Millions of Sources in the Hobby-Eberly Telescope Dark Energy Experiment
Lindsay R. House, Karl Gebhardt, Keely Finkelstein +4
We are merging a large participatory science effort with machine learning to enhance the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). Our overall goal is to remove false…
The Active Galactic Nuclei in the Hobby-Eberly Telescope Dark Energy Experiment Survey (HETDEX) III. A red quasar with extremely high equivalent widths showing powerful outflows
Chenxu Liu, Karl Gebhardt, Wolfram Kollatschny +12
We report an Active Galactic Nucleus (AGN) with extremely high equivalent width (EW), EW(LyA+NV,rest)>921 AA in the rest-frame, at z~2.24 in the Hobby-Eberly Telescope Dark Energy…